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Electronic Theses and Dissertations, 2004-2019
2012
Microscopic Assessment Of Transportation Emissions On Limited Microscopic Assessment Of Transportation Emissions On Limited
Access Highways Access Highways
Hatem Abou-Senna University of Central Florida
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STARS Citation STARS Citation Abou-Senna, Hatem, "Microscopic Assessment Of Transportation Emissions On Limited Access Highways" (2012). Electronic Theses and Dissertations, 2004-2019. 2461. https://stars.library.ucf.edu/etd/2461
MICROSCOPIC ASSESSMENT OF TRANSPORTATION EMISSIONS
ON LIMITED ACCESS HIGHWAYS
by
HATEM AHMED ABOU-SENNA
B.S. Cairo University, 1993
M.E. Cairo University, 2000
M.S. University of Central Florida, 2003
A dissertation submitted in partial fulfillment of the requirements
for the degree of Doctor of Philosophy
in the Department of Civil, Environmental and Construction Engineering
in the College of Engineering and Computer Science
at the University of Central Florida
Orlando, Florida
Fall Term
2012
Major Professor: Ahmed E. Radwan
ii
© 2012 Hatem A. Abou-Senna
iii
ABSTRACT
On-road vehicles are a major source of transportation carbon dioxide (CO2)
greenhouse gas emissions in all the developed countries, and in many of the developing
countries in the world. Similarly, several criteria air pollutants are associated with
transportation, e.g., carbon monoxide (CO), nitrogen oxides (NOx), and particulate matter
(PM). The need to accurately quantify transportation-related emissions from vehicles is
essential.
Transportation agencies and researchers in the past have estimated emissions using
one average speed and volume on a long stretch of roadway. With MOVES, there is an
opportunity for higher precision and accuracy. Integrating a microscopic traffic simulation
model (such as VISSIM) with MOVES allows one to obtain precise and accurate emissions
estimates. The new United States Environmental Protection Agency (USEPA) mobile
source emissions model, MOVES2010a (MOVES) can estimate vehicle emissions on a
second-by-second basis creating the opportunity to develop new software ―VIMIS 1.0‖
(VISSIM/MOVES Integration Software) to facilitate the integration process. This research
presents a microscopic examination of five key transportation parameters (traffic volume,
speed, truck percentage, road grade and temperature) on a 10-mile stretch of Interstate 4 (I-
4) test bed prototype; an urban limited access highway corridor in Orlando, Florida.
iv
The analysis was conducted utilizing VIMIS 1.0 and using an advanced custom
design technique; D-Optimality and I-Optimality criteria, to identify active factors and to
ensure precision in estimating the regression coefficients as well as the response variable.
The analysis of the experiment identified the optimal settings of the key factors and
resulted in the development of Micro-TEM (Microscopic Transportation Emissions Meta-
Model). The main purpose of Micro-TEM is to serve as a substitute model for predicting
transportation emissions on limited access highways in lieu of running simulations using a
traffic model and integrating the results in an emissions model to an acceptable degree of
accuracy. Furthermore, significant emission rate reductions were observed from the
experiment on the modeled corridor especially for speeds between 55 and 60 mph while
maintaining up to 80% and 90% of the freeway‘s capacity. However, vehicle activity
characterization in terms of speed was shown to have a significant impact on the emission
estimation approach.
Four different approaches were further examined to capture the environmental
impacts of vehicular operations on the modeled test bed prototype. First, (at the most basic
level), emissions were estimated for the entire 10-mile section ―by hand‖ using one average
traffic volume and average speed. Then, three advanced levels of detail were studied using
VISSIM/MOVES to analyze smaller links: average speeds and volumes (AVG), second-by-
second link driving schedules (LDS), and second-by-second operating mode distributions
(OPMODE). This research analyzed how the various approaches affect predicted emissions
of CO, NOx, PM and CO2.
v
The results demonstrated that obtaining accurate and comprehensive operating mode
distributions on a second-by-second basis improves emission estimates. Specifically,
emission rates were found to be highly sensitive to stop-and-go traffic and the associated
driving cycles of acceleration, deceleration, frequent braking/coasting and idling. Using the
AVG or LDS approach may overestimate or underestimate emissions, respectively,
compared to an operating mode distribution approach.
Additionally, model applications and mitigation scenarios were examined on the
modeled corridor to evaluate the environmental impacts in terms of vehicular emissions and
at the same time validate the developed model ―Micro-TEM‖. Mitigation scenarios included
the future implementation of managed lanes (ML) along with the general use lanes (GUL)
on the I-4 corridor, the currently implemented variable speed limits (VSL) scenario as well
as a hypothetical restricted truck lane (RTL) scenario. Results of the mitigation scenarios
showed an overall speed improvement on the corridor which resulted in overall reduction in
emissions and emission rates when compared to the existing condition (EX) scenario and
specifically on link by link basis for the RTL scenario.
The proposed emission rate estimation process also can be extended to gridded
emissions for ozone modeling, or to localized air quality dispersion modeling, where
temporal and spatial resolution of emissions is essential to predict the concentration of
pollutants near roadways.
vi
IN THE NAME OF GOD, THE MOST COMPASSIONATE, THE MOST MERCIFUL
To my dearest parents Abla and Ahmed
Whose constant love, kindness and support made me the person I am
To my precious grandmother Nonna
Who had supported me with her endless love and care
To my gorgeous wife Ghada
Whose eternal love and patience gave me hope and happiness
To my marvelous children Nada and Adham
Whose smiles and hugs were nurturing
To my beloved sister Lamiaa
You are all that a brother could ask for
To all my family and friends
Who gave me joy and precious memories through tough moments
vii
ACKNOWLEDGMENTS
First, I praise God for giving me the strength and patience to accomplish this
monumental task.
I would like to express my deepest gratitude for the constant support and guidance
from my committee chair, advisor, and friend Dr. Essam Radwan throughout my PhD study
and throughout the years at UCF especially during the toughest times of my life. His
continuous support, insights, and suggestions contributed in large measure to the success of
this research. I would like also to thank my committee members Dr. Haitham Al-Deek, Dr.
Mohammed Abdel-Aty, Dr. David Cooper and Dr. Mark Johnson for their help and support
in conducting this research and for serving in my committee. I sincerely thank Dr. Cooper
for his valuable insights with the environmental portion as well as Dr. Johnson for his help
with the experimental design portion which ignited the whole idea about this research. Dr.
Aty‘s help with my candidacy procedure was very supportive and Dr. Al-Deek‘s
understanding during my GTA was thoughtful. I can‘t thank enough Dr. Hesham Eldeeb
who committed his time and energy to develop the key software for this research. Words
can‘t express my gratitude towards his contribution and assistance throughout my study. I
would like also to thank the staff members of the Civil Engineering Department for
providing me an opportunity to pursue the graduate program and for their help.
I would like to express my heartfelt appreciation to my parents, sister and all my
family for their endless love, precious advice and support throughout the years. My wife‘s
eternal love helped me to overcome tough moments. My grateful thanks are due to Orange
County Transportation Planning staff for their help, support and understanding during my
internship at the County. I‘m also grateful to Dr. Adel El-Safty for his support during tough
times and backing. Also, my stay at UCF has given me many moments and experiences that
I will cherish for all times to come. My friends and colleagues from the ITS Lab have
confirmed my belief that there is always something to learn from everybody.
viii
TABLE OF CONTENTS
LIST OF FIGURES ..................................................................................................... x
LIST OF TABLES .................................................................................................... xii
1. INTRODUCTION ............................................................................................... 1
1.1 Background ................................................................................................. 1
1.2 Research Objective ...................................................................................... 4
1.3 Research Value to Practitioners .................................................................. 5
1.4 Research Response to Current or Future Needs Statewide ......................... 6
2. LITERATURE REVIEW .................................................................................... 9
2.1 Greenhouse Gas Components and Climate Change .................................... 9
2.2 Criteria Pollutants and Health Effects ....................................................... 11
2.3 Transportation Greenhouse Gas Emissions & CO2 Share ......................... 16
2.4 Calculating CO2 Emissions ....................................................................... 24
2.5 Feasibility of Field Capturing CO2 ............................................................ 25
2.6 US CAFE Program .................................................................................... 26
2.6.1 Overview of Joint EPA/NHTSA National Program.............................. 26
2.6.2 Summary of the Joint Final Rule ........................................................... 28
2.7 Previous Studies to Model and Estimate Traffic Emissions ..................... 28
2.8 EPA Emissions Models and Analysis Tools Softwares ............................ 37
2.9 Examples of Non-EPA Emissions Models ................................................ 39
3. RESEARCH APPROACH ................................................................................ 45
3.1 Design of Experiments (DOE) .................................................................. 45
3.2 Development of Calibrated Base Scenario Using VISSIM Model ........... 48
3.2.1 The VISSIM Model ............................................................................... 48
3.2.2 Calibration & Validation of VISSIM Model ......................................... 48
3.3 Estimation of Scenario-Based Emissions using MOVES Model .............. 50
3.3.1 The MOVES Model .............................................................................. 51
3.3.2 Validation of MOVES Model ............................................................... 52
3.4 Statistical Analysis Using JMP Software .................................................. 58
3.5 Development of Emission Prediction Model (Micro-TEM) ..................... 58
3.6 Application of Mitigation Strategies ......................................................... 59
3.7 Findings of Research Results and Conclusions ......................................... 61
4. VISSIM/MOVES INTEGRATION SOFTWARE (VIMIS) ............................. 63
4.1 Overview ................................................................................................... 63
4.2 Modules Description ................................................................................. 63
ix
5. EVALUATION OF THE I-4 CORRIDOR ....................................................... 70
5.1 Overview of the I-4 Downtown Corridor .................................................. 70
5.2 Model Calibration...................................................................................... 72
5.3 Statistical Analysis .................................................................................... 77
5.4 Model Validation ....................................................................................... 81
6. DEVELOPING MICROSCOPIC EMISSION PREDICTION MODEL .......... 86
6.1 Overview ................................................................................................... 86
6.2 Design of Experiments (DOE) .................................................................. 86
6.3 Test Bed Modeling .................................................................................... 90
6.4 Moves Project Level Data ......................................................................... 91
6.5 Operating Modes & Link Driving Schedules ............................................ 93
6.6 Design Settings versus Actual Settings ..................................................... 95
6.7 Analysis of Results .................................................................................... 97
6.8 Discussion ............................................................................................... 106
6.9 Meta Model for Transportation Emissions ―Micro-TEM‖...................... 114
6.9.1 Introduction to Meta Models ............................................................... 114
6.9.2 Micro-TEM ......................................................................................... 115
7. EMISSION ESTIMATION APPROACHES .................................................. 124
7.1 Overview ................................................................................................. 124
7.2 VISSIM Input/Output Data ..................................................................... 124
7.3 Moves Project Level Data ....................................................................... 127
7.4 Vehicle Activity Characterization ........................................................... 128
7.4.1 Average Speeds, Link Drive Schedules & Operating Modes ............. 128
7.4.2 Vehicle Specific Power (VSP) ............................................................ 129
7.5 Emissions Results and Analysis .............................................................. 131
7.6 Discussion ............................................................................................... 141
8. MODEL APPLICATIONS ............................................................................. 143
8.1 Overview ................................................................................................. 143
8.2 Managed Lanes (ML) .............................................................................. 143
8.3 Restricted Truck Lanes (RTL) ................................................................ 146
8.4 Evaluation of Scenarios ........................................................................... 148
9. CONCLUSIONS AND RECOMMENDATIONS .......................................... 159
APPENDIX A CUSTOM DESIGN ........................................................................ 164
APPENDIX B ANALYSIS OF CUSTOM DESIGN OUTPUT BY LINK ........... 170
APPENDIX C I-4 PROTOTYPE OPERATING MODE DISTRIBUTIONS ........ 177
REFERENCES ........................................................................................................ 182
x
LIST OF FIGURES
Figure 1-1: U.S. Emissions of CO2 by Energy Consuming Sector and Fuel Type (2006) ..... 8
Figure 1-2: U.S. GHG Emissions by Gas Type, 2008 (MMT of CO2 equivalent) ................. 8
Figure 2-1: U.S. Transportation Greenhouse Gas Emissions by Gas, CO2e (2006) ............ 17
Figure 2-2: U.S. Greenhouse Gas Emissions by End Use Economic Sector,2006 ............... 18
Figure 2-3: U.S. Greenhouse Gas Emissions from Transportation by Mode, 2006 .............. 19
Figure 2-4: Vehicle Miles Traveled by Light Duty Vehicles ................................................ 22
Figure 2-5: GHG Emissions from US Freight Sources ......................................................... 23
Figure 3-1: I-4 Downtown Corridor (Orlando, FL) .............................................................. 49
Figure 3-2: Application of Managed Lanes in VISSIM ........................................................ 60
Figure 3-3: Research Approach Scheme ............................................................................... 62
Figure 4-1: VIMIS 1.0 Software ........................................................................................... 64
Figure 4-2: Design File for Input into VIMIS ....................................................................... 65
Figure 4-3: VISSIM Module in VIMIS ................................................................................. 66
Figure 4-4: OPMODE Module in VIMIS ............................................................................. 68
Figure 4-5: MOVES Module in VIMIS ................................................................................ 69
Figure 5-1: I-4 Downtown Corridor and Master Link Count Locations ............................... 71
Figure 5-2: Small Portion of Network Overlaid on Aerial Map ........................................... 73
Figure 5-3: Peak Hour Variable Speed Limits on I-4 ........................................................... 82
Figure 5-4: DMS Travel Time Information on I-4 ................................................................ 83
Figure 5-5: Field Congestion on I-4 at SR 408 Off Ramp .................................................... 84
Figure 5-6: Simulated Congestion on I-4 at SR 408 Off Ramp ............................................ 85
Figure 6-1: Test-bed Prototype of the I-4 Corridor ............................................................... 90
Figure 6-2: Summary of Stepwise Regression for Initial Model (D-Design) ..................... 101
Figure 6-3: Summary of Stepwise Regression for Final Model (D-Design) ...................... 102
Figure 6-4: Validation of the Regression Model by Link ................................................... 104
xi
Figure 6-5: Summary of Stepwise Regression for Final Model (I-Design) ........................ 105
Figure 6-6: Prediction Variance for the Design Factors at Center Points. .......................... 107
Figure 6-7: Prediction Variance for the Design Factors at Center Points (Log Space). ..... 107
Figure 6-8: Interaction Profiles for the Design Factors at Optimal Settings (Log Space). . 108
Figure 6-9: Prediction Variance for the Design Factors at Optimal Settings (Log Space). 108
Figure 6-10: Speed - CO2 Emission Rates Relationship ..................................................... 113
Figure 6-11: Speed-CO2 Emission Rates at Different Temp, Truck & Grade Levels ........ 117
Figure 6-12: Volume-CO2 Emission Rates at Different Temp, Truck & Grade Levels ..... 119
Figure 6-13: Traffic Volume – CO2 Emission Rates Relationship (0%Trucks- 0%Grade) 120
Figure 6-14: Speed Spectrum on Volume-CO2 Emission Rate Curves .............................. 121
Figure 6-15: Stochastic Speed – Density Relationship ....................................................... 122
Figure 6-16: CO2 Surface Profiler for the Predicted model. ............................................... 123
Figure 7-1: Test-bed Prototype of the I-4 Corridor ............................................................. 126
Figure 7-2: Total Emissions by Vehicle Type & Estimation Approach ............................. 133
Figure 7-3: Emissions Variation on Corridor Links for PC by Estimation Approach ........ 136
Figure 7-4: Link Operating Mode Distribution by Vehicle Type on Selected Links .......... 138
Figure 8-1: Pollutant Emissions Comparisons by Scenario ................................................ 152
Figure 8-2: Emissions Scenario Comparison by Vehicle Type .......................................... 156
Figure 8-3: Link Emission Rate Comparison – RTL vs. EX Scenarios .............................. 158
xii
LIST OF TABLES
Table 2-1: National Emissions Estimates .............................................................................. 15
Table 2-2: US Transportation Sector Green House Gas Emissions, 2006 ............................ 20
Table 3-1: Partial Layout of a Generic Experimental Design ............................................... 47
Table 3-2: Summary of Project Level Parameters ................................................................ 57
Table 5-1: Master Link Counts Comparison Based on Best Seed Number .......................... 76
Table 5-2: Master Link Counts Comparison Based on Other Seed Numbers....................... 76
Table 5-3: Paired T-test of Actual vs. Simulated Data .......................................................... 80
Table 5-4: Network Evaluation for I-4 during Peak Hour .................................................... 83
Table 6-1: Factors and Levels ............................................................................................... 89
Table 6-2 Partial Layout of D-Optimal Design for Five Seven-Level Continuous Factors .. 89
Table 6-3: Summary of Project Level Parameters ................................................................ 92
Table 6-4: MOVES Speed Bins ............................................................................................ 94
Table 6-5: Design Setting Versus Actual Setting .................................................................. 96
Table 6-6: Volume-Speed-CO2 Emission Rates at Zero Truck and Zero Grade Levels ..... 111
Table 7-1: Excerpt from VISSIM Vehicle Trajectory Data ................................................ 125
Table 7-2: Summary of project level parameters ................................................................ 127
Table 7-3: Emissions by pollutant, source type, link & vehicle activity characterization .. 132
Table 7-4: Link emissions per vehicle-mile by source type ................................................ 140
Table 8-1: Pollutant Emissions Comparison by Scenario ................................................... 150
Table 8-2: MOE Scenario Comparisons ............................................................................ 157
1
1. INTRODUCTION
1.1 Background
Emissions of greenhouse gases (GHGs), primarily carbon dioxide (CO2), are
contributing to global climate change, which is believed by many to be one of the most
critical environmental issues facing the world this century. CO2 from transportation is
expected to remain the major source of total U.S. greenhouse gas emissions (IPCC 2008).
To help Florida reduce GHGs, the state adopted the California motor vehicle emission
standards for GHG in July 2007 (Executive Order 07-127). Transportation as a whole
represents about 40 percent of Florida's total GHG emissions, second only to the electric
utility sector. Moreover, ambient air quality standards have been established for several
pollutants associated with transportation, including carbon monoxide (CO) and
particulate matter (PM-10 and PM-2.5). In addition to these criteria pollutant emissions,
motor vehicles emit volatile organic compounds (VOCs) and nitrogen oxides (NOx), both
of which are ozone precursors. Nationally, on-road transportation sources are responsible
for 21 percent of VOCs emissions, 32 percent of NOx emissions, and 50 percent of CO
emissions (NEI Trends, 2008).
Transportation agencies and researchers have a long history of implementing
techniques to calculate transportation-related emissions. Traditional methods for creating
2
emission inventories utilized annual average estimates. One comparison of annual
estimates with monthly estimates of vehicular emission provided similar results, implying
that detailed calculations were not necessary for annual emissions inventories (Cooper
and Arbrandt, 2004). Travel demand models have been utilized to provide an
intermediate level of detail (daily values). However, static planning models were found to
ignore individual vehicle activity, which leads to underestimation of pollutant emissions,
as they do not account for link capacity and other dynamic variables. As a result,
estimates of emissions based on static planning models suffer from significant biases in
different traffic conditions (You, et al. 2010). Currently, more accuracy has been
established using microscopic analyses through the reduction of time and distance scales
while splitting the network links into sub-links and utilizing second-by-second operations
to calculate emissions.
As stated in the USEPA report (2006): ―The emission factors (EFs) used in the
U.S. GHG Inventory for highway vehicles are based on laboratory testing of vehicles‖.
Although the measured testing environment simulates actual driving conditions, results
merely approximate real world vehicle activity and interactions due to the stochastic
nature of the transportation system. The USEPA reported that for some vehicle and
control technology types, the testing did not yield statistically significant results within
the 95 percent confidence interval, requiring reliance on expert judgment when
developing the EFs. In those cases, the EFs were developed based on comparisons of fuel
3
consumption between similar vehicle and control technology categories (USEPA, 2006).
Since 95% of transportation GHG emissions are in the form of CO2 (USEPA, 2009),
uncertainty in the CO2 estimates has a much greater effect on the transportation sector
estimates than uncertainty associated with nitrous oxide (N2O), methane (CH4), or
hydrofluorocarbon (HFC) emissions. Other vehicular pollutants are important in spite of
their small contribution to the total. CO is a criterion pollutant with two national ambient
air quality standards (NAAQS), and is used in project level analyses. NOx is a criterion
pollutant that is crucial due to its role (along with volatile organic compounds – VOCs) in
ozone formation. Despite all the past studies conducted on GHG and criteria pollutants
emissions, there is still a crucial need to identify and examine microscopically, key
transportation-related parameters that contribute to vehicle emissions.
Several analyses have been conducted in the literature to identify the main factors
that contribute to the increase in vehicular emissions. The majority of these factors are
found to be traffic related. However, the transportation system encompasses various
disciplines with different traffic, planning, design, and environmental factors. Therefore,
based on the literature findings, key parameters that are traffic-related (traffic volume,
truck percentage, speed limits); geometry-related (road grade) and environment-related
(temperature) are selected for detailed evaluation in this research.
4
1.2 Research Objective
The primary objective of this research is to present a more sophisticated approach
based on stochastic microscopic simulation of traffic and air quality impacts to identify
major key factors contributing to vehicle emissions through experimental design
methodologies. Several studies have shown that microscopic simulation provides better
estimates of vehicular emissions as it models explicitly second by second vehicles‘
accelerations/decelerations, lane changing and merging/diverging, which are typical in
congested conditions.
This research developed a framework for utilizing assessment tools to estimate
CO2 GHG emissions as well as CO, NOx and PM pollutant emissions and then used this
process to apply certain mitigation strategies to a severely congested downtown corridor
in Orlando, Florida, along Interstate 4 (I-4). Mitigation strategies included developing
simulation scenarios that are focused on testing proposed future improvements to the
downtown corridor in central Florida. For example, the introduction of Managed Lanes
(ML) on the I-4 corridor was modeled using the VISSIM model and emissions were
calculated from the simulated traffic during peak periods using MOVES, the latest EPA
emissions model.
The research developed techniques to enhance the current methodology in
calculating emissions factors on limited access highways to ensure that adequate
5
mitigation is provided. This research is important because it contributed to improving the
interface between traffic simulation models and the next generation of modal emissions
models MOVES2010; measuring real-world second-by-second vehicle dynamics and
comparison to corresponding simulated conditions.
The main objectives of the dissertation can be summarized, as follows:
1- To understand the contribution of key transportation parameters to vehicular
emissions and air pollution in a stochastic-microscopic environment via
experimental design methodologies.
2- To develop and assess a new technique through a model that identifies and
calculates vehicular emissions on limited access highways.
3- To improve the interface between traffic simulation models and the next
generation modal emissions model MOVES2010.
4- To evaluate operational improvements and assess mitigation strategies that
advance the congestion management process on limited access highways.
1.3 Research Value to Practitioners
The research methodology provided detailed information on traffic parameters as
well as air quality issues to a reasonable level of detail, taking into account modeling
capabilities and the cost of acquiring data. By examining the input and output parameters
6
of traffic and emissions models, the research identified the most efficient forms of
connection between them and the possibility of developing a hierarchy of models. The
developed model has a scenario modeling capability which could be used to inform
practitioners of the potential effectiveness of proposed mitigation strategies and
measures. Furthermore, the proposed method provided traffic practitioners with improved
emission estimates on limited access highways based on a microscopic-stochastic
approach. The methodology can also be expanded to include other types of street
characteristics and traffic conditions such as arterials and emission processes.
1.4 Research Response to Current or Future Needs Statewide
According to the Florida Department of Environmental Protection (FDEP, 2011)
website, ―The Florida Clean Car Emission Rule, 62-285.400, F.A.C., came into effect on
February 15, 2009. This rule, however, will only apply to future makes and models of
passenger cars, light-duty trucks, and sport utility vehicles.‖ The U.S. Environmental
Protection Agency (USEPA) is considering whether to take action regarding GHG, and
the National Highway Traffic Safety Administration (NHTSA) has proposed Corporate
Average Fuel Economy (CAFE) standards that would achieve GHG reductions,
indirectly, through raising the federal standard for minimum miles per gallon (mpg).
Once enacted, the CAFE standards will apply in Florida (FDEP, 2011). Therefore,
understanding the contribution of these types of vehicles‘ emissions to GHG along with
7
CAFE standards will result in greater GHG reductions compared to just relying on the
federal CAFE rules.
Figure 1-1 and Figure 1-2 show two graphics that have been prepared as part of
(Cooper and Alley, 2010) 4th edition of the air pollution control textbook. These graphs
demonstrate how much CO2 is emitted in the U.S. and how much from each sector. The
environmental impacts of transportation systems are significant, responsible for 20% to
25% of the world‘s energy consumption and carbon dioxide emissions every year. The
social and economic impacts of transportation systems have formed a strong need for a
sustainable transportation system which can meet the increasing traffic mobility needs by
building new infrastructure such as Managed Lanes while minimizing the negative
effects from GHG emissions to the society, economy, and environment.
8
Figure 1-1: U.S. Emissions of CO2 by Energy Consuming Sector and Fuel Type (2006)
Figure 1-2: U.S. GHG Emissions by Gas Type, 2008 (MMT of CO2 equivalent)
9
2. LITERATURE REVIEW
This section is divided into several parts. First, a brief background on green house
gases (GHG) and criteria pollutants is given and their effect on climate change and
human health is explained. Second, detailed information on transportation GHG
emissions and in particular the impacts of carbon dioxide (CO2) emissions with respect to
transportation factors are provided. Third, information on carbon content and how CO2
emissions are calculated is discussed. Synopsis of US corporate average fuel economy
(CAFE) program and its final rule are presented in the next section. Examples of past
research about traffic emissions models, micro-simulation and transportation factors are
discussed and finally, the development of EPA and non-EPA emissions models and
software tools throughout the years is presented. It should be noted that a majority of the
information provided in this section is obtained from the US EPA website,
Intergovernmental Panel on Climate Change (IPCC 2008) and previous reports.
2.1 Greenhouse Gas Components and Climate Change
Common Greenhouse gases (GHG) include carbon dioxide (CO2), methane
(CH4), nitrous oxide (N2O), ozone, water vapor, and chlorofluorocarbons (CFC). Many of
these gases are naturally occurring and are necessary to maintain an atmospheric
10
temperature that supports human life (IPCC, 1996). However, GHGs trap heat in the
earth‘s atmosphere.
GHGs are produced by both natural and human activities and can be removed
through natural processes as well. However, human-produced GHGs have significantly
exceeded natural absorption rates since the industrial revolution due to the increased
combustion of fossil fuel. Unlike other pollutants, CO2 as well as other GHGs take
several years to leave the atmosphere. Atmospheric lifetimes are estimated to be 50-200
years for CO2, 9-15 years for CH4, and 120 years for N2O (IPCC, 1996). The
combinations of the previously mentioned conditions (fossil fuel combustion,
deforestation and atmospheric decay) have contributed to the increased concentration of
these gases. Since the beginning of the industrial revolution, atmospheric concentrations
of CO2 have increased by 36 percent, CH4 concentrations have more than doubled, and
N2O concentrations have risen by approximately 18 percent (IPCC, 2007). Human
activities over the past 70 years have also produced synthetic chemicals that are powerful
greenhouse gases with atmospheric lifetimes ranging from years to millennia (IPCC,
1996). These substances include hydroflurocarbons (HFCs), chlorofluorocarbons (CFCs)
and sulfur hexafluoride (SF6). GHG emissions are projected to continue to rise. The
Intergovernmental Panel on Climate Change (IPCC) estimates that in the absence of
additional climate policies to reduce GHG emissions, baseline global GHG emissions
will increase anywhere from 25 to 90 percent between the years 2000 and 2030, with CO2
11
emissions from energy use growing between 40 and 110 percent over the same period
(IPCC, 2007).
According to the IPCC, ―Warming of the climate system is unequivocal, as is now
evident from observations of increases in global average air and ocean temperatures,
widespread melting of snow and ice, and rising global average sea level‖ (IPCC, 2007).
The IPCC‘s report also describes the anticipated consequences of climate change with
potential temperature increases above 2°C (3.6°F). According to the IPCC (2007), global
GHGs must be reduced to 50-to-85 percent below year 2000 levels by 2050 to keep
warming to 2.0-to-2.4°C (3.6-to-4.3°F).
2.2 Criteria Pollutants and Health Effects
The Clean Air Act requires EPA to set National Ambient Air Quality Standards
(NAAQS) for six common air pollutants which are ozone, particulate matter (PM),
carbon monoxide (CO), nitrogen oxides (NOx), sulphur dioxide (SO2) and lead (Pb).
These are commonly known as "criteria pollutants". Significant portions of mobile source
emissions are composed mainly of three of these criteria pollutants primarily CO, NOx,
PM and one other class of pollutants volatile organic compounds (VOCs) (Air Emission
Sources, 2011).
12
Vehicle emissions also contribute to the formation of photochemical smog (smoke
and fog). During the hot season, pollutants such as NOx and VOCs react to form ground
level ozone (O3) and other pollutants. During the cold season, vehicle emissions can be
trapped near the ground during winter time, a phenomenon known as ―temperature
inversion‖ where colder air is trapped beneath a layer of warmer air. This phenomenon
leads to high concentrations of primary pollutants such as nitrogen dioxide (NO2), CO
and PM2.5. Generally, studies relate smog to several respiratory and cardiovascular
illnesses. Also, numerous studies have concluded that pollutants are found in greater
concentration near major roadways and intersections than local roads. The following
outlines these common pollutants in more detail along with some of the health effects
associated with each pollutant:
Carbon Monoxide (CO): CO results from the vehicle‘s incomplete combustion
of fuels especially at low temperatures. Gasoline engines emit higher amounts of CO than
diesel engines, due to their lower combustion temperature compared to diesel. Carbon
monoxide has the impact of decreasing the amount of oxygen in the blood. At extremely
high levels, CO can cause death but these are not found outdoors. The highest levels of
CO usually occur during the colder months as mentioned earlier especially during night
time due to temperature inversion where vehicle emissions are high and inversion
conditions are more frequent. Motor vehicle exhaust contributes about 60 percent of all
CO emissions nationwide (National Air Quality, 2002).
13
Nitrogen oxides (NOx): NOx is a generic term used to describe the grouping of
NO, NO2 and other oxides of nitrogen. NOx is a group of gases that play a major role in
the formation of ozone. Most NOx is colorless and odorless except for NO2 feature is
brown in color. They are mainly created during fuel combustion especially at high
temperatures where engines burn a small amount of the nitrogen in the air along with
nitrogen compounds from the vehicle fuels. Diesel engines generally produce greater
amounts of NOx than gasoline engines due to their higher combustion temperatures. NOx
can irritate airways leading to lung illnesses. NOx also are precursors of smog
components such as Ozone (O3).
Particulate Matter (PM): PM can be a primary or secondary pollutant.
"Primary" particles, such as dust or black carbon come from several sources such as
passenger cars, trucks, buses, factories and construction sites. "Secondary" particles are
formed from chemical reactions with other emissions. They are formed when gases from
fuel combustion such as motor vehicles or power plants react with sunlight and water
vapor, indirectly. PM2.5 describes the "fine" particles that are less than or equal to 2.5 µm
in diameter. PM10 refers to all particles less than or equal to 10 µm in diameter (about
one-seventh the diameter of a human hair). Diesel engines emit significantly more PM
than gasoline engines. Fine particulate matter can be inhaled deeply in the lungs which
can aggravate symptoms in individuals suffering from respiratory or cardiovascular
diseases. Diesel PM is recognized by many agencies such as the World Health
14
Organization (WHO), United States Environmental Protection Agency (USEPA), and
California Air Resources Board (CARB) to be very toxic compared to gasoline PM and a
potential human carcinogen.
Volatile Organic Compounds (VOCs): VOCs represent hundreds of different
compounds. They come from incomplete fuel combustion. Other VOC emissions come
from evaporation of fuel especially during refueling. Gasoline engines emit higher
amounts of VOCs than diesel engines due to the greater volatility of fuel. Different VOCs
vary broadly in toxicity, and many are precursors of ozone.
Sulphur Dioxide (SO2): SO2 is emitted from the sulphur combustion found in the
fuel. Most of SO2 come from diesel engines since they contain more Sulphur than
gasoline engines.
Lead, Air Toxics, Coolants and Other emissions: Vehicles also emit toxic air
pollutants such as benzene, butadiene, soot, acrolein, and formaldehyde. Some
components are VOCs, while others are in the form of particles. Freon or R12 used in
older air conditioning systems are known as ozone depleting substances which are
emitted through leaks or during repairs. Newer vehicles refrigerant (R134a) are still
considered as GHG pollutants although they are non-ozone depleting coolants. The
storage and distribution of vehicle fuels also cause air pollution emissions such as the
emission of hydrocarbon (HC) vapors during refueling of vehicles.
15
EPA (Air Emission Sources, 2011) estimates nationwide emissions of ambient air
pollutants and their precursors. These estimates are based on actual monitored readings or
engineering calculations of the amounts and types of pollutants emitted by vehicles,
factories, and other sources. Emission estimates are based on many factors, including
levels of industrial activity, technological developments, fuel consumption, and vehicle
miles traveled. Table 2-1 below shows that emissions of the common air pollutants and
their precursors have been reduced substantially since 1980.
Table 2-1: National Emissions Estimates
Millions of Tons Per Year
1
1980
1
1985
1
1990
1
1995
2
2000
2
2005
2
2010
Carbon Monoxide (CO) 1
178
1
170
1
144
1
120
1
102
8
81
5
57
Lead 0
0.074
0
0.023
0
0.005
0
0.004
0
0.003
0
0.002
0
0.002
Nitrogen Oxides (NOx) 2
27
2
26
2
25
2
25
2
22
1
19
1
13
Volatile Organic
Compounds (VOC)
3
30
2
27
2
23
2
22
1
17
1
18
1
11
Particulate Matter (PM)
PM10
PM2.5
6
NA
4
NA
3
2
3
2
2
2
2
1
1
0.9
Sulfur Dioxide (SO2) 2
26
2
23
2
23
1
19
1
16
1
15
8
8
Totals
2
267
2
250
2
220
1
191
1
161
1
136
9
90
16
2.3 Transportation Greenhouse Gas Emissions & CO2 Share
GHGs are produced from multiple sectors of the economy, including industrial
sources, electric power plants, residences, and agriculture, as well as different
transportation modes. Unlike air pollutants, GHGs are global in nature. They do not
create toxic ―hot spots,‖ but rather are well-mixed in the atmosphere. Thus, the impacts
of one ton of carbon dioxide emissions are the same no matter where it is emitted, or by
what sector of the economy. In that sense, the relative effect of transportation emissions
on the global climate can be approximated by their relative magnitude compared to all
other global emissions. The primary GHGs produced by the transportation sector are
carbon dioxide, methane, nitrous oxide, and hydrofluorocarbons (HFC) (CCSP, 2008).
Carbon dioxide, a product of fossil fuel combustion, accounts for 95 percent of
transportation GHG emissions in the United States, as illustrated in Figure 2-1.
Hydrofluorocarbons, which are used in automobile, truck, and rail air conditioning and
refrigeration systems, account for another 3 percent of U.S. transportation emissions.
Nitrous oxide and methane, which are both emitted as byproducts of combustion, account
for the remainder of the U.S. transportation GHG emissions inventory (EPA, 2009).
17
Figure 2-1: U.S. Transportation Greenhouse Gas Emissions by Gas, CO2e (2006)
Source: U.S. EPA (2008). Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990 to 2006.
18
Transportation emissions account for 29 percent of U.S. GHG emissions, and over
5 percent of global GHG emissions (EPA, 2008). Most of the domestically produced
emissions are included in the industry sector shown in Figure 2-2.
Figure 2-2: U.S. Greenhouse Gas Emissions by End Use Economic Sector,2006
(million metric tons CO2 equivalent)
Source: U.S. EPA (2008). Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990 to 2006.
19
As shown in Figure 2-3 and Table 2-2, direct emissions from light-duty vehicles,
which include passenger cars and light duty trucks accounted for about 59 percent of U.S.
transportation GHG emissions in 2006. Emissions from freight accounted for about 19
percent of emissions. Commercial aircraft accounted for about 12 percent. All other
modes accounted for about 10 percent of total emissions. Overall, on-road vehicles
accounted for 79 percent of emissions (EPA, 2008).
Figure 2-3: U.S. Greenhouse Gas Emissions from Transportation by Mode, 2006
Source: U.S. EPA (2008). Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990 to 2006.
20
GHG emissions from the U.S. transportation sector increased about 34 percent
from 1990 to 2006. The growth in U.S. transportation GHG emissions accounted for
almost one-half (47 percent) of the increase in total U.S. GHG emissions for the period.
Emission trends vary by transportation mode. Medium and heavy-duty truck GHG
emissions increased 77 percent from 1990 to 2006, while light duty vehicles increased 24
percent. On-road vehicles accounted for 96 percent of the increase in transportation
emissions during that period; 55 percent from light-duty vehicles, 40 percent from
medium and heavy-duty trucks, and one percent from other modes (EPA, 2006).
Table 2-2: US Transportation Sector Green House Gas Emissions, 2006
21
From 1990 to 2006, an increase in vehicle-miles traveled (VMT) and a stagnation
of fuel economy across the U.S. vehicle fleet, caused light-duty vehicle GHG emissions
to grow by 24 percent. VMT increased 39.4 percent between 1990 and 2006, as shown in
Figure 2-4. Trends in transportation GHGs can generally be seen as a race between fuel
economy and VMT. If VMT growth outpaces improvements in fuel economy, emissions
will grow. If fuel economy improvements outpace VMT growth, emissions will decline.
Recent trends indicate that light duty vehicle emissions are leveling off as VMT growth
slows and fuel economy improves. Growth in passenger vehicle VMT slowed from an
annual rate of 2.6 percent from 1990 to 2004 to an average annual rate of 0.6 percent
from 2004 to 2007 (EPA 2009). In 2008, VMT on all streets and roads in the United
States decreased for the first time since 1980, likely due to a combination of high fuel
prices and a weakened economy. In addition, average new vehicle fuel economy
improved from 2005 to 2007 as the market share of passenger cars increased compared to
light-duty trucks; also a response to higher fuel prices and a weakening economy (EPA
2009).
22
Figure 2-4: Vehicle Miles Traveled by Light Duty Vehicles
Source: Bureau of Transportation Statistics. National Transportation Statistics
23
GHG emissions from freight trucks have increased at a greater rate than all other
freight sources, as shown in Figure 2-5.
Figure 2-5: GHG Emissions from US Freight Sources
Source: U.S. EPA (2008). Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990 to 2006.
24
2.4 Calculating CO2 Emissions
As described in the (EPA, 2006) report for calculating average CO2 Emissions
resulting from gasoline and diesel fuel, ―One of the primary determinants of carbon
dioxide (CO2) emissions from mobile sources is the amount of carbon in the fuel. Carbon
content varies, but typically we use average carbon content values to estimate CO2
emissions‖.
The Code of Federal Regulations (40 CFR 600.113) provides values for carbon
content per gallon of gasoline and diesel fuel which EPA uses in calculating the fuel
economy of vehicles:
Gasoline carbon content per gallon: 2,421 grams
Diesel carbon content per gallon: 2,778 grams
IPCC guidelines for calculating emissions inventories require that an oxidation
factor be applied to the carbon content to account for a small portion of the fuel that is not
oxidized into CO2. For all oil and oil products, the oxidation factor used is 0.99 (99
percent of the carbon in the fuel is eventually oxidized, while 1 percent remains un-
oxidized (EPA, 2006).
25
Finally, to calculate the CO2
emissions from a gallon of fuel, the carbon emissions
are multiplied by the ratio of the molecular weight of CO2
(m.w. 44) to the molecular
weight of carbon (m.w.12): 44/12.
- CO2
emissions from a gallon of gasoline = 2,421 grams x 0.99 x (44/12) =
8,788 grams = 8.8 kg/gallon = 19.4 pounds/gallon
- CO2 emissions from a gallon of diesel = 2,778 grams x 0.99 x (44/12) =10,084
grams = 10.1 kg/gallon = 22.2 pounds/gallon
2.5 Feasibility of Field Capturing CO2
Capturing CO2 from air is possible either naturally through plants or chemically
through many ways such as bubbling air through a calcium hydroxide (CAOH) solution
to remove CO2. However, economic considerations must be given to the dilution ratio in
air, which is roughly one part in three thousand. It would be possible to move the air
mechanically but only at speeds that are easily achieved by natural flows such as passing
over some recyclable sorbent (Klaus et al., 1999). Once this process of extracting CO2
out of the air is done, the downstream process deals with volumes and masses and is
therefore not subject to the amplification factor resulting from the dilution process.
Scrubbing out CO2 is not the only cost considered in extracting CO2 from air. Moreover,
the sorbent used has to be recovered to release CO2 in a concentrated disposal stream and
26
then the CO2 has to be disposed. These process steps are far more expensive than the
capture process. Another difficult process to manage is capturing CO2 from the
transportation sector (Klaus et al., 1999). Although transitioning towards the electric or
hydrogen fueled vehicles is in process, it would take a long time to accomplish. Even
though it has been proposed (Seifritz et al., 1993), it is not economically viable to collect
the carbon dioxide of a vehicle directly at the source since the mass flows would be
prohibitively large. A unit mass of fuel results in roughly three mass units of gaseous CO2
that would need to be stored and then shipped to another disposal site. Therefore,
capturing CO2 is simply not practical because of the mass, storage and shipping costs
involved.
2.6 US CAFE Program
2.6.1 Overview of Joint EPA/NHTSA National Program
In December 2007, Congress enacted the Energy Independence and Securities Act
(EISA), amending the Energy Policy Conservation Act (EPCA) to require substantial,
continuing increases in fuel economy standards. The Corporate Average Fuel economy
(CAFE) standards address most, but not all, of the real world CO2 emissions since vehicle
air conditioner are turned off during fuel economy testing. Fuel economy is determined
by measuring the amount of CO2 emitted from the tailpipe. The carbon content of the test
27
fuel is then used to calculate the amount of fuel that had to be consumed per mile in order
to produce that amount of CO2. Finally, that fuel consumption figure is converted into a
miles-per-gallon figure. CAFE standards also do not address the 5–8 percent of GHG
emissions that come from nitrous oxide (N2O), and methane (CH4) as well as emissions
of CO2 and hydrofluorocarbons (HFCs) related to operation of the air conditioning
system (EPA, 2009).
In 2004, the California Air Resources Board (CARB) approved standards for new
light duty vehicles, which regulate the emission of not only CO2, but also other GHGs.
Thirteen states and the District of Columbia, comprising about 40 percent of the light
duty vehicle market, have adopted California‘s standards. These standards apply to model
years (MY) 2009 through 2016 and require CO2 emissions for passenger cars and the
smallest light trucks of 323 g/mi in 2009 and 205 g/mi in 2016 and for the remaining
light trucks of 439 g/ mi in 2009 and 332 g/mi in 2016. To promote the National
Program, in May 2009, California announced its commitment to take several actions in
support of the National Program, including revising its program for MYs 2009–2011 and
MYs 2012–2016. This will allow the single national fleet produced by automakers to
meet the two Federal requirements and to meet California requirements as well. Several
automakers and their trade associations also announced their commitment to take several
actions in support of the National Program, including not contesting the final GHG and
CAFE standards for MYs 2012–2016 (EPA, 2009).
28
2.6.2 Summary of the Joint Final Rule
In this joint rulemaking, EPA is establishing GHG emissions standards under the
Clean Air Act (CAA), and NHTSA is establishing CAFÉ standards under the EPCA of
1975, as amended by the EISA of 2007. This joint final rule covers passenger cars, light-
duty trucks, and medium duty passenger vehicles built in MYs 2012 through 2016
(NHTSA, 2009). These vehicle categories are responsible for almost 60 percent of all
U.S. transportation-related GHG emissions. The National Program is estimated to result
in approximately 960 million metric tons of total carbon dioxide equivalent emissions
reductions and approximately 1.8 billion barrels of oil savings over the lifetime of
vehicles sold in MYs 2012 through 2016. In total, the combined EPA and NHTSA 2012–
2016 standards will reduce GHG emissions from the U.S. light-duty fleet by
approximately 21 percent by 2030 over the level that would occur in the absence of the
National Program (NHTSA, 2009).
2.7 Previous Studies to Model and Estimate Traffic Emissions
Many studies have reported on the variation of emission factors (EFs) with
average vehicle speed. The largest EFs for CO and other pollutants tend to occur at
speeds of less than 20 mph because of inefficient engine operation and travel at low
speeds. CO2 emissions are linked directly to fuel consumption, and so CO2 emissions per
mile go up at very low or very high speeds. Knowledge of traffic-flow patterns is also
29
relevant because local pollutant concentrations (more important for CO and PM – not so
much for CO2) are directly proportional to vehicle numbers and their characteristics
(Bogo et al., 2001). Marsden et al. (2001) studied CO emissions in microscopic traffic
modeling based on vehicle speed and classification using vehicle acceleration,
deceleration, cruising and idle inputs, enriched acceleration, state of repair of the vehicle
emission control system and type of engine. They showed that vehicle-exhaust emissions
depend strongly on the fuel-to-air ratio.
Sturm et al. (1998) described three approaches for compiling emission inventories
based on ‗actual driving behavior‘, ‗specific streets‘ and ‗vehicle-miles traveled‘ (VMT).
The parameters considered were travel demand, traffic condition, vehicle-operating mode
(cruising, idling, accelerating or decelerating), and vehicle-operating condition (cold or
hot start, average speed, load, trip length, frequency of trips). Furthermore, vehicle
parameters (model and year, state of maintenance, engine type and size, emission
reduction devices, accrued mileage, fuel-delivery system), and the fuel characteristics
(type, volatility, chemical composition) were also included. Besides, driver behavior,
local climate conditions, and topography were considered.
A study by Hallmark et al.(2002) found that driving patterns (e.g., speeds) at
different intersections are significantly influenced by queue position, downstream and
upstream lane volume, incidents, percent of heavy vehicles, and posted link speed.
30
Emissions also vary with respect to drivers‘ attitude, experience, gender, physical
condition, and age. Aggressive driving increases emissions compared to normal driving
(De Vlieger et al., 2000). Sierra Research found that most drivers spend about 2% of total
driving time in aggressive mode, which contributes about 40% of total emissions (Samuel
et al., 2002).
Nesamani et al. (2007) proposed an intermediate model component that can
provide better estimates of link speeds by considering a set of Emission Specific
Characteristics (ESC) for each link. The intermediate model was developed using
multiple linear regression and evaluated using a microscopic traffic simulation model.
The evaluation results showed that the proposed emission estimation method performed
better than current practice and was capable of estimating time-dependent emissions if
traffic sensor data are available as model input.
Chu and Meyer (2009) described an analysis that utilized EPA's MOBILE6.2
vehicle emissions modeling software to identify freeway locations with large pollutant
emissions and estimated the changes in emission associated with Truck-only toll (TOT)
lanes. Emissions including hydrocarbon (HC), carbon monoxide (CO), nitrogen oxide
(NOx), and CO2 were estimated by emission factors associated with various vehicle types
and average speeds. The CO2 calculation was limited due to lack of sensitivity in the
model to speed variation, which was one of the benefits of the implementation of TOT
31
lanes. The change in vehicle speeds was applied to estimate the change in fuel
consumption and CO2 emissions. The results showed that voluntary and mandatory use of
TOT lanes would reduce total CO2 emissions on all freeway lanes by 62%.
In an effort by Int Panis et al. (2011) to determine PM, NOx and CO2 emission
reductions from speed management policies in Europe, they examined the impact on
urban versus highway traffic with different modeling approaches--microscopic (VeTESS-
tool) versus macroscopic (COPERT). Results indicated that emissions of most classic
pollutants do not rise or fall dramatically. The effects of specific speed reduction schemes
on PM emissions from trucks were ambiguous but lower maximum speed (e.g., 55-65
mph) for trucks consistently result in lower fuel consumption and in lower emissions of
CO2. In an earlier attempt by Int Panis et al. (2006) to model instantaneous traffic
emissions and the influence of traffic speed limits, they concluded that the speed
management impact on vehicle emissions is complex. The frequent acceleration and
deceleration movements in the network may significantly reduce the benefits of changing
the overall average speed. The conclusion from that study was that active speed
management had no significant impact on total pollutant emissions.
Boriboonsomsin and Barth (2009) evaluated the effect of road grade on vehicle
fuel consumption (and thus carbon dioxide emissions). The real-world experimental
32
results showed that road grade does have significant effects on the fuel economy of light-
duty vehicles both at the roadway link level and at the route level.
Bachman et al. (2000) investigated a GIS-based modeling approach called the
Mobile Emission Assessment System for Urban and Regional Evaluation (MEASURE).
MEASURE provides researchers and planners with a means of assessing motor vehicle
emission reduction strategies. Estimates of spatially resolved fleet composition and
activity are combined with activity-specific emission rates to predict engine start and
running exhaust emissions. Engine start emissions are estimated using aggregate zonal
information. Running exhaust emissions are predicted using road segment specific
information and aggregate zonal information.
Liping and Yaping (2005) calculated emission factors for three emission modes;
hot emissions, emissions from vehicles after they have warmed up to their normal
operating temperature, cold-start emissions, the emissions from vehicles while they are
warming up and the water temperature is below 70 °C, and the evaporative emissions.
Husch (1998) applied SYNCHRO, a macroscopic traffic-flow model with a built-
in simplified emission model, for estimating vehicle emissions by first predicting the fuel
consumption as a function of vehicle-miles, delay in vehicles hour-per-hour, and stops in
stops per hour. Fuel consumption was then multiplied by an adjustment factor (differs
depending on the type of emissions) to estimate the vehicle emissions.
33
These studies facilitated the advancement of emission models that account for
start emissions, vehicle activity and roadway types. For example, the latest version of
MOBILE6 included emission rates and off-cycle emissions that reflect real-world traffic
conditions more accurately and can account separately for start emissions and running
emissions. It is capable of estimating emissions by roadway type, time of day, and other
characteristics (US EPA, 2002). Also EMFAC included low emission vehicle standards
and EPA Tier II standards (CARB, 2000) and assumes modest emission reductions for
appropriate inspection and maintenance programs. It produces separate emission factors
for cold starts, hot starts, and hot stabilized conditions. Modal emission models based on
various vehicle-operating modes have also been emerged as alternatives (Barth et al.,
1996a) and (Guensler et al., 1998). The accuracy of these models, however, depends on
estimates of traffic-network activity obtained from travel forecasting models, which are
still based on steady state analyses. Barth et al. (1996b) developed a methodology to
utilize both traffic sensor and microscopic data to estimate emissions, but it does not
consider road geometry and cannot be used for links without loop detectors. Models
based on it incorporate standard conditions established in laboratory dynamometer
driving tests and predicted CO, HC, and nitric Oxide (NO) emissions (US EPA, 1997).
A number of microscopic traffic models estimate vehicle emissions as a function
of vehicle type, speed, and acceleration on a second-to-second basis. The US Federal
Highway Administration (1997) developed CORSIM, a microscopic model, and used
34
vehicle emission rates from the dynamometer testing as the basis of its emissions model.
It determined the emissions on each link by applying speed and acceleration related
emission rates to each vehicle for each second the vehicle traveled on the given link. Van
Aerde (1995) also computed the fuel consumption for each vehicle on a second-by-
second basis as a function of speed and acceleration using the INTEGRATION traffic
emission model. Further, he included an estimation of vehicle emissions on a second-by-
second basis as a function of fuel consumption, ambient air temperature, and the extent to
which a particular vehicle‘s catalytic-converter already warmed up during an earlier
portion of a trip.
Yu (1998) developed the ONROAD model for estimating vehicular CO and HC
emissions based on on-road data and establishes relationships between the on-road
vehicle-exhaust emission rates and vehicle instantaneous speed profile, which is a
function of different traffic demand and control scenarios. The model estimates the
implications of alternative traffic control and management strategies on emissions. A
traffic simulation model easily incorporates ONROAD in situations where a vehicle‘s
instantaneous speed profile can be tracked consistently. Further, it indicates that
MOBILE and EMFAC underestimate on-road vehicle emissions for all vehicle types.
Yu‘s work also includes comparisons of instantaneous emission rates among emission
models, showing that emissions from the TRANSYT model deviate from those from
35
ONROAD, while MOBILE and EMFAC exhibit consistency in their emission rate
estimations.
Studies as old as Alexopoulos et al. (1993) developed a model for spatial and
temporal evaluation of traffic emissions in metropolitan areas. Gertler and Pierson (1994)
showed that improving the inputs to mobile source emission models, rather than
developing new models, can reduce the differences between their predictions and
observed concentrations of CO and HC.
Electronic fuel injection systems optimize the fuel flow to ensure balanced
combustion (Heywood, 1988). In this case, CO2 and NOx are the main products of the
combustion. In contrast, diesel engines operate with a lower fuel to air ratio than petrol
engines using lean burning fuel and air mixtures (Al-Omishy and Al-Samarrai, 1988).
Al-Deek et al. (1997) liaised with the UCF air quality research team and
conducted ozone modeling using UAM. They developed FLINT (the FLorida
INTersection) air quality model, a Gaussian-based model based on macroscopic theory
for calculating idling emissions and predicting CO concentrations near intersections.
Density, flow, vehicle composition, v/c ratio, the number of traffic lights per mile,
signal coordination, and the number of stops per mile are traffic-related variables.
Congested traffic conditions increase emissions and reduces speed compared to free flow
36
conditions (Andre and Hammarstrom, 2000 and Vlieger et al., 2000). Rakha et al. (2000)
concluded that proper signal coordination could reduce emissions up to 50%.
Roadway environmental characteristics along the road can have a significant
influence on link speed. A study by Galin (1981) found that the land use adjacent to roads
strongly influences speed. The type of land use (e.g., residential or commercial) is
especially influential.
Shu et al. (2010) developed a multiple linear regression model to disaggregate
traffic-related CO2 emission estimates from the parish-level scale to a 1 × 1 km grid
scale. Considering the allocation factors (population density, urban area, income, road
density) together, they used a correlation and regression analysis to determine the
relationship between these factors and traffic-related CO2 emissions, and developed the
best-fit model. The result reveals that high CO2 emissions are concentrated in dense road
network of urban areas with high population density, and low CO2 emissions are
distributed in rural areas with low population density and sparse road networks. The
proposed method of Shu can be used to identify the emission ―hot spots‖ at fine scale and
is considered more accurate and less time-consuming than the previous methods.
A microscopic simulation platform for estimating vehicle emissions that can
capture the vehicles' instantaneous modal activities was developed by Chen et al. (2007).
They integrated the microscopic traffic-emission simulation platform by using the
37
microscopic traffic simulation model VISSIM and the modal emission model CMEM on
a sub-network selected from the Haidian district of Beijing to evaluate the network‘s
traffic and emission conditions.
2.8 EPA Emissions Models and Analysis Tools Softwares
There are a variety of tools available to transportation practitioners for analyzing,
measuring, and projecting vehicular emissions. These also include tools and methods to
estimate GHG emissions and develop inventories for quantifying the effects of
transportation projects, technologies, and strategies. Results from such programs are used
to guide policy and planning decisions at reducing emissions. The following information
is obtained from the US EPA website (http://www.epa.gov/oms/models.htm).
MOBILE6 was used to produce motor vehicle emission factors for use in
transportation analysis and can be used at any geographic level within the U.S.
NONROAD Model links to information on the NONROAD emission inventory
model, which is a software tool for predicting emissions of hydrocarbons, carbon
monoxide, oxides of nitrogen, particulate matter, and sulfur dioxides from small and
large nonroad vehicles, equipment, and engines. This model produces estimates of
criteria pollutant emissions and CO2 from all non-road sources, with the exception of
commercial marine vessels, locomotives, and aircraft. The model calculates past, present,
38
and future emission inventories for 80 basic and 260 specific non-transportation
equipment categories.
MOVES (MOtor Vehicle Emission Simulator) is EPA‘s current official model for
estimating air pollution emissions from cars, trucks and motorcycles. This modeling
system estimates emissions for on-road and non-road sources for a broad range of
pollutants and allow multiple scale analysis. This system replaced MOBILE6 and will
eventually replace NONROAD.
NMIM or National Mobile Inventory Model, is a free, desktop computer
application developed by EPA to help develop estimates of current and future emission
inventories for on-road motor vehicles and nonroad equipment. NMIM uses current
versions of MOBILE6 and NONROAD to calculate emission inventories, based on
multiple input scenarios that can be entered into the system to calculate national,
individual state, or county inventories.
Fuels Models links to information on EPA's heavy-duty diesel fuel analysis
program which seeks to quantify the air pollution emission effects of diesel fuel
parameters on various nonroad and highway heavy-duty diesel engines. It also links to
the Complex Model and the Simple Model used for the Reformulated Gasoline Program.
39
OMEGA the Optimization Model for Reducing Emissions of Greenhouse Gases
from Automobiles, which estimates the technology cost for automobile manufacturers to
achieve variable fleet-wide levels of vehicle greenhouse gas emissions.
Climate Leadership in Parks (CLIP) currently is not publicly available. This tool
calculates emissions based on fuel consumption and/or vehicle miles traveled and thus
allows for greenhouse gas (GHG) and criteria pollutant emissions estimation at a more
local level.
COMMUTER Model This model analyzes the impacts of transportation control
measures (TCMs) on vehicle miles traveled (VMT), criteria pollutant emissions, and
CO2.
State Inventory Tool (SIT) This tool will help develop a comprehensive GHG
inventory at the state level by allowing users to enter their own state-specific activity data
to estimate emissions.
State Inventory Project Tool This tool is based on the State Inventory Tool (SIT)
and forecasts emissions through 2020 to allow users to compare trends back to 1990.
2.9 Examples of Non-EPA Emissions Models
Comprehensive Modal Emission Model (CMEM), which is one of the newest
power demand-based emission models, was developed at the University of California,
40
Riverside (Barth et al., 2000). The model estimates LDV and LDT emissions as a
function of the vehicle‘s operating mode. The term ‗comprehensive‘ is utilized to reflect
the ability of the model to predict emissions for a wide variety of LDVs and LDTs in
various operating states (e.g., properly functioning, deteriorated, malfunctioning).
Vehicles were categorized in the CMEM model based on a vehicle‘s total emission
contribution. Twenty-eight vehicle categories were constructed based on a number of
vehicle variables. These vehicle variables included the vehicle‘s fuel and emission
control technology (e.g., catalyst and fuel injection), accumulated mileage, power-to-
weight ratio, emission certification level (tier0 and tier1), and emitter level category (high
and normal emitter). In total 24 normal vehicles and 4 high emitter categories were
considered (Barth et al., 2000).
The Virginia Tech microscopic energy and emission model (VT-Micro model) was
developed from experimentation with numerous polynomial combinations of speed and
acceleration levels. Specifically, linear, quadratic, cubic, and quartic terms of speed and
acceleration were tested using chassis dynamometer data collected at the Oak Ridge
National Laboratory (ORNL). The final regression model included a combination of
linear, quadratic, and cubic speed and acceleration terms because it provided the least
number of terms with a relatively good fit to the original data (R2 in excess of 0.92 for all
measures of effectiveness (MOE)).
41
EMFAC Model, California Air Resources Board (CARB) This model produces
emission rates and inventories for criteria air pollutants and CO2. It is the approved
emissions model used in the State of California for SIP development, conformity
analysis, and other analyses that are typically conducted using MOBILE6 in other states.
The Greenhouse Gases, Regulated Emissions, and Energy Use in Transportation
(GREET) model Argonne National Laboratory, This full life-cycle model was designed
to evaluate energy and emission impacts of advanced vehicle technologies and new
transportation fuel combinations on a full fuel-cycle/vehicle-cycle basis.
Intelligent Transportation Systems Deployment Analysis System (IDAS) Federal
Highway Administration, This sketch planning analysis tool is used to estimate the
impacts, benefits, and costs resulting from the deployment of Intelligent Transportation
System (ITS) components of over 60 types of ITS investments.
Lifecycle Emissions Model (LEM) (not publicly available), University of
California, Davis, This model estimates energy use, criteria pollutant emissions, and
CO2-equivalent GHG emissions from transportation and energy sources.
Long Range Energy Alternatives Planning (LEAP) System Commend: Community
for Energy, Environment and Development. LEAP is a software tool for energy policy
analysis and climate change mitigation assessment that uses integrated modeling to track
energy consumption, production, and resource extraction in all sectors of an economy.
42
The MARKAL-MACRO Model, Department of Energy. This model is an
integration of two models, MARKAL and MACRO to link the use of energy and
environmental resources to the economy. It can forecast emissions sources and levels for
CO2, SOx, and NOx, and any user-specified pollutants and wastes.
MiniCAM Model, Pacific Northwest National Laboratory (PNNL). This model
forecasts CO2 and other GHG emissions and estimates the impacts on GHG atmospheric
concentrations, climate, and the environment. As of 2005, this model was superseded by
ObjECTS-MiniCAM, a C++ version of the model that incorporates object-oriented
programming designs for increased flexibility, maintenance, and modeling detail.
National Energy Modeling System (NEMS), Energy Information Administration
(EIA), USDOE. This modeling system represents the behavior of energy markets and
their interactions with the U.S. economy. It contains a transportation demand module
(TRAN) that has several sub-modules and that uses NEMS inputs.
System for the Analysis of Global Energy Markets (SAGE), (Not publicly
available), USDOE, SAGE is an integrated set of regional models that provides a
technology-rich basis for estimating regional energy supply and demand. For each region,
reference case estimates of end-use energy service demands (e.g., car, commercial truck,
and heavy truck road travel; residential lighting; steam heat requirements in the paper
industry) are developed on the basis of economic and demographic projections.
43
Transitional Alternative Fuels and Vehicle Model (TAFV). University of Maine.
The TAFV model represents economic decisions among auto manufacturers, vehicle
purchasers, and fuel suppliers and can predict the choice of alternative fuel technologies
for light-duty motor vehicles.
VISION Model, Argonne National Laboratory. This model forecasts energy use
until 2050 and provides estimates for advanced light- and heavy-duty highway vehicle
technologies and alternatives, including potential energy use, oil use, and carbon
emissions impacts. The model was designed as a simplified and fast way to assess the
potential impact of new fuel technologies on energy use and carbon emissions.
World Energy Protection System (WEPS) Transportation Energy Model (TEM),
USDOE. This structural accounting model for transportation energy use generates mid-
term forecasts of the transportation sector's energy use in order to evaluate the effect of
changes in fuel economy on carbon emissions.
Based on the above literature review, it is concluded that limited research has
been done on quantifying the impacts of transportation emissions through experimental
design methodologies in a stochastic-microscopic environment. The models listed above
show that although there are a variety of traffic emissions models to calculate general
vehicular emissions, very few focused on developing and calculating carbon dioxide and
other pollutants emissions on limited access highways and particularly considering most
44
of the transportation factors (volume, length, speed, temperature and grade) in one model.
Also, even though micro-simulation is gaining strength in many transportation aspects, it
is still a new subject in emissions applications. The simulations that have been applied so
far focused mostly on one of the transportation factors, which is speed and were limited
to model existing conditions. Furthermore, research directed at investigating decision
processes underlying emissions mitigation strategies is still in its infancy.
This research attempts to develop experimental design methodologies to quantify
and mitigate transportation emissions in response to current environmental challenges. In
addition, this approach is extended to integrate a powerful microscopic emissions
package ―MOVES2010a‖ along with a powerful microscopic traffic simulation package
―VISSIM‖. VISSIM is stochastic in nature and models explicitly second by second
accelerations/decelerations, lane changing and merging/diverging, which are typical in
stop-and-go traffic. VISSIM output is integrated with MOVES to quantify emission
inventories and rates.
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3. RESEARCH APPROACH
In order to achieve the stated objectives, the following research methodology was
implemented:
1. Design of Experiments (DOE)
2. Development of Calibrated Base Scenario using VISSIM Model
3. Estimation of Scenario-Based Emissions using MOVES Model
4. Statistical Analysis Using JMP Software
5. Development of Emission Prediction Model (Micro-TEM)
6. Application of Mitigation Strategies
7. Findings of Research Results and Conclusions
3.1 Design of Experiments (DOE)
In many scientific investigations, the concern is to optimize the system.
Experimentation is one of the popular activities used to understand and/or improve a
system. This can be achieved by studying the effects of two or more factors on the
response at two or more values known as ―levels‖ or settings simultaneously. This type of
standard experiment is known as factorial design. Cost and practical constraints must be
considered in choosing factors and levels. Therefore, two-level factorial designs are
common for factor screening in industrial applications (Jones and Montgomery, 2010).
However, if a non-standard model is required to adequately explain the response or the
model contains a mix of factors with different levels, types or results in an enormous
number of runs, the requirements of a standard experimental design will not fit the
research requirements (Johnson et. al, 2011). As stated in Johnson‘s Expository Paper on
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Optimal Designs: ―Designing experiments for these types of problems requires a different
approach. We can’t look in the textbook or course notes and try to match the designs we
find there to the problem‖, (Johnson et. al, 2011). Under such conditions, optimal custom
designs are the recommended design approach. Choosing an optimality criterion to select
the design points to be run is another requirement. Since the factors of interest in the
experiment consist of five (5) quantitative factors, each with six (6) levels and one
quantitative response (CO2), the standard number of full factorial design needed to cover
all cases would amount to 7,776 runs (65). Even choosing to run a fractional factorial of
l (k-p)
―where l is the number of levels of each factor investigated, k is the number of
factors investigated, and p describes the size of the fraction of the full factorial used‖, the
methodology needed to generate designs for more than two levels is too cumbersome.
Accordingly, the D-optimality and I-optimality criteria were the two custom designs
employed for this experiment which are explained in greater detail in Chapter 6. The
factors and levels ranges included:
1) Volume (2,000 vph - 7,000 vph)
2) Speed (20 mph - 70 mph)
3) Trucks (0% - 15%)
4) Grade (0% - 5%)
5) Temperature (50 F - 100 F)
The optimal custom design of this experiment resulted in 140 runs (70 runs for
each design) obtained by considering all main effects, quadratic and cubic effects along
with two-way and three-way factor interactions of the studied factors with six possible
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levels in addition to center points. Table 3-1 provides a partial basic layout of the
planning matrix in a standard order which describes the experimental plan in terms of the
actual values or settings of the factors. Each row of the table represents one set of
experimental conditions that when run produces a value of the response variable y. The
response variable was the amount of Carbon Dioxide (CO2) emissions in (kg) produced
in each scenario. The five factors were designated A though E with the levels (-1) as the
low setting and (+1) as the high setting. The experiment was conducted in a microscopic
and stochastic environment.
Table 3-1: Partial Layout of a Generic Experimental Design
Run# A B C D E Y
Volume Speed Truck % Grade% Temperature CO2
1 -1 -1 -1 -1 -1 Y1
2 1 -1 -1 -1 -1 Y2
3 -1 1 -1 -1 -1 Y3
4 1 1 -1 -1 -1 Y4
5 -1 -1 -1 -1 -1 Y5
6 1 -1 -1 -1 -1 Y6
7 -1 1 -1 -1 -1 Y7
8 1 1 -1 -1 -1 Y8
9 -1 -1 1 -1 -1 Y9
10 1 -1 1 -1 -1 Y10
11 -1 1 1 -1 -1 Y11
12 1 1 1 -1 -1 Y12
13 -1 -1 1 -1 -1 Y13
14 1 -1 1 -1 -1 Y14
15 -1 1 1 -1 -1 Y15
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3.2 Development of Calibrated Base Scenario Using VISSIM Model
Because the collection of a large representative second-by-second vehicle
operation dataset for every traffic circumstance is not realistic, the use of microscopic
traffic simulation models to replicate the real world second-by-second driver behavior for
hundreds of vehicles and traveling patterns is essential.
3.2.1 The VISSIM Model
VISSIM 5.3 is a microscopic, stochastic and behavior based simulation model
developed by PTV to model urban traffic and public transit operations. The program can
analyze traffic and transit operations under constraints such as lane configuration, traffic
composition, traffic signals, transit stops, etc., thus making it a useful tool for the
evaluation of various alternatives based on ITS-based transportation engineering and
planning measures of effectiveness. VISSIM can be applied as a powerful tool in a
variety of transportation problem settings (www.ptvamerica.com).
3.2.2 Calibration & Validation of VISSIM Model
As mentioned previously, the study corridor was the Interstate 4 (I-4) located in
downtown Orlando, Florida as shown in Figure 3-1. Peak hour information was gathered
for the eastbound direction of the evening peak period including traffic counts, travel
49
time and delays for the distance traveled, truck percentages and temperature of the day.
The existing network was coded in the micro-simulation model VISSIM, calibrated and
validated to replicate existing conditions. The calibrated base scenario was used for
comparison purposes against the mitigation scenarios as discussed in Chapter 8.
The following methodology was utilized to calibrate and validate the VISSIM
simulation model. The Florida Department of Transportation (FDOT) monitors the I-4
corridor and collects traffic counts at several field stations on a yearly basis. The FDOT
traffic information database formed the basis of the calibration and validation step.
Furthermore, travel time and delay information posted on the Dynamic Message Signs
(DMS) on the I-4 downtown Corridor were compared against the simulated data.
Figure 3-1: I-4 Downtown Corridor (Orlando, FL)
50
The network geometry including horizontal curves, grades and ramp locations is a
critical step in the calibration process. Initial evaluation was conducted to compare the
distribution of simulation outputs (traffic volumes, travel times, delays, and queues) with
the field data as well as initial identification of the calibration parameters. The calibration
involved accurately modeling drivers‘ driving behavior and ensuring reasonable
throughput values on the network. Travel times, delay and queuing were also used for
validation. Multiple runs were conducted with each combination of parameters and the
distribution of traffic volumes and queues were compared to the field data. If the field
data lies within the simulated distribution of the simulated data, thus the parameters and
their ranges were considered as appropriate. Evaluation of parameter sets was completed
by comparing the simulations results of default parameters against adjusted parameters as
well as corridor visualization as explained in detail in Chapter 5.
3.3 Estimation of Scenario-Based Emissions using MOVES Model
Estimating vehicle emissions based on second-by-second vehicle operation
encourages the integration of microscopic traffic simulation models with more accurate
vehicle activity-based regional mobile emissions models. Therefore, the simulated
vehicle driving cycle data was integrated into the latest EPA‘s mobile emissions model
MOVES2010 to calculate CO2 emissions based on project-level constraints. A prototype
of the calibrated I-4 corridor was utilized to test for the 140 scenarios mentioned earlier.
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Network output from the microscopic traffic simulation model VISSIM were input into
MOVES to calculate emissions.
3.3.1 The MOVES Model
MOVES2010 model is the state-of-the-art upgrade to EPA‘s previous modeling
tools for estimating emissions from vehicles. MOVES2010 replaces the previous model
MOBILE6.2, for estimating on-road mobile source emissions.
MOVES modeling process requires the input of vehicle types, time periods,
geographical areas, pollutants, vehicle operating modes and road types. The model then
accurately reflect vehicle operating condition, such as extended idle or running emissions
and provide estimates of total emissions or emission rates. The characteristics of a
particular scenario are developed by creating a Run Specification database (RunSpec).
The MOVES model is totally different from previous EPA mobile source
emissions models in that it is purposely designed to be flexible with databases. And so, if
new data becomes available, it would be easily incorporated into the model. Furthermore,
MOVES enables the import of specific data needs. The MOVES model includes a default
emissions database for the entire United States. The data was collected from several
sources such as EPA research studies, Census Bureau vehicle surveys, Federal Highway
Administration, and other federal, state, local, industry and academic sources. MOVES is
52
continuously updated, especially for analyses concerning State Implementation Plans
(SIPs) and conformity purposes.
MOVES uses Vehicle Specific Power (VSP) and instantaneous speeds to
calculate emission rates on a second-by-second basis. This approach added flexibility to
emissions covering all achievable combinations of instantaneous speeds and accelerations
which were essential in developing emissions for any driving pattern. VISSIM generates
trajectories on a second by second basis, vehicle number and types, vehicle location,
speed and acceleration for each vehicle within each link on a second-by-second basis.
The same temporal resolution and level of detail modeled in VISSIM supports the
integration of MOVES. The trajectory data from VISSIM were converted to match
MOVES input. MOVES calculates the emissions for each vehicle over time using the
trajectory data based on a specified time step in each calculation.
3.3.2 Validation of MOVES Model
The Clean Air Act (CAA) requires EPA to regularly update its mobile source
emission models and therefore EPA continuously collects data and measures vehicle
emissions to ensure they have the most accurate information on mobile source emissions.
The model is based on results of millions of emission tests and substantial improvements
related to the understanding of vehicle emissions.
53
MOVES2010a estimates air pollution emissions from cars, trucks, motorcycles,
and buses. It is approved by EPA for assessing official state implementation plan (SIP)
submissions as well as transportation conformity analyses outside of California. It can be
used also to evaluate the benefits of several mobile source control strategies either local
or regional and for policy evaluation. MOVES2010a is considered the best available tool
for quantifying criteria pollutant and GHG emissions for the transportation sector.
This detailed approach to modeling allowed the incorporation of large amounts of
validated data from several sources, such as vehicle inspection data and maintenance
(I/M) programs, remote sensing device (RSD) testing, and portable emission
measurement systems (PEMS). This approach also helped in scenario comparisons and
identified differences resulting from changes to vehicle speeds and acceleration patterns.
As mentioned earlier, the output from the VISSIM model was used as input into
the MOVES model. The first input step was to create the Project Level database where
the imported data is stored in a MySQL database. The data was specified in separate text
files for each input parameter such as volumes, speed, link length, grade, etc. This
process was repeated for each of the following main input files:
1. Meteorology Data Importer
2. Source Type Population Importer
3. Age Distribution Importer
4. Vehicle Type VMT and VMT Fractions
5. Link Source Types Importer
6. Links Importer
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7. Operating Mode Distribution Importer
8. Link Drive Schedules Importer
9. Fuel Supply Importer
10. Fuel Formulation Importer
As stated in the MOVES technical guidance report (EPA, 2010):
3.3.2.1 Meteorology Data Importer
The Meteorology Data Importer includes temperature and humidity data for
months, zones, counties, and hours that are included in the RunSpec. While the MOVES
model contains 30-year average temperature and humidity data for each county, month,
and hour, specific data for the modeled location and time should be used.
3.3.2.2 Source Type Population Importer
The Source Type Population Importer includes the number of vehicles in the
geographic area which is to be modeled for each vehicle or "source type" selected in the
RunSpec. Data must be supplied for each source type (e.g., passenger car, passenger
trucks, light commercial trucks, etc.) selected in the RunSpec.
3.3.2.3 Age Distribution Importer
The Age Distribution Importer includes data that provides the distribution of
vehicle counts by age for each calendar year (yearID) and vehicle type (sourceTypeID).
The distribution of ageIDs (the variable for age) must sum to one for each vehicle type
and year.
55
3.3.2.4 Vehicle Type VMT and VMT Fractions
The Vehicle Type VMT importer includes yearly vehicle miles traveled (VMT)
and the monthly, type of day, and hourly VMT fractions. These values will represent
county-specific values for the County Data Manager.
3.3.2.5 Link Source Types Importer
The Link Source Types Importer is used to enter the fraction of the link traffic
volume which is driven by each source type. For each linkID, the
sourceTypeHourFraction must sum to one across all source types.
3.3.2.6 Links Importer
The Links Importer is used to define individual roadway links of the study
network. The MOVES links need not correspond to traffic modeling "links" but each link
should be uniform in its activity. Each link requires a linkID that is used to reference the
link in the network). Other required inputs for each link are countyID, zoneID, and, the
length of the roadway link in units of miles, the traffic volume on the roadway link in
units of vehicles per hour, the average speed of all of the vehicles on the roadway link in
the given hour, and the average road grade of a particular link. In addition to roadway
links, a project may include a single ―off-network‖ (parking lot or other non-road zone)
link.
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3.3.2.7 Operating Mode Distribution Importer
The Operating Mode Distribution Importer is used to import operating mode
fraction data for source types, hour / day combinations, roadway links and pollutant /
process combinations that are included in the RunSpec and Project domain. These data
are entered as a distribution across operating modes. Operating modes are "modes" of
vehicle activity that each have a distinct emission rate. For example, "running" activity
has modes that are distinguished by their Vehicle Specific Power and instantaneous
speed. "Start" activity has modes that are distinguished by the time the vehicle has been
parked prior to the start ("soak time").
3.3.2.8 Link Drive Schedules Importer
The Link Drive Schedules Importer is used to define the precise speed and
grade as a function of time (seconds) on a particular roadway link. The time domain is
entered in units of seconds, the speed variable in miles per hour and the grade variable in
percent grade (i.e., vertical distance / lateral distance; 100% grade equals a 45 degree
slope).
3.3.2.9 Fuel Supply Importer
The Fuel Supply importer is used to assign existing fuels to counties, months,
and years, and to assign the associated market share for each fuel. The market share for a
57
given fuel type (gasoline, diesel, etc.) must sum to one for each county, fuelyear (i.e.,
calendar year), and month.
3.3.2.10 Fuel Formulation Importer
The Fuel Formulation importer and the Fuel Supply importer should be used
together to input appropriate fuel data. The Fuel Formulation importer is used to select
an existing fuel in the MOVES database and change its properties, or create a new fuel
formulation with different fuel properties. Table 3-2 also shows a summary of the data
parameters used explicitly for this research to conduct the experimental design analysis.
Table 3-2: Summary of Project Level Parameters
Location County Orange County, Florida
Calendar Year 2010
Time One hour
Weekday/Weekend Weekday
Humidity 70.0 %
Road type Urban Restricted Access – represents
urban freeway road (I-4 downtown links)
with 3 lanes in each direction
Types of Vehicles Passenger cars, SUVs, Vans & Trucks
Type of Fuel Gasoline for cars and Diesel for Trucks
Roadway Length 10 miles
Link Traffic Volume 2,000 - 7,000 vehicles per hour
Link Speed Limit 20 - 70 miles per hour
Truck Traffic 0.0 – 15.0 percent
Average Road Grade 0.0 - 5.0 percent
Temperature 50 F - 100 F
58
3.4 Statistical Analysis Using JMP Software
JMP 9.0 (pronounced as "jump"), a module from SAS, is a state-of-the-art
statistical analysis software program used to perform complex statistical analyses. It
dynamically links statistics with graphics to interactively understand and visualize data.
JMP provides a comprehensive set of statistical tools for the design of experiments and
can work with a variety of data formats such as Excel, text and SAS files. JMP was used
to facilitate the generation and understanding of the results of the experiment. Of
immediate concern was the determination of the impact of the factors individually on the
response variable which included the main effects, any two-way, three-way interactions
as well as any quadratic or cubic effect.
3.5 Development of Emission Prediction Model (Micro-TEM)
The purpose of the experiment was to explore all possible settings of the studied
factors and develop a Microscopic Transportation Emissions Meta-Model ―Micro-TEM‖,
a predictive model with the ability to calculate emission rates on freeways or limited
access highways based on a stochastic-microscopic manner. Contour plots were
generated for the response surface model. With the predictive model, tentative
considerations of optimal settings were identified. The predictive model consisted of a
function of the estimated main effects and significant interactions between the factors.
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The experiment was analyzed using forward stepwise regression with all main effects and
two-way factor interactions or more as candidate effects. Alternate mathematical models
were envisaged based on examination of the residuals which were the collection of
predictive values minus the observed values. It should be noted that the experiment was
conducted for CO2. However, the methodology can be expanded to include all other
criteria pollutants of CO, NOx and PM.
3.6 Application of Mitigation Strategies
As mentioned earlier, the I-4 downtown central corridor was the network under study.
Several mitigation strategies were applied and tested in search for a plausible solution to
the congestion problem on the I-4 corridor and at the same time mitigate the impacts of
transportation emissions. Congestion pricing is one of the major applications tested and
proved its effectiveness in the largest impact on reducing GHG emissions. However,
congestion pricing along with any fee-based systems are not considered feasible without
the implementation of ITS technologies. Consequently, real-time information on traffic
conditions is essential for dynamically changing the price with demand fluctuation as is
done with Managed Lane systems. The following summarizes the proposed mitigation
strategies that were applied on the I-4 study corridor to manage future congestion and
evaluate future transportation emissions:
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o Managed Lanes (ML) (see Figure 3-2)
o Restricted Truck Lanes (RTL)
o Variable Speed Limits (VSL)
Each of the above mentioned strategies were tested and compared against the base
scenario in terms of CO2 emissions in order to validate the developed model (Micro-
TEM). Other pollutants such as CO, NOx and PM were analyzed as well.
Figure 3-2: Application of Managed Lanes in VISSIM
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3.7 Findings of Research Results and Conclusions
The results were analyzed and documented in a useful and practical format;
including a summary of the analysis, graphical illustrations and the necessary output files
to support each scenario evaluation as well as the validation of the developed emission
prediction model (Micro-TEM). Final recommendations were also provided regarding
adaptation strategies that would improve the impact of transportation on the environment.
Figure 3-3 provides a summary of the previously described research methodology which
is presented in the following flow chart:
62
Figure 3-3: Research Approach Scheme
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4. VISSIM/MOVES INTEGRATION SOFTWARE (VIMIS)
4.1 Overview
This section presents the programming efforts carried out in the development of
VIMIS 1.0 software; custom software developed to assist with automating the
experimental design and analysis. VIMIS integrates between VISSIM and MOVES in
order to facilitate the design of experiment portion on the modeled network as well as the
conversion process of the VISSIM files into MOVES files.
The program was developed in collaboration between me and my colleague (Dr.
Hesham Eldeeb). I provided all the necessary technical information and the algorithms
needed for the calculations and Dr. Eldeeb wrote the code using C Sharp (C#)
programming language which can handle most sophisticated applications. The Graphical
User Interface (GUI) developed for the software is shown in Figure 4-1.
4.2 Modules Description
The program consists of four (4) main modules which were compiled using
Microsoft Visual Studio 2008. The function of the first module ―Design Cases‖ is to
generate all the design cases (developed from ―JMP‖) needed for the experiment in
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VISSIM file format. As mentioned earlier, the custom design employed the D-optimality
and I-optimality criteria; each resulted in 70 cases (a total of 140 cases).
Figure 4-1: VIMIS 1.0 Software
65
The ―Design Cases‖ module requires two main inputs:
1. The ―Design file‖ is the text file (from JMP) containing a list of the design cases
where each row represents a design of the 70 cases as shown in Figure 4-2.
2. The ―Base filename‖ is the base name for VISSIM files.
The output is 70 VISSIM input files. The main purpose is to prepare all VISSIM
input files with the corresponding information in each design case.
Figure 4-2: Design File for Input into VIMIS
66
The second module ―VISSIM‖ automates the simulation runs for each case with
different seed number in order to account for the randomness and variability of the
simulation output. It also requires two main inputs as shown in Figure 4-3:
1. ―Case folder‖ is the folder containing the input files from previous step.
2. ―Layout filename‖ is the VISSIM initialization file containing all the necessary
settings of all types of output needed in each run. For example, trajectory file
aggregated for the whole hour of the simulation run or minute-by-minute or on a
second-by-second basis.
Figure 4-3: VISSIM Module in VIMIS
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The third module is the operating mode ―OPMODE‖. This is a crucial step in the
conversion process. It converts the trajectory output file from VISSIM into an operating
mode distribution for input into MOVES. The output from VISSIM can be a very large
file that cannot be opened by a conventional program such as excel or word because these
programs attempt to load the whole file in memory and it is too big to fit in memory.
However, VIMIS doesn‘t load the whole file all at once, it extracts one time step at a time
(sec by sec or minute by minute), and so VIMIS is not affected by the file size. VIMIS
processes each time step as it reads it.
In most cases, if the output was set on a second-by-second basis, the file size can
reach 10 gigabytes and cannot be opened. The magnificence of this module is that it
converts this 10 gigabyte file into 300 kilobyte file (max), without the need to load the
whole file as mentioned previously, and in a MOVES input format containing all the
necessary links to be analyzed, types of pollutants and emission processes (running,
extended idle, etc., …) to be executed by MOVES as shown in Figure 4-4. It is worth
noting that MOVES can create an operating mode distribution from two default driving
schedules based on average speeds. However, it may not be representative of the actual
driving schedules of the modeled corridor or the specific vehicle trajectories generated
from each run which highlights the importance of this module.
68
Figure 4-4: OPMODE Module in VIMIS
The fourth and last module is the ―MOVES‖ module. The main purpose was to
prepare all the necessary folders and files needed as input into MOVES to run the design
cases. It requires three main inputs as shown in Figure 4-5:
1. The ―Design file‖ as in Module-1 which contains the list of the design cases.
2. The ―VISSIM output folder‖ which contains all the output files from VISSIM
runs that will be used in MOVES in addition to the ―operating mode‖ file.
3. The ―MOVES template folder‖ which contains all the specification files needed to
be created and converted into an SQL database format to run MOVES as
explained in section 3.3.2.
69
Figure 4-5: MOVES Module in VIMIS
Afterwards, the MOVES SQL browser was utilized to export the output of all the
runs in excel format to be analyzed. VIMIS was used in all different stages of the
research including the design of experiment, base scenario as well as the application
scenarios.
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5. EVALUATION OF THE I-4 CORRIDOR
5.1 Overview of the I-4 Downtown Corridor
I-4 is a primary east-west transportation corridor between Tampa and Daytona
cities, serving commuters, commercial and recreational traffic. I-4 is known to have
severe recurring congestion during peak hours. The congestion spans about 11 miles in
the evening peak period in the central corridor area as it is considered the only non-tolled
limited access facility connecting the Orlando Central Business District (CBD) and the
tourist attractions area (Walt Disney World). The traffic on the I-4 freeway section is
collected from double inductance loops embedded in the pavement every 0.5 miles,
which extends from the Walt Disney World area on the west side of the corridor to Lake
Mary boulevard on the east side, for a total length of 39 miles. The interstate carries an
average annual daily traffic of 200,000 vehicles on segments in Orlando. It is imperative
to evaluate the environmental impacts of this corridor in terms of vehicular emissions.
The modeled section was composed of approximately a 10 mile stretch of the urban
limited access highway with three lanes in each direction, 12 on ramps and 13 off ramps
as shown in Figure 5-1. Traffic composition included 60% passenger cars, 37% SUV‘s
and 3% heavy-duty diesel trucks. Traffic counts were obtained from the latest FDOT
online traffic information.
71
Figure 5-1: I-4 Downtown Corridor and Master Link Count Locations
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5.2 Model Calibration
The calibration was conducted for the eastbound direction during the evening peak
period from 4:45 – 6:00 pm with the first 15 minutes as a warm up period to reflect field
operations with regards to the mainline volume, on and off ramp volumes, speeds and
observed queues during the peak hour.
The definition of parameter calibration refers to minimizing the misfit between
observed data from the real network and simulation results by fine-tuning parameter
values. When running VISSIM, the user can assess the results from a visual or from a
numerical point of view. The visual inspection can be observed to see the vehicle
movements on the screen visualization, in order to check for network geometry which
reflects that the traffic is moving in a realistic manner. Realistic manner means that the
vehicles on the highway do not make U-turns at the nodes or sharp turns at the beginning
or at the end of links, causing a drop in the vehicle speed. Also, sudden stops can cause
shockwaves leading to disruption in the traffic flow. This emphasizes the importance of
geometry coding. A small portion of the network geometry at SR 408 off-ramp overlaid
on an aerial map is shown in Figure 5-2. The quantitative analysis was also carried out in
parallel; when the comparison between the simulation and observed (field) data is not
within recommended guidelines, it is necessary to make some changes with selected
model input parameter values.
73
Figure 5-2: Small Portion of Network Overlaid on Aerial Map
74
Traffic simulation models contain numerous parameters and variables to define.
With regards to VISSIM, there are two main models; car following models and lane
change models. Car following models are concerned with the vehicle following behavior
that affect the flow rates depending on the selected car following model. The two car-
following models are:
- Wiedemann 74: Model mainly suitable for urban traffic (arterials) and
- Wiedemann 99: Model mainly suitable for interurban traffic (freeways)
The lane changing models affect the driving behavior based on an extensive range
of parameters. However, these are very sensitive parameters and should be adjusted with
care. The driving behavior can be defined for each link type as well as for each vehicle
class even within the same link. In some cases, these variables affect the entire network
while others are specific to individual links.
To accomplish the calibration process, a field data set, obtained from FDOT
online traffic information (http://www2.dot.state.fl.us/FloridaTrafficOnline/viewer.html),
was selected for the evening peak period. The data set consisted of 11 locations on the
network known as master links. Traffic counts on these master links were compared with
the simulated traffic volumes.
As a first step in the calibration process, an initial set of runs was conducted using
the VISSIM default parameters and with different seed numbers. The seed value is a
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starting value for the random number generator which are called by the program and used
in processes that calculate many different parameters within the simulation. After each
model run, the output produced on the master link volumes were compared to the actual
data. In order to gain results of high reliability, relative error between the simulated data
and actual data was calculated. If any volume has relative error in excess of a specific
threshold within 10%, the traffic volume on the links related to this route with the higher
error were increased or decreased according to the error. Relative error was found using
the formula:
Actual traffic volumes - Simulated traffic volumesRelative error = * 100
Actual traffic volumes
The parameters that use random numbers include car following, lane changing,
driver‘s behavior, and release of demand. Using different seed values on the same
network produce different simulation results. After several parameter adjustments and
numerous runs, the best-fit seed values were selected that matched the field data within
the 10% thresholds.
Table 5-1 and Table 5-2 show the results of three seed values that were tested in
the calibration process and matched closely the field data being within the 10% relative
error threshold. Statistical analysis was conducted using Paired t-tests as explained in the
next section.
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Table 5-1: Master Link Counts Comparison Based on Best Seed Number
Master Links Count Locations
FDOT
Counts
Seed#
35455
VISSIM_1
Relative
Error_1
JYP - OBT W. OBT 4950 4602 7.03%
OBT - Kaley Ave S. Michigan Ave 3832 3546 7.46%
Kaley Ave - Gore St S. SR 408 Off-Ramp 4037 3727 7.68%
Gore St - Church St S. SR 408 On-Ramp 2749 2569 6.55%
Church St - SR 50 N. Robinson Rd 4574 4956 -8.35%
SR 50 - Ivanhoe Blvd N. SR 50 4793 5175 -7.97%
Ivanhoe Blvd - Princeton St S. Princeton St 5933 5556 6.35%
Princeton St - Par Ave N. Princeton St 5327 5718 -7.34%
Par Ave - Fairbanks Ave N. Par Ave 5166 5603 -8.46%
Fairbanks Ave - Lee Rd S. Lee Rd 3903 4270 -9.40%
Lee Rd - Maitland Ave N. Lee Rd 4918 4883 0.71%
Overall Average 4562 4600 -0.84%
Table 5-2: Master Link Counts Comparison Based on Other Seed Numbers
Master Links Count Locations
FDOT
Counts
Seed#53
VISSIM2
Seed#1029
VISSIM3
Relative
Error_2
Relative
Error_3
JYP - OBT W. OBT 4950 4597 4635 7.13% 6.36%
OBT - Kaley Ave S. Michigan Ave 3832 3635 3520 5.14% 8.14%
Kaley - Gore St S. SR 408 Off-Rmp 4037 3811 3826 5.60% 5.23%
Gore St - Church St S. SR 408 On-Rmp 2749 2635 2616 4.15% 4.84%
Church St - SR 50 N. Robinson Rd 4574 4997 5029 -9.25% -9.95%
SR 50 - Ivanhoe Bl N. SR 50 4793 5236 5235 -9.24% -9.22%
Ivanhoe - Princeton S. Princeton St 5933 5574 5598 6.05% 5.65%
Princeton - Par Ave N. Princeton St 5327 5756 5738 -8.05% -7.72%
Par - Fairbanks Ave N. Par Ave 5166 5582 5577 -8.05% -7.96%
Fairbanks - Lee Rd S. Lee Rd 3903 4225 4208 -8.25% -7.81%
Lee - Maitland Ave N. Lee Rd 4918 4795 4803 2.50% 2.34%
Overall Average 4562 4622 4617 -1.32% -1.20%
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5.3 Statistical Analysis
Comparing the simulated and the actual volumes on the master links for all the datasets
included visual inspection and Confidence Interval method (t-test). The Confidence
Interval (C.I.) is a reliable approach for comparing a simulation model with the real-
world system. C.I. is performed for m collected sets of data from the field and n sets of
data from the model (m and n are 11 observations each). Generalization of Gossett's t-
distribution helps in testing whether or not two-sample mean come from equal or non-
equal populations. However, Paired t-test is appropriate for testing the mean difference
between paired observations when the paired differences follow a normal distribution.
The Paired t is used to compute a confidence interval and perform a hypothesis test of the
mean difference between paired observations in the population. A paired t-test matches
responses that are dependent or related in a pairwise manner. The matching helps to
account for variability between the pairs, usually resulting in a smaller error term, thus
increasing the sensitivity of the hypothesis test or confidence interval. The null
hypothesis 0H that is tested was:
H0: d = 0 versus H1: d ≠ 0
Where d is the population mean of the differences and 0 is the hypothesized mean of
the differences.
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If the null hypothesis is rejected, this infers that the two-sample means come from
different populations and are different. To compute Paired t-test, two main computations
were needed before computing the t-test. First, the pooled standard deviation of the two
samples needs to be estimated. The pooled standard deviation gives a weighted average
of the standard deviations of the two samples. The pooled standard deviation is going to
be between the two standard deviations, with greater weight given to the standard
deviation from a larger sample. The equation for the pooled standard deviation is:
Where: 1n is the sample size for the first sample, 2n is the sample size for the
second sample, 1S is the standard deviation of the first sample, 2S is the standard
deviation of the second sample, and PS is the pooled standard deviation of the two
samples.
In all work with t-test, the degrees of freedom or df is
The formula used for the Paired t-test is:
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Where, the top of the formula is the sum of the differences (i.e. the sum of d). The bottom
of the formula reads as:
The square root of the following: n times the sum of the differences squared
minus the sum of the squared differences, all over n-1.
The sum of the squared differences: ∑d2 means take each difference in turn,
square it, and add up all those squared numbers.
The sum of the differences squared: (∑d)2means add up all the differences and
square the result.
Table 5-3 shows the Paired t-test results using a confidence interval of 95%
extracted from Minitab statistics software. Upper and lower limits of the resulting
confidence intervals for the master links included zero. Therefore, the confidence interval
method suggested that there was no significant difference between the simulated and the
actual volumes on the master links of the datasets.
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Table 5-3: Paired T-test of Actual vs. Simulated Data
Paired T-Test and CI: FDOT, VISSIM_1
Paired T for FDOT - VISSIM_1
N Mean StDev SE Mean
FDOT 11 4562.00 876.94 264.41
VISSIM_1 11 4600.45 990.67 298.70
Difference 11 -38.4545 350.5531 105.6957
95% CI for mean difference: (-273.9593, 197.0502)
T-Test of mean difference = 0 (vs not = 0):
T-Value = -0.36 P-Value = 0.724
Paired T-Test and CI: FDOT, VISSIM_2
Paired T for FDOT - VISSIM_2
N Mean StDev SE Mean
FDOT 11 4562.00 876.94 264.41
VISSIM_2 11 4622.09 969.52 292.32
Difference 11 -60.0909 341.7219 103.0330
95% CI for mean difference: (-289.6628, 169.4810)
T-Test of mean difference = 0 (vs not = 0):
T-Value = -0.58 P-Value = 0.573
Paired T-Test and CI: FDOT, VISSIM_3
Paired T for FDOT - VISSIM_3
N Mean StDev SE Mean
FDOT 11 4562.00 876.94 264.41
VISSIM_3 11 4616.82 986.28 297.38
Difference 11 -54.8182 344.1554 103.7668
95% CI for mean difference: (-286.0249, 176.3886)
T-Test of mean difference = 0 (vs not = 0):
T-Value = -0.53 P-Value = 0.609
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5.4 Model Validation
As a second step, the validation process was basically validation of measures of
effectiveness (MOEs), which were part of the simulation output data. MOEs were
representatives of the system performance. One of the qualitative MOEs was the queue
length on the I-4. Quantitative MOEs included the travel time, delay and average speeds
observed on the I-4 corridor during the peak hour. As mentioned earlier, field
observations and photos took place on the corridor during the evening peak hour and
were compared with the simulated queues. Furthermore, Dynamic Message Signs (DMS)
along the I-4 corridor display data regarding the travel time and expected delays on the
10-mile stretch during the peak hour. It should be noted that Variable Speed Limits
(VSL) along the corridor were in effect as part of a safety program to improve safety.
Speed limits were observed as 30 and 40 mph as shown in Figure 5-3.
Speed limits, queue comparisons as well as the travel time and expected delays
resulting from the simulated corridor were compared with the observed field data.
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Figure 5-3: Peak Hour Variable Speed Limits on I-4
As shown in Figure 5-4 from the DMS on I-4, travel time along the 10-mile
section ranged from 24 to 40 minutes during the peak hour. Simulated travel times
resulted in the same range of 30 to 40 minutes reflecting an overall average speeds
ranging from 15 to 20 miles per hour. Table 5-4 is an excerpt from VISSIM output for
network evaluation for seed# 35455 shows the overall average network speed of 15.58
mph during the peak hour.
Simulated queues at Orange Blossom Trail (OBT), Kaley Avenue and SR 408
Off-Ramp were compared with field photos and were found to match the field congestion
at the same time step as shown in Figure 5-5 and Figure 5-6.
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Figure 5-4: DMS Travel Time Information on I-4
Table 5-4: Network Evaluation for I-4 during Peak Hour
Simulation time from 900.0 to 4500.0. Seed Value = 35455
Parameter Value
Number of vehicles in the network, All Vehicle Types 3670
Number of vehicles that have left the network, All Vehicle Types 11654
Total Distance Traveled [mi], All Vehicle Types 49787.061
Total travel time [h], All Vehicle Types 3194.945
Total delay time [h], All Vehicle Types 1886.591
Total stopped delay [h], All Vehicle Types 224.818
Average delay time per vehicle [s], All Vehicle Types 443.208
Average number of stops per vehicles, All Vehicle Types 48.568
Average speed [mph], All Vehicle Types 15.583
Average stopped delay per vehicle [s], All Vehicle Types 52.815
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Figure 5-5: Field Congestion on I-4 at SR 408 Off Ramp
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Figure 5-6: Simulated Congestion on I-4 at SR 408 Off Ramp
86
6. DEVELOPING MICROSCOPIC EMISSION PREDICTION MODEL
6.1 Overview
The main objective of this research was to study the effect of major transportation
related parameters on vehicular emissions on limited access highways and specifically in
a microscopic and stochastic manner, hence developing an emission prediction model
that is stochastic and microscopic in nature. This section explains in greater detail the
procedures of the experiment taken to arrive at the resulting model. It should be noted
that CO2 was selected as the GHG pollutant, as an example, to be studied in this
experiment. However, the same methodology can be expanded to include all other criteria
pollutants such as CO, NOx and PM.
6.2 Design of Experiments (DOE)
As mentioned in the research approach section, standard experimental designs
either using full factorial or fractional factorial did not fit this research requirements and
therefore, optimal custom designs were selected as the recommended design approach.
Also, choosing an optimality criterion to select the design points to be run was another
requirement. The custom design approach in JMP (statistical software created by SAS)
generates designs using a mathematical optimality criterion. Optimal designs are
87
computer-generated designs that aim at solving specific research problem to optimize the
respective criterion. The optimal designs fall under two main categories:
1. Designs that are optimized with respect to the regression coefficients (D-Optimality
Criteria) and
2. Designs that are optimized with respect to the prediction variance of the response (I-
Optimality Criteria).
D-Optimal designs are most appropriate for screening experiments because the
optimality criterion focuses on estimating of the coefficients precisely.
The D-optimal design criterion minimized the volume of the simultaneous confidence
region of the regression coefficients when selecting the design points (Johnson et al.,
2011). This was achieved by maximizing the determinant of X‘X over all possible
designs with specific number of runs. Since the volume of the confidence region is
related to the accuracy of the regression coefficients, a smaller confidence region means
more precise estimates even for the same level of confidence (Johnson et al., 2011).
The experiment included five (5) main factors and one quantitative response
(carbon dioxide emissions). The factors‘ levels were chosen to cover all possible
scenarios on limited access highways covering all level of service (LOS) ranging from
LOS A to LOS F as follows:
1) Volume (from 2,000 vph to 7,000 vph)
2) Speed (from 20 mph to 70 mph)
3) Trucks (from 0% to 15%)
4) Grade (from 0% to 5%)
5) Temperature (from 50 F to 100 F)
88
JMP statistical software was used to generate the custom design for this experiment.
In order to increase the number of levels in the custom design, additional search points
were added to the coordinate exchange algorithm in JMP resulting in 6 levels for each
factor plus the center points; a total of seven levels. The developed D-design and I-
Design from JMP are included in Appendix A.
The D-optimal design of this experiment resulted in a minimum of 36 runs
obtained by considering all 36 combinations of the (5) main effects, (10) two-way and
(10) three-way factor interactions as well as the (5) quadratic and (5) cubic size effects in
addition to the intercept. However, 64 runs were chosen to increase the sample size.
Additional six (6) degrees of freedom were introduced for lack of fit. These extra runs
estimated the error variance in the model and increased the denominator‘s degrees of
freedom for significance tests of the model coefficients. The total number of runs
amounted to 70 runs. The factors levels were designated from (-1) as the low setting and
(+1) as the high setting. The resulting factor settings and levels are summarized in Table
6-1. Table 6-2 provides a partial basic layout of the planning matrix in a standard order,
which describes the experimental plan in terms of the actual values of the factors. Each
row of the table represented one set of experimental conditions that produced a value of
the response variable (Y) which was the amount of Carbon Dioxide (CO2) emissions
produced in kilograms (kg).
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Table 6-1: Factors and Levels
Setting Volume Speed Truck % Grade % Temperature
-1 2000 20 0 0 50
-0.6 3000 30 3 1 60
-0.4 3500 35 4.5 1.5 65
0 4500 45 7.5 2.5 75
0.4 5500 55 10.5 3.5 85
0.6 6000 60 12 4 90
1 7000 70 15 5 100
Table 6-2 Partial Layout of D-Optimal Design for Five Seven-Level Continuous Factors
Run# Volume Speed Truck % Grade % Temperature CO2
1 1 -1 -1 1 -1 Y1
2 -1 0.4 0.4 -1 1 Y2
3 -1 1 1 -0.6 -0.4 Y3
4 0.6 -0.6 -0.6 1 -1 Y4
5 -1 -1 -1 1 1 Y5
6 -0.4 1 -1 0.6 -0.6 Y6
7 -0.4 -1 -0.4 -0.4 1 Y7
8 0.6 1 1 -1 1 Y8
9 1 0.6 -0.6 -1 0.4 Y9
10 0.4 1 0.6 -1 -1 Y10
11 -0.6 -0.6 0.4 1 -0.6 Y11
12 0.6 1 1 1 -0.6 Y12
13 1 -0.4 1 0.4 1 Y13
14 1 -1 -1 1 1 Y14
15 0 0 0 0 0 Y15
16 -0.4 0.6 0.6 1 0.6 Y16
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6.3 Test Bed Modeling
The test bed corridor under study was a prototype of the calibrated I-4 downtown
corridor. The modeled section was composed of approximately 10 mile stretch of the
urban limited access highway with three lanes in each direction. However, for
experimentation purposes, the freeway section links were aggregated into 11 links, 5
main on-ramps and 5 main off-ramps as shown in Figure 6-1. Nine (9) of the segments
were approximately 1-mile in length including horizontal curves and the first and last
links were 0.5 mile each. Traffic composition included passenger cars, SUV‘s and heavy-
duty diesel trucks which correspond to MOVES vehicle types 21, 31 and 62, respectively.
The analysis was conducted for the eastbound direction and the experimental design
encompassed all peak and off-peak conditions throughout the day reflecting all different
factors‘ levels.
Figure 6-1: Test-bed Prototype of the I-4 Corridor
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VISSIM licenses currently do not provide an integrated emissions model for
North America. Higher-level emissions – Carbon Monoxide (CO), Nitrogen Oxides
(NOx), Volatile Organic Compounds (VOC) – statistics are available only via node
evaluation. There were no results for CO2 emissions from link evaluation which also
created the motivation to link VISSIM quantitative output with MOVES.
VIMIS 1.0 was used to prepare and run the 70 cases of the D-experimental design
in VISSIM using the Component Object Module (COM) interface. Trajectory files
generated from VISSIM were configured to output vehicle speed, acceleration, location,
link length and number of vehicles and were mapped with MOVES input files through
VIMIS software as well.
6.4 Moves Project Level Data
As mentioned earlier, the output from the VISSIM model was used as input into
the MOVES model. The Project Level database was specified in separate text files for
each input parameter such as link volumes, link average speed, link length, and link
grade. This process was repeated for each run of the input files. The main project
parameters are described in Table 6-3.
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Table 6-3: Summary of Project Level Parameters
Location County Orange County, Florida
Calendar Year 2010
Month November
Time One hour
Weekday/Weekend Weekday
Temperature 50 F - 100 F
Humidity 70.0 %
Roadway type Urban Restricted Access – represents
freeway urban road with 3 lanes per direction
Types of Vehicles Passenger cars, Passenger trucks & Long
haul combination diesel trucks
Type of Fuel Gasoline for cars and diesel for trucks
Roadway Length (11 links - Total) Approx. 1 mile/link – Total of 10 miles
Link Traffic Volume 2,000 - 7,000 vehicles per hour
Link Truck traffic 0 – 15% Trucks
Average road grade 0 – 5 % upgrade
Link Average Speed 20 – 70 miles per hour
Operating Mode Running Exhaust Emissions
Output Atmospheric CO2, Total Energy
Consumption & CO2 Equivalent
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6.5 Operating Modes & Link Driving Schedules
The simulated vehicle driving cycle data was integrated into the MOVES model
based on the above mentioned project-level traffic conditions. Processing the VISSIM
output files for input into MOVES required the calculation of the operating mode for
each vehicle in the network. The operating mode is a measure of the state of the vehicle‘s
engine based on its speed and acceleration expressed in vehicle specific power (VSP). In
MOVES, operating mode distribution input will take calculation precedence over an
imported drive schedule, which will take calculation precedence over an average link
speed input when more than one is entered for a given link (EPA 2010).
There are 16 "speed bins" in MOVES which describe the average driving speed
on a road type or link. Based on the experiment, speed levels corresponded to MOVES‘
speed bins ID# 5 to 15 (20 to 70 mph). However, in some cases, actual operating speeds
recorded from VISSIM fell below or higher than the specified speeds. In this case, other
speed bins were included (less than 20 mph or greater than 70 mph). On the other hand,
maximum speeds were constrained by the type of road and type of vehicles used.
MOVES calculations only consider average speed ranges of (2.5 - 73.8 mph) for cars and
(5.8 - 71.7 mph) for heavy-duty trucks (EPA 2010). All VISSIM simulated link speeds
were checked to comply with MOVES speed ranges.
94
Table 6-4: MOVES Speed Bins
Speed Bin ID Average Bin Speed Speed Bin Range
1 2.5 speed < 2.5mph
2 5 2.5mph <= speed < 7.5mph
3 10 7.5mph <= speed < 12.5mph
4 15 12.5mph <= speed < 17.5mph
5 20 17.5mph <= speed <22.5mph
6 25 22.5mph <= speed < 27.5mph
7 30 27.5mph <= speed < 32.5mph
8 35 32.5mph <= speed < 37.5mph
9 40 37.5mph <= speed < 42.5mph
10 45 42.5mph <= speed < 47.5mph
11 50 47.5mph <= speed < 52.5mph
12 55 52.5mph <= speed < 57.5mph
13 60 57.5mph <= speed < 62.5mph
14 65 62.5mph <= speed < 67.5mph
15 70 67.5mph <= speed < 72.5mph
16 75 72.5mph <= speed
Use of VISSIM‘s link speed and link road grade allowed the MOVES model to
create an operating mode distribution from two built-in driving schedules based on its
predefined 16 speed bins and an interpolation algorithm to produce a default operating
mode distribution whose speeds bracket the given speed. In this research, we were
concerned only with the ―running modes‖. It should be noted that "running" activity has
modes that are distinguished by their Vehicle Specific Power (VSP) and instantaneous
speed. The emission rate (mass of emissions per unit of time) varies with the vehicle‘s
95
operating mode which is a function of the speed and the vehicle specific power (VSP) or
the related concept, Scaled Tractive Power (STP). Both VSP and STP are calculated
based on a vehicle‘s speed and acceleration. They differ in how they are scaled. The VSP
equation is used for light duty vehicles (source types 11-32) and the STP equation is used
for heavy-duty vehicles (source types 41-62) (EPA 2010). VSP and operating modes are
explained in detail in the next Chapter.
6.6 Design Settings versus Actual Settings
VIMIS was used to run the factorial experiment with the different factor levels and
settings as mentioned earlier. Each run of the VISSIM simulation was for one hour and
using different random number generator to account for reasonable randomness and
variability. The randomness in the experiment was also based on the random arrival
patterns of the vehicles in each run.
The combined effects or interactions between specific factors demand careful
thought prior to conducting the experiment. Because we were performing the experiment
through simulation, there were a lot of uncontrolled variables that need to be taken into
account. For example, a lot of attention was given specifically to Volume and Speed.
Random arrival of vehicles, dynamic network loading, stochasticity of the traffic system,
and unexpected traffic demand variation due to the capacity of the highway clarifies that
the design setting of the volume and/or speed were not always the same. In other words,
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setting the volume ―dial‖ at 7,000 vph input resulted in an actual volume output less than
or greater than the designed 7,000 vph instead. Likewise were the speed settings.
Furthermore, increasing the volume level more than a specific threshold affects the speed.
Compounding this complexity of the main effects identified that we should be analyzing
the actual output values instead of the design values as shown in Table 6-5 (sample of 20
runs). The rest of the runs are included in Appendix B.
Table 6-5: Design Setting Versus Actual Setting
Run# Input
Volume
Posted
Speed
Limit Trucks Grade Temp
CO2
(kg) Output
Volume
Output
Speed
1 7000 20 0.15 0.05 50 55782 3803 18.4
2 6000 70 0.15 0 100 39458 5406 45.2
3 3500 70 0 0.04 60 20822 3519 61.9
4 7000 70 0.15 0 50 37000 5708 39.3
5 7000 70 0 0 50 22474 6470 57.2
6 2000 20 0.15 0.05 100 30404 1802 19.2
7 3000 30 0.105 0.05 60 32815 2868 30.1
8 6000 20 0.105 0.015 50 33730 3855 18.2
9 7000 20 0 0 50 20095 4021 18.2
10 3000 30 0.03 0 50 12912 2823 29.9
11 6000 20 0.15 0.05 90 63403 3796 18.3
12 7000 70 0.12 0 100 38744 5975 43.5
13 2000 20 0 0.05 50 13426 1925 19.1
14 7000 20 0.15 0 50 31986 3841 18.4
15 2000 60 0.03 0.05 100 18737 2187 59.4
16 3500 20 0.15 0 85 26824 2842 18.6
17 2000 20 0.15 0 50 15112 1842 19.1
18 2000 55 0.105 0 100 12526 2243 58.4
19 7000 70 0.15 0.05 50 65773 4745 27.3
20 7000 20 0.15 0 100 37949 3855 18.4
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6.7 Analysis of Results
The results were analyzed using JMP‘s forward stepwise regression approach with
all 36 main effects (quadratic, cubic, two-way and 3-way interactions) as candidate
effects according to the effect hierarchy principle. The carbon dioxide (CO2) emissions
output values were transformed to log space for better correlation as well as improved
presentation. Preliminary analysis of the 70 runs using the stepwise regression showed an
initial model including all main effects of Volume, Speed, Trucks, Grade, and
Temperature. Other two-way factor interactions included Speed*Grade and
Trucks*Grade in addition to two quadratic effects for the Volume and Speed factors as
shown in Figure 6-2. There was no confounding between any main effects and two-way
or three-way factor interactions that are aliased with each other. Furthermore, the model
showed an adjusted correlation value of 99.47%. However, the model failed to pass the
lack of fit test. The lack of fit was significant.
As mentioned earlier, six (6) center points were added to the D-Optimal design.
The center points were used to provide an estimate of the pure error as well as testing the
significance of active factors. The lack of fit table shows a special diagnostic test and
appears only when the data and the model provide the opportunity. The idea is to estimate
the error variance independently of whether this is the right form of the model. This
occurs when multiple observations occur all at the same (x) variable, known as center
98
points. The error that is measured for these exact replicates is called pure error. This is the
portion of the sample error that cannot be explained or predicted no matter what form of
model is used. The difference between the residual error from the model and the pure
error is called lack of fit error. Lack of fit error can be significantly greater than pure
error, which means that the model represents the wrong functional form of the regressor.
In that case, a different kind of model fit should be tried.
This step-by-step iterative construction of the regression model that involved
automatic selection of independent variables can be achieved either by trying out one
independent variable at a time and including it in the regression model if it is statistically
significant, or by including all potential independent variables in the model and
eliminating those that are not statistically significant, or by a combination of both
methods.
An initial assumption was made to remove the main effect of the Temperature then
the Speed*Grade interaction. The stepwise regression was done for both cases. However,
the analysis showed no significant difference between the two cases. With further
examination of the speed factor and its interaction terms, the Temperature main effect
only had to be eliminated in order to represent a right form of the model with no
indication of lack of fit. This improved form of the model including the Volume, Speed,
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Trucks and Grade effects with their interaction terms showed an adjusted R2 of 97.9 as
shown in Figure 6-3.
The Temperature factor in the model represented the effect of air condition (AC)
being turned on in the vehicles during high or low ambient temperature which was an
indirect measure of the emission rates, thus statistically insignificant. It should be noted
that the high value of the model‘s R2 is attributed to the fact that emission rates calculated
from MOVES are based on its built-in default database. MOVES database is calibrated
and validated from field datasets as explained in Chapter 3. The model is based on results
of millions of emission tests and large amounts of validated data from several sources,
such as vehicle inspection data and maintenance (I/M) programs, remote sensing device
(RSD) testing, and portable emission measurement systems (PEMS). The main variability
in the experiment is actually attributed to the stochastic nature of the traffic simulation
environment. The resulting prediction expression is included in Appendix A.
Since the traffic network consisted of 11 links, further analysis was conducted on
link by link basis. That is each run of the experimental design resulted in 11 outputs (one
output for each link). Therefore, total sample size of 770 points was analyzed using the
stepwise regression and the same model was valid across all the links with adjusted
correlation value ranging from 94% to 98% as shown in Figure 6-4 (a) for link 1 and (b)
for link 9.
100
To recall, the primary focus of the D-optimal design was to find the main effects
that significantly influenced the response where the goal was to estimate the regression
coefficients. Another goal to the experiment was to achieve precision in terms of the
response variable and the overall prediction model which can be achieved through the I-
optimality criterion especially when the resulting model included a second-degree order.
Therefore, another design was created from JMP 9.0 using the I-optimality criterion
resulting in another 64 runs with six levels and 6 more center points for the determination
of lack of fit totaling another 70 runs. This second set of I-optimal design actually served
as a confirmation experiment to the D-optimal design as the results were identical. We
came to the conclusion of the final form of the I-Design model terms showing an adjusted
R2 value of 97.3 with no indication of lack of fit as shown in Figure 6-5.
101
RSquare
RSquare Adj
Root Mean Square Error
Mean of Response
Observations (or Sum Wgts)
0.995413
0.994725
0.039526
10.17395
70
Summary of Fit
Model
Error
C. Total
Source9
60
69
DF20.341160
0.093738
20.434898
Sum of
Squares2.26013
0.00156
Mean Square1446.674
F Ratio
<.0001*
Prob > F
Analysis of Variance
Lack Of Fit
Pure Error
Total Error
Source
57
3
60
DF
0.09368915
0.00004845
0.09373761
Sum of
Squares
0.001644
0.000016
Mean Square 101.7705F Ratio
0.0014*
Prob > F
1.0000
Max RSq
Lack Of Fit
Trucks(0,0.15)
Grade(0,0.05)
Volume(2000,7000)
Volume*Volume
Temp(50,100)
Speed(20,70)
Trucks*Grade
Speed*Grade
Speed*Speed
Term0.3123965
0.2975272
0.5468211
-0.28713
0.0737408
-0.078489
0.0545563
0.0581846
0.0673743
Estimate0.005863
0.006497
0.014267
0.017674
0.005487
0.008365
0.006141
0.008002
0.014499
Std Error53.29
45.80
38.33
-16.25
13.44
-9.38
8.88
7.27
4.65
t Ratio<.0001*
<.0001*
<.0001*
<.0001*
<.0001*
<.0001*
<.0001*
<.0001*
<.0001*
Prob>|t|
Sorted Parameter Estimates
Figure 6-2: Summary of Stepwise Regression for Initial Model (D-Design)
102
RSquare
RSquare Adj
Root Mean Square Error
Mean of Response
Observations (or Sum Wgts)
0.981603
0.979191
0.078504
10.17395
70
Summary of Fit
Lack Of Fit
Pure Error
Total Error
Source
50
11
61
DF
0.28334282
0.09259069
0.37593351
Sum of
Squares
0.005667
0.008417
Mean Square 0.6732F Ratio
0.8332
Prob > F
0.9955
Max RSq
Lack Of Fit
Figure 6-3: Summary of Stepwise Regression for Final Model (D-Design)
103
(a) LINK# 1
104
(b) LINK# 9
Figure 6-4: Validation of the Regression Model by Link
105
9
9.5
10
10.5
11
9 9.5 10 10.5 11
Ln CO2 Predicted P<.0001
RSq=0.98 RMSE=0.0781
Actual by Predicted Plot
RSquare
RSquare Adj
Root Mean Square Error
Mean of Response
Observations (or Sum Wgts)
0.976079
0.972942
0.0781
10.22937
70
Summary of Fit
Model
Error
C. Total
Source8
61
69
DF15.182380
0.372073
15.554453
Sum of
Squares1.89780
0.00610
Mean Square311.1371
F Ratio
<.0001*
Prob > F
Analysis of Variance
Lack Of Fit
Pure Error
Total Error
Source
56
5
61
DF
0.36306150
0.00901126
0.37207276
Sum of
Squares
0.006483
0.001802
Mean Square 3.5973F Ratio
0.0767
Prob > F
0.9994
Max RSq
Lack Of Fit
Trucks(0,0.15)
Grade(0,0.05)
Volume(2000,7000)
Volume*Volume
Trucks*Grade
Speed(20,70)
Speed*Grade
Speed*Speed
Term0.321709
0.3133789
0.5446681
-0.289654
0.0630133
-0.074305
0.0749066
0.0875821
Estimate0.013337
0.014748
0.027512
0.035478
0.015067
0.019448
0.020642
0.034142
Std Error24.12
21.25
19.80
-8.16
4.18
-3.82
3.63
2.57
t Ratio<.0001*
<.0001*
<.0001*
<.0001*
<.0001*
0.0003*
0.0006*
0.0128*
Prob>|t|
Sorted Parameter Estimates
Figure 6-5: Summary of Stepwise Regression for Final Model (I-Design)
106
6.8 Discussion
Prediction profiles for the significant factors selected by the model are shown in
Figure 6-6 showing the actual values of the response variable as CO2 while Figure 6-7
shows the response variable as the log space for CO2 (Log). The prediction profiler
displays prediction traces for each factor. The steepness of a prediction trace reveals a
factor‘s significance. This shows the significant effect of the trucks and grade on the
increase of carbon dioxide. Furthermore, Interaction profiles are shown on Figure 6-8.
Evidence of interaction shows as non-parallel lines. The substantial slope shift proves the
significant interaction involving speed and trucks with the grade.
When analyzing the results of the custom design, a separate prediction equation for
each dependent variable (containing different coefficients but the same terms) is fitted to
the observed responses on the respective dependent variable. Once these equations are
constructed, predicted values for the dependent variables can be computed at any
combination of levels of the predictor variables. If appropriate current values for the
independent variables were selected, inspecting the prediction profile can show which
levels of the predictor variables produce the most desirable predicted response on the
dependent variable. Through the desirability function, JMP adjusts the graph to display
the optimal settings at which CO2 would be minimized as shown on Figure 6-9.
107
Figure 6-6: Prediction Variance for the Design Factors at Center Points.
Figure 6-7: Prediction Variance for the Design Factors at Center Points (Log Space).
108
Figure 6-8: Interaction Profiles for the Design Factors at Optimal Settings (Log Space).
Figure 6-9: Prediction Variance for the Design Factors at Optimal Settings (Log Space).
109
On the other hand, the results showed that there‘s a strong correlation between the
speed and the emission rates instead of total emissions. The potential for emissions rate
reductions through travel speed adjustments was significant for speeds between about 55
and 60 mph while maintaining volume levels up to 80% and 90% of the roadway
capacity on limited access highways provided that the grade and truck percentages are at
their minimum. It was observed from the runs that at certain volume levels and speeds
ranging from 55 to 60 mph; the CO2 emission rate was minimized resulting in efficient
operation with higher miles per gallon on link by link basis as highlighted in Table 6-6
and plotted on Figure 6-10. This also showed the effect of other factors such as truck
percentages and roadway grades.
Emissions at a given speed appeared to be influenced by the vehicle‘s engine
loading getting to that speed from previous speed, which is the acceleration rate. The
difference in CO2 emissions between speeds lower than 55 mph and higher than 60 mph
were represented by a rapid succession of high speed and/or high power events
represented by the Vehicle Specific Power (VSP), which resulted in more aggressive
driving cycles likely resulting in higher emissions. The Speed was almost flat especially
in the range between 55 mph and 60 mph, while increases outside this range.
Conversely, the relationship developed between the volume and CO2 was found to
be quadratic as was the case with the speed. Figure 6-6 and Figure 6-7 also show a good
110
fit between the modeled data and the fitted curve especially when compared with the
freeway capacity. The increase in CO2 emissions was limited by the capacity of the
roadway and the amount of traffic that it can handle – in our case 7,200 vph - capacity on
a 3 lane freeway. The fitted data was also compared with the modeled data at the same
settings to examine the model validity. For example, run# 25 (Appendix A, Table A-1)
showed an output of 8232 kg while the fitted data resulted in approximately 8390 kg of
CO2 emissions, a difference of 1.9%. Further model validation is conducted in Chapter 8.
The analysis of the experiment determined the lowest settings for volume, trucks
and grade while the speed setting between 55 and 60 mph; optimum speed for engines
that requires the least amount of work to complete fuel combustion. These settings
yielded approximately 7900 kg of CO2 emissions. This also proved that the optimal
design approach was an accurate and powerful way to approach these types of
maximization/minimization problems on a response variable.
111
Table 6-6: Volume-Speed-CO2 Emission Rates at Zero Truck and Zero Grade Levels
VMT Length
(ft) Volume
(vph) Speed (mph)
Truck% Grade%
CO2 (kg)
Miles per Gallon
CO2 Per mile/Veh
667 2464 1430 19.07 0 325.1 18.10 0.487
968 2639 1937 19.83 0 459.7 18.57 0.475
1297 2464 2780 18.83 0 637.1 17.95 0.491
1415 5210 1434 19.02 0 690.1 18.08 0.488
1535 5166 1569 19.11 0 745.9 18.15 0.486
1606 5299 1600 19.03 0 782.9 18.09 0.488
1838 5274 1840 19.05 0 895.3 18.10 0.487
1895 5275 1897 19.02 0 924.0 18.08 0.488
1955 5299 1948 73.23 0 717.2 24.04 0.367
1990 5279 1990 19.10 0 967.6 18.13 0.486
2037 5290 2033 19.13 0 989.5 18.15 0.486
2080 5210 2108 69.37 0 753.3 24.35 0.362
2126 5269 2131 19.00 0 1037.6 18.07 0.488
2146 5286 2144 19.35 0 1034.8 18.29 0.482
2188 5290 2184 73.30 0 802.8 24.03 0.367
2236 5166 2285 72.11 0 818.8 24.08 0.366
2239 5286 2237 72.51 0 822.6 24.00 0.367
2248 5279 2248 73.10 0 823.6 24.06 0.366
2265 5274 2267 73.22 0 830.4 24.05 0.367
2436 5275 2439 72.80 0 896.2 23.97 0.368
2449 5299 2440 72.21 0 891.5 24.22 0.364
2635 5166 2693 70.92 0 963.8 24.11 0.366
2655 2639 5312 17.11 0 1388.6 16.86 0.523
2726 5290 2721 72.56 0 999.6 24.05 0.367
2741 5274 2744 72.32 0 998.3 24.21 0.364
2745 5279 2745 72.08 0 1001.0 24.18 0.365
2767 5286 2764 71.57 0 1001.3 24.37 0.362
2784 5269 2790 72.87 0 1024.0 23.97 0.368
2892 5275 2895 71.87 0 1051.8 24.25 0.364
2949 5166 3014 18.83 0 1446.6 17.98 0.491
3087 5210 3128 18.83 0 1515.2 17.96 0.491
3660 5269 3668 18.56 0 1813.5 17.80 0.495
3789 5275 3793 18.32 0 1893.3 17.65 0.500
3999 5274 4003 18.12 0 2012.6 17.52 0.503
4147 5299 4132 17.89 0 2105.5 17.37 0.508
4479 5279 4480 17.80 0 2281.3 17.31 0.509
4814 5290 4805 18.38 0 2400.4 17.68 0.499
112
VMT Length
(ft) Volume
(vph) Speed (mph)
Truck% Grade%
CO2 (kg)
Miles/Gallon CO2/mile/Veh
5118 5286 5113 17.86 0 2600.5 17.36 0.508
2716 2464 5822 61.29 0 931.1 25.73 0.343
5822 5166 5950 59.94 0 1984.8 25.86 0.341
5824 5166 5953 59.78 0 1984.8 25.88 0.341
5849 5210 5928 56.13 0 2009.7 25.67 0.344
5855 5210 5934 54.74 0 2023.6 25.52 0.346
6289 5269 6302 35.37 0 2368.6 23.41 0.377
6327 5269 6340 36.28 0 2372.2 23.52 0.375
6390 5275 6397 45.11 0 2313.3 24.36 0.362
6497 5275 6504 49.28 0 2304.0 24.87 0.355
6568 5274 6575 57.77 0 2241.3 25.84 0.341
6600 5274 6607 53.25 0 2295.7 25.35 0.348
6605 5299 6581 57.52 0 2256.8 25.81 0.342
6608 5299 6584 56.96 0 2262.8 25.75 0.342
6796 5279 6797 57.87 0 2318.4 25.85 0.341
6819 5279 6820 58.10 0 2324.1 25.87 0.341
6849 5290 6836 65.13 0 2388.3 25.29 0.349
6876 5290 6863 59.31 0 2340.9 25.90 0.340
6977 5286 6970 61.90 0 2393.5 25.71 0.343
6979 5286 6972 60.86 0 2386.9 25.78 0.342
113
Figure 6-10: Speed - CO2 Emission Rates Relationship
114
6.9 Meta Model for Transportation Emissions ―Micro-TEM‖
6.9.1 Introduction to Meta Models
In most engineering problems, experimentation and/or simulations are needed to
optimize a system or evaluate a design objective as a function of several design variables.
Alternatively, many real world problems require extensive simulation runs to be
explained. As a result, design optimization, design space exploration, or sensitivity
analysis (what-if scenarios) becomes time consuming since they involve hundreds or
even thousands of simulation evaluations. Therefore to resolve this problem,
approximation models, known as ―Meta models‖ or ―response surface models‖ are
constructed that replicate the performance of a simulation model as closely as possible
while being computationally accurate and cheaper to evaluate. A Meta model is an
engineering technique used when an outcome of interest or response is not easily
calculated or directly measured (such as transportation emissions); therefore a substitute
model of this response is developed instead. Meta models are constructed using data-
driven bottom-up approach as was constructed in this research using optimal design along
with micro-simulation. Since the particular internal functioning of simulation models (i.e.
VISSIM, MOVES) is not always explicitly defined and depends on several underlying
models, thus the input-output activities are essential.
115
The developed Meta model was constructed based on modeling the CO2
emissions response from the microscopic simulation to a sufficient number of
intelligently designed data points using (D) and (I) optimal designs. This approach is also
known as black-box modeling or behavioral modeling. The main purpose of the
developed Micro-TEM (Microscopic Transportation Emissions Meta Model) is to serve
as an alternative model for predicting transportation emissions on limited access
highways in lieu of running simulations using a traffic model and integrating the results
in an emissions model. The model accuracy was validated against different applications
and compared with several simulation outputs on the link level as well as the route level
covering all possible combination scenarios as explained in the following section.
6.9.2 Micro-TEM
As mentioned earlier, the main objective of this research was to study the effect of
each of the significant transportation parameter on transportation emissions represented
in CO2 emissions as a case study. The optimal design approach facilitated this process.
Combining the 140 runs from the D-design and I-design on the modeled network with 11
links resulted in 1,540 data points creating the opportunity to model the effect of each of
the studied parameter on CO2 emissions at different settings for the remaining parameters
represented by the Meta model displayed in Figure 6-11 and Figure 6-12. The modeled
116
curves represent the results of the response surface model developed from the custom
design.
The curves on Figure 6-11 show the effect of speed on CO2 emission rates at
different temperature, truck% and grade% levels. As can be seen from the curves, there
are three main conclusions. First, emission rates are the lowest at speeds between 55 and
60 mph as concluded previously. Second, the effect of temperature increases gradually
with the increase in truck and grade %. Third, there is an enormous increase in emission
rates (from 0.5 to 1.7 kg/veh-mile)) due to the effect of grade (0-5)% and truck (0-15)%
amounting to approximately 340%. The practicality of the developed emission rate
versus speed curves lie in the determination of an average speed on a link level or route
level. If an average link speed is determined, emission rates can be predicted, thus total
emissions at specific volume, grade%, truck% and ambient temperature level.
117
Figure 6-11: Speed-CO2 Emission Rates at Different Temp, Truck & Grade Levels
118
Similarly, the polynomial curves on Figure 6-12 show the effect of the traffic
volume on CO2 emission rates at different temperature, truck% and grade% levels.
Although it appears that the speed was not taken into account; when examining the data
points, a V-like shape was observed as shown on Figure 6-13. The polynomial fitted
curves were based on the best fit of the data points by minimizing the sum of squares of
their deviations through the least squares method. However, the effect of the speed was
observed in the ―spectrum‖ created by the V-shaped curve, ranging from speeds of 20
mph to 60 mph with emission rates higher at the 20 mph curve. Furthermore, there was
another observed deviation in the data points closer to the 60 mph curve. These data
points reflect a higher emission rates for speeds higher than 60 mph which matches the
speed curves shown on Figure 6-11. To demonstrate the observed ―Speed Spectrum‖ on
the volume curves, more detailed data points were plotted at different speeds as shown on
Figure 6-14. It was found that the speed spectrum curves follow a power-law function.
The developed traffic volume curves can be used to predict emissions per mile, thus total
emissions based on a link or group of links with a specific volume or flow rate at
different parameter settings.
119
Figure 6-12: Volume-CO2 Emission Rates at Different Temp, Truck & Grade Levels
120
0
500
1000
1500
2000
2500
3000
0 1000 2000 3000 4000 5000 6000 7000 8000
CO2 Em
ission Rate (kg/mile)
Volume (vph)
Figure 6-13: Traffic Volume – CO2 Emission Rates Relationship (0%Trucks- 0%Grade)
Another major finding from this experiment is the speed-density relationship.
Based on the traffic flow theory, the speed-density relationship is known to be linear.
However, the curve fitting shown on Figure 6-15 showed otherwise; a third-order model
with a 94% coefficient of correlation (R2). As stated by Wang et al. (2009): “Speed-
density (or concentration) models in a deterministic sense, whether single or multi-
regimes, have a ‘pairwise’ relationship; that is, given a density there exists a
corresponding speed from a deterministic formula….., There is a distribution of traffic
speeds at a certain density level due to the stochastic nature of traffic flow, this is in
20 mph 60 mph
121
contrast to the ‘pairwise’ pattern from deterministic models”. This statement and the
graph shown proved that the developed “Micro-TEM” model is stochastic and
microscopic in nature. The distribution of speeds around the same density level was
attributed to many factors that correspond to the stochastic feature of freeways and their
associated capacity such as the dynamic traffic flow, random arrival and interaction
between vehicles, in addition to the randomness in the driving behavior. The speed-
density curve is a useful tool to predict density, hence freeway level of service (LOS).
R² = 0.997R² = 0.998
0
500
1000
1500
2000
2500
3000
3500
0 2000 4000 6000 8000
CO2 Em
issions (kg/m)
Flow Rate (vph)
T100, T0, G0, S17‐20
T100, T0, G0, S20‐25
T50, T0, G0, S17‐20
T100, T0, G0, S25‐30
T100, T0, G0, S30‐40
T100, T0, G0, S50‐60
T50, T0, G0, S35‐60
Figure 6-14: Speed Spectrum on Volume-CO2 Emission Rate Curves
122
Figure 6-15: Stochastic Speed – Density Relationship
The developed model (Micro-TEM) can be used to predict emission rates as well as
total emissions on limited access highways at different parameter settings without the
need to run any micro-simulation whether traffic or emission models. Micro-TEM was
validated through different scenarios and applications as will be explained in Chapter 8.
The resulting emissions were compared with MOVES output and the relative error did
not exceed 10% in the worst case scenario. This approach added flexibility to emissions
testing covering all the achievable combinations of instantaneous speeds and
accelerations, which was used to develop emissions for all desired driving patterns.
123
The analysis of the experiment developed a predictive model (Micro-TEM) for CO2
emissions and identified optimal settings of the key factors. Contour plots were generated
for the surface model shown in Figure 6-16. The predictive model consisted of a function
of all the significant main effects and the speed interaction with the truck and grade
factors as well as the quadratic effect of the Volume and the Speed. The predictive model
expression is included in Appendix A. A simple form for the model equation can be
represented as: Emission Rates = a + b (volume2) + c (speed
2) + d (truck%) + e (grade%)
Finally, specific model limitations included the following:
1) Freeways (up to 3 lanes in each direction plus auxillary lanes)
2) Parameter settings are within the studied limits (speed, volume,…etc.)
3) Response variable for CO2 emissions (other pollutants can be included)
4) Running exhaust emissions mode only
Figure 6-16: CO2 Surface Profiler for the Predicted model.
124
7. EMISSION ESTIMATION APPROACHES
7.1 Overview
There are several ways to estimate emissions and transportation agencies and
researchers have a long history of implementing techniques in estimating emissions.
However, precision and accuracy depends mainly on the methodology used. For example,
old traditional methods for creating emission inventories utilized annual average
estimates; others have estimated emissions using one average speed and volume on a
long stretch of roadway. Currently, more accuracy has been established using
microscopic analyses through the reduction of time and distance scales while splitting the
network links into sub-links and utilizing second-by-second operations to calculate
emissions as demonstrated in this research. However, even at this level of detail, there
exist different emission estimation approaches that can be investigated through the
integration of VISSIM and MOVES models. This section explains in greater detail the
main differences between these estimation approaches and provides an emission
sensitivity analysis in calculating CO2, CO, NOx and PM emissions in each approach.
7.2 VISSIM Input/Output Data
The VISSIM model generates a significant amount of output data detailing each
vehicle‘s performance within the network which are critical for calculating air pollutant
125
emissions. These details include second-by-second speed-acceleration profiles, network
characteristics, and other vehicle parameters.
For this study, three types of output data were generated from VISSIM runs to
correspond with vehicle characterization inputs within MOVES. The first output included
link-average speeds during the entire peak hour; the second output was a set of link-
instantaneous speeds but on a second-by-second basis, and the third output included
vehicle trajectory data: length, speed, acceleration, weight, location and grade, on a
second-by-second basis as shown in Table 7-1. All of the inputs required for MOVES
emissions model were generated from VISSIM.
Table 7-1: Excerpt from VISSIM Vehicle Trajectory Data
t Link#
Veh
Length VehNr Weight a V DistX WorldX WorldY Grade
60 1 14.4 215 0.8 0.82 27.42 176 -7816.74 -15425.6 0
60 1 14.9 207 1 -
0.99 28.14 247 -7795.91 -15425.9 0
60 1 13.5 201 1.6 -
0.69 26.43 297 -7779.42 -15426.1 0
60 1 15.1 195 1.8 -
0.93 24.96 350 -7763.16 -15426.4 0
60 1 14.4 190 1.4 -
1.35 23.25 401 -7747.78 -15426.6 0
60 1 14.4 185 1.7 -
1.66 21.2 449 -7733.25 -15426.8 0
60 1 14.4 181 0.8 -
1.39 18.97 499 -7718.97 -15427 0
60 1 15.6 178 1.1 -0.9 16.94 544 -7705.68 -15427.1 0
60 1 15.6 172 1.6 1.39 15.69 580 -7693.38 -15427.3 0
60 1 14.4 164 0.9 6.31 18.42 629 -7679.81 -15427.5 0
60 1 13.5 162 1.2 4.24 23.51 672 -7665 -15427.7 0
60 1 15.6 156 1.2 3.35 26.89 734 -7647.09 -15427.9 0
60 1 15.6 153 1 2.98 29.54 799 -7627.38 -15428.2 0
60 1 13.5 147 1.7 2.97 31.87 863 -7607.06 -15428.5 0
126
The subject test-bed is the previously described 10-mile prototype of I-4 as shown
in Figure 7-1. Traffic composition was set at 60% passenger cars (LDGV), 37%
passenger trucks (LDGT) and 3% heavy-duty diesel trucks (HDDV) as obtained from
Florida Department of Transportation (FDOT) traffic information. The study period
encompassed the eastbound evening peak hour from 5:00 to 6:00 pm which carries more
than 6,000 vehicles per hour. The speed limits on the study corridor over the 10-mile
section during the peak hour range from 30-40 mph as part of a Variable Speed Limit
(VSL) safety program. Therefore, VISSIM input volumes were assigned a speed
distribution based around the posted speed limits. Roadway links were coded with 0%
grade as nominal grade changes exist on the study corridor.
Figure 7-1: Test-bed Prototype of the I-4 Corridor
127
7.3 Moves Project Level Data
As mentioned earlier, MOVES project level database files include meteorology
data, traffic composition & percentage of trucks, length, volume, average speeds & grade,
distribution of vehicles age, operating mode distribution for running emissions, link drive
schedules and fuel information (gasoline, diesel). A summary of MOVES project level
parameters used can be seen in Table 7-2.
Table 7-2: Summary of project level parameters
Location County Orange County, Florida
Calendar Year 2010
Month November
Time 5:00 PM to 6:00 PM (one hour)
Weekday/Weekend Weekday
Temperature 75 F
Humidity 70.0 %
Roadway type Urban Restricted Access – represents freeway
urban road with 3 lanes in each direction
(%)Types of Vehicles(Source type)
60% Passenger cars–LDGV(21), 37%
Passenger trucks–LDGT(31) & 3% Long haul
combination diesel trucks–HDDV(62)
Type of Fuel
Gasoline for passenger cars (LDGV) and
trucks (LDGT); diesel for heavy duty diesel
trucks (HDDV)
Roadway Length (11 links - Total) Approx. 1 mile/link – Total of 10 miles
Link Traffic Volume 4000-6,500 vehicles per hour
Link Truck traffic 3% Heavy Duty Diesel Trucks (HDDV)
Average road grade 0 %
Link Average Speed 20 – 40 miles per hour
Pollutant Process Running Exhaust Emissions
Output CO, NOx and Atmospheric CO2
128
7.4 Vehicle Activity Characterization
7.4.1 Average Speeds, Link Drive Schedules & Operating Modes
Selection of vehicle speeds and volumes on network links is a complex process
due to the fundamental relationship between speed and volume. The recommended
approach for estimating average speeds and volumes is to post-process the output from a
traffic model. Therefore, the simulated vehicle driving cycle output data from VISSIM
was input into the MOVES model based on the above mentioned project-level traffic
conditions to calculate CO, NOx and CO2 emissions. Four approaches were used to
estimate vehicle emissions for the hour. The first was a simple hand calculation that
estimated emissions from total VMT at one average speed for the whole 10-mile stretch
just to illustrate the ―old‖ method of creating a mobile source emission inventory. Three
other estimation approaches were used, all of which used 1-mile sub-sections: average
speeds (AVG), link drive schedules (LDS), and operating mode distributions
(OPMODE). The MOVES operating mode distribution allows one to define the amount
of travel time spent in various operating modes including: braking, idling, coasting, and
cruising/accelerating within various speed ranges and at various ranges of vehicle
specific power (VSP). In all of the model runs, only the ―running exhaust emissions‖
were modeled.
129
Use of the AVG approach forces MOVES to use built-in driving schedules based
on predefined speed bins and an interpolation algorithm to produce a default operating
mode distribution. On the other hand, in the LDS approach, each vehicle, or group of
similarly performing vehicles, is modeled on a second-by-second basis using
instantaneous speeds. However, even with this great amount of activity detail, MOVES
will convert it to an operating mode distribution based on its internal algorithms. In the
third approach (OPMODE), all vehicle activity data from VISSIM are pre-processed to
develop the simulated operating mode distribution on a second-by-second basis, and this
is input directly into MOVES. Thus the main differences among these three approaches
lie in the distinctions among the representations of each operating mode distribution.
7.4.2 Vehicle Specific Power (VSP)
MOVES calculates emissions by calculating a weighted average of emissions by
operating mode. For running exhaust emissions, the operating modes are defined by
Vehicle Specific Power (VSP) from cars or the related concept from trucks, Scaled
Tractive Power (STP). Both VSP and STP are calculated based on a vehicle‘s speed and
acceleration, but they differ in how they are scaled. The VSP, as shown in equation 1, is
used for light duty vehicles (source types 11-32), while the STP, as shown in equation 2,
is used for heavy-duty vehicles (source types 41-62) (USEPA, November 2010).
130
(1)
(2)
Where:
VSP=Vehicle Specific Power (kW/ton)
STP= Scaled Tractive Power (kW/ton)
M = vehicle Mass (metric tons)
A = rolling Term A (kw-s/m) B = rotating Term B (kw-s
2/m
2)
C = aerodynamic drag Term C (kw-s3/m
3)
v = Instantaneous vehicle velocity (m/s)
a = Instantaneous vehicle acceleration (m/s2)
f = fixed mass factor, g = gravitational acceleration (m/s2)
= Road grade (fraction)
Since "running" activity has modes that are distinguished by their VSP and
instantaneous speed, the Operating Mode Distribution Generator (OMDG) classifies
vehicle operating modes into different bins associated with vehicle specific power and
speed, and develops mode distributions based on pre-defined driving schedules. The
MOVES emission rates are a direct function of VSP, a measure that has been shown to
have a better correlation with emissions than average vehicle speeds (USEPA, 2002), and
users can input locally-specific VSP distributions based on the exclusive characteristics
of the modeled system. VSP represents the power demand placed on a vehicle when the
vehicle operates in various modes and at various speeds. In other words, the operating
mode is a measure of the state of the vehicle‘s engine at that particular moment. This
function produces operating mode fractions for each bin, which are used as one of several
131
inputs for computing base emission rates. The output of the simulation run is a vehicle
trajectory file that, for every second of the simulation, indicates the instantaneous speed
and acceleration of every vehicle in the network, an excerpt is shown in Table 7-1. Since
both speed and acceleration are available in the micro-simulation output for every vehicle
for every second of simulation, MOVES operating mode distributions based on VSP
were computed only in the OPMODE vehicle activity characterization approach. This is
thought to be a more accurate way of capturing driving cycle patterns when literally
thousands of vehicles have their trajectories traced, as in simulation. Since the grade was
set at 0 percent in the simulation, the term for it falls out of the equation and is not used.
7.5 Emissions Results and Analysis
Table 7-3 provides a comparison of the results for CO, NOx and CO2 emissions
(kg) when the MOVES analysis was conducted using the three simulation approaches.
For the same 10-mile stretch of I-4, those three approaches resulted in CO2 emission
estimates ranging from more than 19,000 kg to almost 26,000 kg. By comparison, the
hand calculation method gave 29,100 kg. In general, the AVG approach estimated higher
total emissions than the OPMODE approach while the LDS approach estimated lower
total emissions as shown on Figure 7-2. If indeed the OPMODE approach is the most
accurate, the AVG approach resulted in overestimation of emissions while the LDS
approach resulted in underestimation of emissions.
132
Table 7-3: Emissions by pollutant, source type, link & vehicle activity characterization
Source Type LNK CO Emissions (kg/hr) NOx Emissions (kg/hr) CO2 Emissions (kg/hr)
AVG LDS OPMODE AVG LDS OPMODE AVG LDS OPMODE
Pas
senger
Car
s-G
aso
line
(LD
GV
)
1 4.99 3.81 6.45 0.50 0.24 0.67 771 605 863
2 9.41 7.00 11.44 1.00 0.56 1.15 1425 1094 1500
3 9.24 7.22 11.63 0.97 0.56 1.17 1408 1106 1508
4 8.33 6.66 8.49 0.97 0.59 0.85 1266 997 1167
5 7.35 6.08 6.94 0.88 0.57 0.69 1126 905 987
6 7.85 6.04 7.89 0.86 0.53 0.81 1178 904 1124
7 8.01 6.15 8.27 0.86 0.49 0.87 1213 943 1189
8 8.10 6.26 8.39 0.86 0.49 0.87 1231 967 1203
9 6.41 5.16 6.32 0.75 0.47 0.63 977 779 884
10 6.41 5.27 6.04 0.78 0.48 0.60 983 778 856
11 2.98 2.44 2.86 0.36 0.23 0.29 457 363 401
Total (LDGV) 79.1 62.09 84.73 8.79 5.21 8.60 12035 9442 11682
Pas
senger
Tru
cks
- G
asoli
ne
(LD
GT
)
1 5.19 3.39 6.59 0.64 0.32 0.79 624 469 688
2 9.98 7.79 11.60 1.31 0.88 1.39 1163 880 1189
3 9.76 7.50 11.75 1.26 0.82 1.40 1146 871 1199
4 9.15 7.03 8.63 1.30 0.82 1.08 1046 792 920
5 8.20 6.32 7.10 1.20 0.76 0.91 936 714 775
6 8.40 6.37 8.11 1.13 0.74 1.04 964 718 889
7 8.50 6.23 8.51 1.12 0.69 1.10 990 742 943
8 8.56 6.28 8.63 1.11 0.69 1.09 1003 758 956
9 7.08 5.45 6.44 1.02 0.65 0.82 809 617 696
10 7.17 5.46 6.18 1.05 0.64 0.79 818 614 672
11 3.33 2.55 2.92 0.49 0.31 0.37 380 288 315
Total (LDGT) 85.3 64.37 86.46 11.64 7.32 10.78 9879 7463 9242
Hea
vy D
uty
Die
sel
Tru
cks
(HD
DT
)
1 0.30 0.22 0.32 1.08 0.69 1.05 248 140 246
2 0.55 0.48 0.51 2.09 1.41 1.65 487 299 378
3 0.55 0.45 0.53 2.03 1.39 1.72 471 292 400
4 0.45 0.41 0.40 1.75 1.31 1.25 400 283 280
5 0.39 0.36 0.34 1.54 1.19 1.07 350 259 239
6 0.45 0.37 0.40 1.79 1.18 1.28 418 254 289
7 0.47 0.37 0.41 1.79 1.18 1.33 415 250 298
8 0.48 0.38 0.45 1.79 1.20 1.49 415 252 338
9 0.34 0.31 0.30 1.33 1.01 0.98 303 219 218
10 0.34 0.31 0.30 1.34 1.03 0.95 306 222 210
11 0.16 0.15 0.13 0.62 0.48 0.41 142 103 93 Total (HDDV) 4.48 3.81 4.08 17.16 12.08 13.19 3952 2573 2987
Total Emissions 168.9 130.27 175.27 37.59 24.60 32.57 25866 19478 23912
133
(a) CO
(b) NOx
(c) CO2
Figure 7-2: Total Emissions by Vehicle Type & Estimation Approach
134
Figure 7-3 (a-c) shows in greater detail the differences between the three
approaches, link by link, and show the greater variability in emissions from the
OPMODE approach when compared with the AVG and the LDS approaches. This is
attributed to the fact that average speeds generally omit detailed vehicle activity such as
acceleration and deceleration. Furthermore, this variability increases at certain locations
(links 1-3 and 6-8) and decreases at other locations (links 4-5 and 9-10). When examining
the network, it is found that links 1 and 2 are considered as loading points on the
network from the mainline as well as the on-ramp, while link 3 is a discharging location
(off-ramp) which creates a weaving area for vehicles trying to enter the network and
others leaving. Weaving areas cause excessive acceleration and deceleration resulting in
an increase in braking, deceleration, idling, and acceleration. Large fractions of vehicles
spend a substantial amount of time operating in these modes of stop and go operation
which are characterized by relatively high vehicle specific power and low speeds. The
same pattern was seen for links (6, 7, and 8). However, emissions are lower on links 6-8
due to the relatively longer weaving distance between the on-ramp and the off-ramp
resulting in a relatively smoother operation in addition to lower volumes compared to the
volume and weaving distance on links 1-3. Figure 7-4 (a-c) maps the operating mode
distribution in details for links 1, 2 and 10 for comparison purposes. The remaining links
are included in Appendix C.
135
(a)
(b)
136
(c)
Figure 7-3: Emissions Variation on Corridor Links for PC by Estimation Approach
137
(a)
(b)
138
(c)
Figure 7-4: Link Operating Mode Distribution by Vehicle Type on Selected Links
Furthermore, the results displayed in Table 7-3 enable us to evaluate the behavior
of the studied pollutants with respect to each other. According to Figure 7-2b, there is an
apparent increase in NOx emissions in all estimation approaches when compared to the
fleet composition; contrary to CO and CO2. The 37% passenger gasoline trucks generated
higher emissions than the 60% passenger gasoline cars while only 3% heavy duty diesel
trucks generated higher emissions than the passenger cars (60%) and the passenger trucks
(37%). This is attributed to a combination of increased engine loading in heavier vehicles
139
(and thus higher combustion temperature) and the fact that diesel engines produce much
more NOx than gasoline engines.
Table 7-4 addresses the effect of VMT along with the operating mode distribution
(speeds and accelerations) on the CO, NOx, and CO2 emissions on selected corridor
links. These links were selected for comparison purposes. As shown in Table 7-4,
emission rates (emissions per vehicle-mile) are the highest on link 1 when compared to
the rest of the network links although Figure 7-3(a), (b), (c) seem to show otherwise. The
difference lies in the distance traveled (link 1 was only one-half mile long). All
parameters should have the same scale for a fair comparison between them. By
normalizing the emissions to vehicle-miles, it was found that link 1 has the highest
emission rate (e.g. 583 grams/veh-mile CO2). Furthermore, Link 1 has a greater fraction
of the passenger car activity (about 30%) in the braking, idling and low speed coasting
operating modes (0, 1 and 11), as well as 20% in cruise/acceleration modes (12-16) at
lower speeds (1-25 mph) as shown in Figure 7-4(a). Link 2 shows nearly similar
operating mode distribution patterns but with lower percentages than Link 1, especially in
operating mode 11 (coasting), and had a lower emission rate (486 grams/veh-mile CO2).
It should be noted that link 2 has 152 vehicles more than link 1. However, emission rates
are lower which is attributed to improved traffic operations compared to link 1. Link 10
has the smallest emission rates on the corridor links (362 grams/veh-mile CO2). A greater
fraction of the vehicle activity is in operating modes 21-25 (moderate speed coasting and
140
cruise/acceleration); here there are relatively higher speeds (25-50 mph) with almost 0%
idling or braking and 11% coasting. It is concluded that increased braking, idling and
coasting at lower speeds, along with the consequent re-accelerating, described as
acceleration events, have a significant impact on pollutant emission rates.
Table 7-4: Link emissions per vehicle-mile by source type
Link# Source
Type
Emissions (kg)
Link
Volume
Link
Avg.
Speed
(mph)
Link
Dist.
(miles)
Emissions (Kg/Veh-Mile)
CO NOx CO2
CO NOx CO2
1
LDGV 6.45 0.67 863
6159 22.09 0.50
0.00209 0.00022 0.28
LDGT 6.59 0.79 688 0.00214 0.00026 0.22
HDDV 0.32 1.05 246 0.00010 0.00034 0.08
Total 13.36 2.52 1797 0.00434 0.00082 0.58
2
LDGV 11.44 1.15 1500
6311 27.31 1.001
0.00181 0.00018 0.24
LDGT 11.60 1.39 1189 0.00184 0.00022 0.18
HDDV 0.51 1.65 378 0.00008 0.00026 0.06
Total 23.55 4.18 3067 0.00373 0.00066 0.48
10
LDGV 6.04 0.60 856
4903 36.86 0.978
0.00126 0.00012 0.18
LDGT 6.18 0.79 672 0.00129 0.00016 0.14
HDDV 0.30 0.95 210 0.00006 0.00020 0.04
Total 12.52 2.34 1738 0.00261 0.00049 0.36
141
7.6 Discussion
This section presented a detailed examination of three different vehicle activity
characterization approaches to capture the environmental impacts of vehicular travel on a
limited access urban highway corridor. The VISSIM/MOVES integration using VIMIS
1.0 was used to estimate emissions derived from three approaches characterizing vehicle
activity, namely average speeds (AVG), link drive schedules (LDS), and operating mode
distribution (OPMODE). The OPMODE approach covered all the simulated
combinations of instantaneous speeds and accelerations, and was used to develop detailed
emissions for all desired driving patterns.
The results demonstrated that obtaining second-by-second vehicle operations from
a traffic simulation model are essential to achieve the most accurate operating mode
distributions and presumably the most accurate emissions estimates. Specifically,
emission rates are found to be highly sensitive to the frequent acceleration events that
occur at lower speeds, that is, frequent braking/coasting, idling (operating mode bins 0,
11 and 1, respectively) and re-accelerating. In the lower speed range (< 25 mph), the
emission rates for VSP bins up to 12 kW/ton are actually higher than the emission rates
from the same VSP bins in the higher speed range (> 25 mph). In addition, results from
VISSIM show that there were more frequent speed changes in the lower speed range,
perhaps due to increased weaving and more aggressive driving. These two facts likely
142
accounted for the higher emissions on links 1-3 compared with emissions on links 4, 5,
and 10 regardless of the amount of vehicle miles traveled (VMT). Moreover, the use of
average speeds often conceals the effects of acceleration/deceleration on emissions.
Using AVG and LDS approaches resulted in overestimation or underestimation of
emissions, respectively, when compared to the OPMODE approach.
In addition, the results of this section addressed previous conclusions (Int. Panis et
al., 2006) regarding evaluating speed management policies in Europe through modeling
instantaneous traffic emissions and the influence of using an average speed approach.
They concluded that active speed management has no significant impact on pollutant
emissions. They also concluded that ―the analysis of the environmental impacts of any
traffic management and control policies is a complex issue and requires detailed analysis
of not only their impact on average speeds but also on other aspects of vehicle operation
such as acceleration and deceleration‖ (Int. Panis et al., (2006) .
This study limited the pollutants to only CO2, CO, and NOx; however, methods have
been demonstrated that can be used for other mobile-source pollutants. Furthermore, CO
and PM dispersion modeling analyses, which are often required for roadway projects, can
use the resulting spatially-determinate EFs in roadway dispersion models such as
CAL3QHC or AERMOD to predict concentrations of various pollutants near roadways,
or in gridded ozone modeling.
143
8. MODEL APPLICATIONS
8.1 Overview
The main objective of the model applications in this chapter is to study congestion
mitigation strategies on the I-4 corridor and evaluate the environmental impacts in each
scenario in terms of vehicular emissions and at the same time validate the developed
model ―Micro-TEM‖. Since the VISSIM model was properly calibrated and validated, it
was ready to perform and evaluate a range of operational strategies through various
scenarios and analysis of the model outputs.
Three main applications were proposed in the research methodology; Variable
Speed Limits (VSL), Managed Lanes (ML) and Restricted Truck Lanes (RTL). However,
the VSL application was conducted and evaluated as part of the experimental design
approach since it was already implemented on the I-4 corridor during the peak hour.
Therefore, the remaining strategies would include the ML and RTL as explained in the
following sections.
8.2 Managed Lanes (ML)
Roadway agencies face several challenges to expand freeway capacity due to the
increase in construction costs, restricted right-of-way as well as environmental
144
regulations. Thus, transportation professionals are seeking solutions for managing the
demand on existing limited access facilities efficiently and providing options for
travelers. The concept of MLs is an increasingly accepted countermeasure that aims at
efficiently utilizing the existing limited access facilities by restricting access to one or
more lanes to certain vehicle types on a facility that is parallel to existing general use
lanes (GUL). ML is also considered one of the congestion pricing applications, also
called ―Value Pricing,‖ ―Variable Pricing,‖ or ―Peak Hour Pricing‖, which is the practice
of charging motorists more to use a roadway, bridge, tunnel or parking spot during
periods of heaviest use. The term, congestion pricing, comes into play at places where a
charge, fee or toll is applied with the intent of reducing car trips or encouraging shorter
parking stays.
Johnston et al. (1996) used travel demand simulations to demonstrate that new
HOV lanes may increase travel (vehicle-miles) and increase emissions when compared to
transit alternatives. They recommended better travel demand modeling methods for such
evaluation. Sinprasertkool (2010) concluded that higher toll rates tend to generate higher
toll revenues, reduce overall CO and NOx emissions, and shift demand to general
purpose lanes. However, HOV privileged treatments (HOV2 paying 50% and free service
for HOV3+) at any given toll level tend to reduce toll revenue, have no impact or reduce
system performance on managed lanes, and increase CO and NOx emissions, when
compared to SOV. Kall et al. (2009) studied air quality impacts on I-85 managed lanes in
145
Atlanta. They concluded that emissions results were mixed, with small estimated
increases for CO, NOx, PM10 and small decreases for HCs. Higher concentrations were
found in most of the study area for the modeled pollutants (CO, NOx, and benzene), with
the largest increase near the corridor. Overall, changes in emissions were small indicating
little impacts of the managed lane project on air quality.
Quantitative research on the air quality benefits of managed lane facilities, also
known as ―High Occupancy Toll (HOT) lanes‖ has been limited and inconclusive. With
growing interest in mitigating climate change, primarily greenhouse gas emissions in
addition to other pollutants from transportation sources and in strategies to reduce
congestion, research is needed to examine the emissions benefits of ML treatments. In
this section, the air quality impacts of ML and GUL were examined and compared with
the existing conditions (EX) on the calibrated I-4 corridor using VIMIS 1.0.
The future scenario for the I-4 managed lanes was obtained from the FDOT. The
FDOT plans to implement two express toll lanes in each direction (Managed Lanes) with
variable tolls concept during the congested travel periods. Detailed information regarding
the I-4 ML project as well as the dynamic algorithm for calculating tolls on the ML are
not released to the public yet and therefore, only the released information on the I-4
website can be disclosed at this moment (www.moving-4-ward.com). The new concept as
well as the modeled network including the dynamic assignment model on the basis which
146
they calculated the variable toll system was provided by the FDOT project manager to
ensure the same results replicated. It should be noted that the future scenario also
includes major improvements along the general use lanes over the total project length.
Ultimate improvements along the corridor totaled 20 miles in length starting from west of
Kirkman Road to east of SR 434. It includes the reconstruction of 15 interchanges, 60
new bridges; improving overall safety with the goal to increase the design speed to 60
mph. Access to and from the tolled ML will be limited since the intention is to increase
speed over longer distances and reduce disruption to the traffic flow. The GUL
anticipated opening year 2020 traffic demand is 8,000 vph and expected to reach 9,700
by 2030 (3,250 vphpl), while the opening year traffic demand for the ML is expected to
be 2,500 vph and anticipated to reach 3,000 vph by the year 2030 (1,500 vphpl).
8.3 Restricted Truck Lanes (RTL)
Restricted Truck Lanes (RTLs) are lanes designated only for the use of trucks. The
main purpose of RTL is to separate the heavy truck traffic from other mixed-flow traffic
in order to improve operations and safety. Although the concept of RTL is not new, very
few truck-only lanes exist in the US. The majority of the states that have implemented
RTL restrict trucks to certain lanes. However, all other vehicles are still allowed to use all
lanes including the truck lanes. Most studies in the literature revealed that exclusive truck
lanes are the most plausible solution for congested highways based on specific factors
147
such as truck volumes exceeding certain percentages of the vehicle mix during off peak
and peak periods.
Heavy truck traffic often results in significant congestion, safety issues, emissions,
and noise impacts. These impacts not only affect lifestyle, but also economy and the
quality of the environment especially on key commercial corridors such as the I-4.
Segregation of truck traffic from passenger traffic has the potential to mitigate traffic
operations by enhancing speeds, thus improving air quality as well as improving safety.
Rakha et al. (2005) evaluated alternative truck management strategies along I-81, the
results demonstrated that separation of heavy-duty trucks from the regular traffic
physically offers the maximum benefits. Also, restricting trucks from the leftmost lane
offers the second-highest benefits in terms of efficiency, energy, and environmental
impacts. Samba et al. (2011) also evaluated large-truck transportation alternatives with
safety, mobility, energy, and emissions analysis using TRANSIMS micro-simulation.
They concluded that left-lane restrictions were the most statistically significant and
beneficial treatment strategy; decreasing the likelihood of a rear-end crash by about 2%
for the off-peak and peak hours. Supplementary analysis with the emissions model
indicated that left-lane restrictions also had marginally positive effects on fuel
consumption and emissions.
148
Since the FDOT is planning on prohibiting heavy duty trucks from using the I-4
corridor completely especially after the implementation of ML, the model application
presented in this section is hypothetical. The main idea is to study the environmental
impacts of RTL implementation on the I-4 corridor during the peak hour and evaluate the
potential benefits of mitigation strategies in terms of vehicular emissions. Although, the
truck percentage on I-4 did not exceed 5% during the peak hour, environmental benefits
still need to be studied and evaluated in light of the literature findings.
8.4 Evaluation of Scenarios
As mentioned earlier, the I-4 VISSIM model for the ML ultimate project obtained
from the FDOT was run and the results of both the managed lanes (ML) and the general
use lanes (GUL) were compared with the existing conditions (EX) for CO, NOx, CO2 as
well as PM10 and PM2.5 emissions. In addition, the restricted truck lane (RTL)
hypothetical scenario on I-4 was also compared with the existing conditions and
assuming an additional lane for trucks, thus having 0% trucks on the mainline while 5%
of the total I-4 vehicular traffic is assumed as trucks on the truck only lane. The analysis
was conducted for the peak hour (5-6) pm utilizing the OPMODE approach and the
VISSIM/MOVES integration as explained in Chapter 7. Traffic composition in all
scenarios included 60% passenger cars (PC), 37% passenger trucks (PT) and 3% heavy
duty diesel trucks (HDDT) except in the RTL scenario, HDDT were assumed as 5%
149
(about 425 vph) as more truck traffic would be induced and therefore PT were decreased
from 37% to 35%. The assumption was based on the FDOT AADT of 200,000 vpd and
using the traffic characteristics of the I-4 corridor (k=8%, D=53%). It should be noted
that MOVES project level data were the same as in Table 6-3 except for 3 inputs. The
temperature was set at 85F for the month of June and calendar year 2011 since the I-4
traffic counts and calibrated data was for that date. Emissions analysis was conducted for
total emissions as well as for each vehicle type as shown in the following figures and
table. Furthermore, emission rates on the I-4 corridor were calculated in each scenario
and compared with the base scenario (EX) to determine the effectiveness of the
mitigation application.
As shown in Table 8-1 and illustrated in Figure 8-1(a), (b), (c), overall total
emissions were reduced in all scenarios and for all pollutants when compared with the
EX scenario except for the CO emissions in the GUL scenario and slightly in the RTL
scenario.
150
Table 8-1: Pollutant Emissions Comparison by Scenario
Pollutant Scenario
PC-Gas
(kg)
PT-Gas
(kg)
HDDT
(kg)
Total
(kg)
CO
EX 171.78 144.5 4.39 320.67
GUL 249.05 240.13 3.28 492.46
ML 30.81 35.68 0.85 67.34
RTL 184.99 143.53 0.38 328.90
NOx
EX 13.93 14.17 13.05 41.15
GUL 13.32 14.7 11.8 39.82
ML 2.81 2.78 3.73 9.32
RTL 15.84 14.16 1.57 31.57
CO2
EX 16,757 12,048 4,136 32,941
GUL 11,085 8,932 3,354 23,371
ML 3,548 2,956 1,061 7,566
RTL 13,864 10,505 472 24,840
PM10
EX 0.28 0.22 0.80 1.30
GUL 0.11 0.09 0.57 0.77
ML 0.04 0.04 0.13 0.21
RTL 0.14 0.104 0.06 0.30
PM2.5
EX 0.25 0.20 0.78 1.23
GUL 0.11 0.09 0.56 0.76
ML 0.04 0.03 0.13 0.20
RTL 0.13 0.096 0.06 0.29
151
(a) CO2
(b) CO & NOx
152
(c) PM10 & PM2.5
Figure 8-1: Pollutant Emissions Comparisons by Scenario
153
When examining emissions in each scenario by vehicle type as shown in Figure
8-2(a) through (e), we can see that total emissions were reduced for all pollutants for all
vehicle types in all scenarios except for the CO and NOx emissions especially in the
GUL and RTL scenarios. This was attributed to two main reasons. First, the behavior of
the pollutant is different with each vehicle type and second, more induced demand due to
improved operations or sometimes known as latent demand that was restrained due to
capacity restrictions. Recall that, CO results from the vehicle‘s incomplete combustion of
fuels. Gasoline engines emit higher amounts of CO than diesel engines, due to their lower
combustion temperature compared to diesel. Furthermore, CO increases at very high
speeds which is also attributed to relatively cooler engine operations. On the other hand,
NOx is totally opposite in performance to CO. NOx are mainly created during fuel
combustion where a small amount of the nitrogen in the air along with nitrogen
compounds from the vehicle fuels is oxidized at high temperatures. Diesel engines
generally produce greater amounts of NOx than gasoline engines due to their higher
combustion temperatures which can be observed in the amount of NOx emissions
generated from only 3% of trucks (almost equal to the emissions released from 60% PC
or 37% PT). Conversely, maximum benefits were observed in the ML scenario for
gasoline vehicles and in the RTL scenario for diesel trucks.
154
(a) CO2
(b) CO
155
(c) NOx
(d) PM10
156
(e) PM2.5
Figure 8-2: Emissions Scenario Comparison by Vehicle Type
Network measures of effectiveness (MOEs) such as overall average speed, total
VMT including emission rates were also calculated and compared over the 10-mile
section which addressed the effect of vehicle miles traveled (VMT) as well as the induced
demands. The results shown on Table 8-2 indicated that although the VMT increased in
all the mitigation scenarios, emission rate reductions were achieved in all scenarios when
compared to the EX scenario except for the CO in the GUL scenario as explained
previously.
157
Table 8-2: MOE Scenario Comparisons
Scenario MOE CO NOx CO2 PM10 PM2.5
EX
Overall Avg Speed (mph) 16
Total VMT (Veh-miles) 45,596
Total Emissions (kg) 320.7 41.15 32,941 1.30 1.23
Emission Rates(kg/vh-ml) 0.0070 0.00090 0.72 2.9E-05 2.7E-05
GUL
Overall Avg Speed (mph) 38
Total VMT (Veh-miles) 46,134
Total Emissions (kg) 492.5 39.89 23,371 0.77 0.76
Emission Rates(kg/vh-ml) 0.0107 0.00086 0.506 1.7E-05 1.6E-05
ML
Overall Avg Speed (mph) 59
Total VMT (Veh-miles) 18,180
Total Emissions (kg) 67.3 9.32 7,566 0.21 0.20
Emission Rates(kg/vh-ml) 0.0037 0.00051 0.416 1.2E-05 1.1E-05
RTL
Overall Avg Speed (mph) 21 (EX) & 57 (RTL)
Total VMT (Veh-miles) 52,155
Total Emissions (kg) 328.9 31.57 24,840 0.304 0.286
Emission Rates(kg/vh-ml) 0.0063 0.00061 0.476 5.8E-06 5.5E-06
Since the ML/GUL network varied in number of links and link definition
compared to the EX network as it was developed by FDOT, emission rates were
compared on a link by link basis for the RTL and EX scenarios as shown in Figure 8-3.
As illustrated, emission rate reduction was achieved on a link by link basis, specifically
around the SR 408 on and off ramps that accounted for the highest emissions as well as
emission rates. Micro-TEM model validation was conducted through the comparison of
emission rates and speeds from the developed speed curves on Figure 6-11 using the red
curve with Temp 85F, Trucks at 3% and Grade 0%.
158
Figure 8-3: Link Emission Rate Comparison – RTL vs. EX Scenarios
159
9. CONCLUSIONS AND RECOMMENDATIONS
This research presented a detailed microscopic examination of several key traffic-
related parameters that contribute to the increase of transportation emissions, specifically
traffic volume, speed, truck percent, road grade and temperature on a limited access
urban highway corridor in Orlando, Florida. The corridor was modeled using VISSIM
coupled with the state-of-the-art EPA‘s mobile source emissions model MOVES2010a.
The analysis was conducted using an advanced experimental custom design technique;
D-Optimality and I-Optimality criteria, to identify active factors and to ensure precision
in estimating the regression coefficients as well as the response variable. The analysis of
the experiment resulted in 140 runs (70 for each design criterion). The VISSIM/MOVES
integration was used to run the multilevel factorial experiment based on MOVES project-
level constraints. The same temporal resolution and level of detail in VISSIM and
MOVES supported the integration of both models. The VISSIM/MOVES integration
resulted in the development of VIMIS 1.0 (VISSIM MOVES Integration Software) to
facilitate the design of experiment process.
The analysis of the experiment identified the optimal settings of the key factors
and resulted in the development of Micro-TEM (Microscopic Transportation Emission
Meta-Model). The predictive model consisted of a function of most of the estimated main
effects in that order (Volume, Truck%, %Grade, Speed) and the interaction between the
160
truck and grade factors as well as the quadratic effect of the (Volume) and the (Speed)
leaving out the (Temperature) factor as insignificant. Furthermore, significant emission
rate reductions were observed on the modeled corridor especially for speeds between 55
and 60 mph while maintaining up to 80% and 90% of the freeway‘s capacity. However,
vehicle activity characterization in terms of speed was shown to have a significant impact
on the emission estimation approach.
Three different vehicle activity characterization approaches, namely average
speeds (AVG), link drive schedules (LDS), and operating mode distribution (OPMODE),
were examined to capture the environmental impacts of vehicular travel on the limited
access urban highway corridor. VISSIM outputs (link volumes, speeds,
acceleration/deceleration profiles) within each specified link in the network were
combined with the MOVES model which used Vehicle Specific Power (VSP) and
instantaneous speeds to generate emission rates on a second-by-second basis. The
OPMODE approach covered all the simulated combinations of instantaneous speeds and
accelerations, and was used to develop accurate emissions for all desired driving patterns.
The results demonstrated that obtaining second-by-second vehicle operations
from a traffic simulation model are essential to achieve the most accurate operating mode
distributions and presumably the most accurate emissions estimates. Specifically,
emission rates are found to be highly sensitive to the frequent acceleration events that
161
occur at lower speeds, that is, frequent braking/coasting, idling (operating mode bins 0,
11 and 1, respectively) and re-accelerating. Emissions at any given moment (speed) on a
link appeared to be influenced by the power the vehicle used in getting to that speed from
previous speed, expressed in acceleration rates. In the lower speed range (< 25 mph), the
emission rates for VSP bins up to 12 KW/ton are actually higher than the emission rates
from the same VSP bins in the higher speed range (> 25 mph). In addition, results from
VISSIM showed that there were more frequent speed changes in the lower speed range,
perhaps due to increased weaving and more aggressive driving during the peak period.
These two facts likely accounted for the higher emissions on specific links when
compared with emissions on other links. Moreover, the use of average speeds often
conceals and reduces the effects of acceleration/deceleration on emissions. Using AVG
and LDS approaches resulted in overestimation or underestimation of emissions,
respectively, when compared to the OPMODE approach.
In addition, the results of this study addressed previous conclusions regarding
evaluating speed management policies in Europe through modeling instantaneous traffic
emissions and the influence of using an average speed approach. The results of this
research demonstrated that active speed management does have a significant impact on
pollutant emissions provided that detailed and microscopic analysis of vehicle operation
of acceleration and deceleration is achieved.
162
The developed Micro-TEM model has the capability of predicting CO2 emissions
on limited access highways at different settings and levels of the studied factors without
the need to run any micro-simulation analysis whether for traffic or emissions modeling
or any further coding of freeways. The developed Meta curves determine emission rates
in terms of speeds and volumes as well as density which proved that ―Micro-TEM‖
model is stochastic and microscopic in nature. The distribution of speeds around the same
density level was attributed to many factors that correspond to the stochastic feature of
freeways and their associated capacity such as the dynamic traffic flow, random arrival
and interaction between vehicles, in addition to the randomness in the driving behavior.
The speed-density curve is not necessarily linear and is a useful tool to predict density at
a given speed, hence freeway level of service (LOS).
Micro-TEM experiment was conducted for CO2 emissions; however, methods
have been demonstrated that can be used for other mobile-source pollutants. Microscopic
traffic simulation models like VISSIM can produce second-by-second vehicle operating
mode data, which can be used directly in MOVES to obtain more accurate emissions.
Furthermore, CO, NOx and PM dispersion modeling analyses, which are often required
for roadway projects, can use the resulting spatially-determinate EFs in roadway
dispersion models such as CAL3QHC or AERMOD to predict concentrations of various
pollutants near roadways, or in gridded ozone modeling.
163
Additionally, model applications and mitigation scenarios were examined on the
modeled corridor. Mitigation scenarios included the future implementation of managed
lanes (ML) along with the general use lanes (GUL) on the I-4 corridor, the currently
implemented variable speed limits (VSL) scenario as well as a hypothetical restricted
truck lane (RTL) scenario. Results of the mitigation scenarios showed an overall speed
improvement on the corridor which resulted in reduction in emissions as well as emission
rates in all scenarios when compared to the existing condition (EX) scenario and
specifically on link by link basis for the RTL scenario. Pollutant analysis also included
CO, NOx, CO2, PM10 and PM2.5. Each pollutant showed different performance in
relation to the studied vehicle types of passenger cars (PC), passenger trucks (PT) and
heavy duty diesel trucks (HDDT).
It is recommended that future research focus on improving the developed Micro-
TEM model to include arterials with signalized intersections as well as other emission
processes such as extended idling, crankcase and start exhausts along with other criteria
pollutants that were not studied in this research.
164
APPENDIX A
CUSTOM DESIGN
165
Table A-1: D-Design
Run# Design Volume
Actual Volume
Design Speed
Actual Speed Truck% Grade% Temp CO2 (kg)
1 7000 3850 20 18 0.15 0.05 50 55782.16
2 6000 5500 70 45 0.15 0 100 39457.53
3 3500 3500 70 60 0 0.04 60 20821.53
4 7000 5500 70 40 0.15 0 50 37000.29
5 7000 6500 70 55 0 0 50 22473.73
6 2000 2000 20 20 0.15 0.05 100 30403.75
7 3000 3000 30 30 0.105 0.05 60 32814.88
8 6000 3850 20 18 0.105 0.015 50 33730.21
9 7000 4000 20 18 0 0 50 20094.56
10 3000 3000 30 30 0.03 0 50 12912.33
11 6000 3850 20 18 0.15 0.05 90 63402.54
12 7000 6000 70 45 0.12 0 100 38744.31
13 2000 2000 20 20 0 0.05 50 13425.63
14 7000 3850 20 18 0.15 0 50 31985.89
15 2000 2200 60 60 0.03 0.05 100 18737.38
16 3500 3000 20 18 0.15 0 85 26823.73
17 2000 2000 20 20 0.15 0 50 15111.59
18 2000 2000 55 58 0.105 0 100 12526.15
19 7000 4800 70 27 0.15 0.05 50 65772.52
20 7000 3850 20 18 0.15 0 100 37949.28
21 7000 3850 20 18 0.15 0.05 100 64098.01
22 7000 6250 70 50 0 0.05 50 37791.72
23 2000 2250 55 60 0 0.015 50 9244.973
24 4500 4250 45 40 0.075 0.025 75 32631.13
25 2000 2250 70 70 0 0 50 8232.48
26 3500 3400 60 35 0.12 0.05 90 46767.9
27 3000 3200 70 70 0 0.01 100 15106.19
28 2000 2250 70 70 0 0.05 100 16017.43
29 2000 1850 20 20 0 0.05 100 15067.64
30 7000 4000 20 18 0 0.05 100 32827.16
31 2000 2100 30 30 0 0.04 90 13493.57
32 4500 4100 45 40 0.075 0.025 75 31826.36
33 2000 2200 70 70 0.045 0 65 9574.956
34 2000 2300 70 70 0 0.05 50 14634.51
35 2000 2000 20 20 0 0 100 10928.16
36 2000 2250 70 70 0 0 100 9273.851
166
Run# Design Volume
Actual Volume
Design Speed
Actual Speed Truck% Grade% Temp CO2 (kg)
37 2000 1850 20 20 0.15 0.04 100 28727.74
38 7000 6350 70 50 0 0 100 26339.51
39 7000 5850 70 35 0.045 0.035 50 41164.03
40 2000 2200 70 70 0.15 0 100 15332.49
41 7000 4000 20 18 0 0.05 50 28340.24
42 2000 1800 20 20 0 0 50 8852.758
43 7000 6300 60 55 0.03 0 85 27310.65
44 4500 4200 45 38 0.075 0.025 75 32307.28
45 6000 5600 55 50 0 0.01 60 22215.33
46 5500 5000 70 50 0.12 0 50 28364.59
47 2000 2200 70 55 0.15 0.05 50 29230.49
48 6000 4800 30 27 0.03 0.05 50 38027.24
49 2000 2100 70 50 0.15 0.05 100 31606.53
50 7000 6300 70 50 0 0.05 100 43567.72
51 2000 2000 20 20 0.15 0 100 18284.89
52 2000 1850 20 20 0.15 0.05 50 27431.67
53 7000 5000 35 28 0.15 0.035 100 66061.05
54 7000 4000 20 18 0 0 100 24224.85
55 7000 3850 20 18 0.105 0.04 65 43808.92
56 7000 5750 35 35 0.15 0 60 36297.8
57 2000 2300 70 70 0.15 0.01 65 17457.16
58 6000 5000 30 27 0 0 100 24138.52
59 3500 3300 55 40 0.15 0.035 50 38296.27
60 2000 2000 30 30 0.12 0.01 90 17574.25
61 6000 5300 70 40 0.03 0.04 90 41701.45
62 7000 4600 70 25 0.15 0.05 100 73296.36
63 6000 4600 70 27 0.15 0.05 60 64363.54
64 4500 4200 45 40 0.075 0.025 75 31791.52
65 4500 4200 45 40 0.075 0.025 75 32045.03
66 7000 4000 20 18 0 0.05 100 32781.26
67 2000 2200 70 70 0.15 0 50 13408.4
68 7000 5700 70 40 0.15 0.01 85 47479.74
69 4500 4200 45 40 0.075 0.025 75 31762.19
70 3500 3000 20 18 0.045 0.015 100 23454.65
167
Table A-2: I-Design
Run# Design Volume
Actual Volume
Design Speed
Actual Speed Truck% Grade% Temp CO2 (kg)
1 2000 1830 20 20 0 0 100 10727.79
2 7000 4800 26.25 25 0.1125 0 87.5 36800.78
3 4500 4150 45 40 0.075 0.025 75 31783.74
4 3250 3100 32.5 30 0.1125 0.0125 87.5 26408.99
5 4500 4200 45 40 0.075 0.025 75 32473.37
6 5750 5500 70 60 0 0.0375 87.5 33626.81
7 5750 4000 20 20 0.0375 0.04375 50 33882.86
8 7000 5800 70 40 0.15 0 50 36258.35
9 3875 3500 32.5 30 0.0375 0.03125 100 27584.05
10 3250 3300 57.5 60 0.0375 0.0125 62.5 16227.94
11 7000 6000 63.75 45 0.15 0 100 41752.3
12 7000 5100 32.5 30 0.1125 0.0375 100 60812.06
13 2000 2200 57.5 60 0 0.0375 62.5 11997.08
14 2000 2200 70 55 0.15 0.0375 62.5 25344.92
15 6375 4000 20 20 0 0.05 93.75 32876.53
16 4500 4200 45 40 0.075 0.025 75 32212.5
17 3250 2750 20 20 0.13125 0 62.5 21515.33
18 2000 2200 57.5 60 0.0375 0.03125 87.5 15705.85
19 3875 3650 38.75 35 0 0.0375 62.5 19973.83
20 5750 5300 70 50 0.0375 0.0125 62.5 27790.06
21 2625 2800 70 70 0 0 100 11474.53
22 7000 6300 63.75 50 0 0.05 50 38096.03
23 2625 2900 70 65 0.15 0 50 17493.75
24 7000 5550 32.5 30 0 0.05 62.5 34249.22
25 3250 3200 70 50 0.0375 0.05 50 25944.94
26 3250 2700 20 20 0.05625 0.0375 68.75 24458.09
27 7000 3900 20 20 0.13125 0.05 62.5 53104.94
28 7000 6500 63.75 55 0 0 100 25634.47
29 7000 5000 70 30 0.1125 0.0375 100 60042.1
30 2000 2000 26.25 25 0.01875 0.05 50 14377.28
31 5750 3800 20 20 0.15 0.05 93.75 63690.19
32 2625 2750 70 65 0.15 0 100 18659
33 5750 3850 20 20 0.15 0.0125 100 44425.63
34 3250 3250 57.5 45 0.0375 0.05 100 30104.07
35 5125 4600 51.25 40 0.1125 0.0125 87.5 37246.36
36 2625 2800 70 70 0 0.05 87.5 19620.25
168
Run# Design Volume
Actual Volume
Design Speed
Actual Speed Truck% Grade% Temp CO2 (kg)
37 2000 2250 57.5 60 0.075 0.0125 81.25 13917.96
38 2000 2000 26.25 25 0.15 0 100 16635.74
39 5750 5000 57.5 40 0.05625 0.0375 62.5 39061.92
40 3250 3000 32.5 30 0 0.0125 62.5 13192.9
41 4500 4200 45 40 0.075 0.025 75 32278.72
42 7000 3800 20 20 0.15 0.0125 50 37693.41
43 3250 3200 32.5 30 0.0375 0.0125 87.5 19125.23
44 5125 4350 32.5 25 0.15 0.0375 50 53123.58
45 2000 2000 26.25 25 0.13125 0.05 100 29400.51
46 4500 4200 45 40 0.075 0.025 75 32302.2
47 7000 5800 57.5 35 0.01875 0.05 100 46191.66
48 7000 4700 70 25 0.15 0.05 56.25 65395.93
49 7000 5500 32.5 30 0.0375 0.03125 87.5 41536.12
50 5750 5000 51.25 35 0.1125 0.0375 50 51401.08
51 2000 2000 32.5 30 0.15 0.0125 50 17506.59
52 3250 2700 20 20 0.1125 0.0375 81.25 33693.54
53 5750 5000 32.5 30 0.0375 0 62.5 23211.1
54 2000 1800 20 20 0.15 0.05 56.25 26069.79
55 5750 4650 57.5 30 0.15 0.05 87.5 71877.07
56 5750 5000 38.75 35 0.0375 0 81.25 24606.18
57 7000 6500 63.75 55 0 0 50 22421.74
58 5750 4850 32.5 30 0.1125 0 62.5 31010.57
59 4500 4200 45 40 0.075 0.025 75 32200.5
60 2000 1800 20 20 0 0.05 93.75 14544.36
61 5750 5250 57.5 45 0.1125 0.0125 62.5 36820.88
62 2625 2700 70 70 0 0 50 9854.164
63 2000 2250 70 55 0.1125 0.05 100 29535.11
64 7000 4000 20 20 0 0.0125 50 22048.54
65 3250 3200 57.5 40 0.15 0.0375 87.5 41960.63
66 2000 1800 20 20 0.01875 0 50 9472.055
67 3250 3250 57.5 55 0.1125 0.0125 62.5 21782.06
68 3250 3200 57.5 40 0.1125 0.05 50 38140.66
69 5750 5000 70 45 0.09375 0.0125 87.5 37821.2
70 6375 4000 20 20 0 0.00625 100 25116.46
169
170
APPENDIX B
ANALYSIS OF CUSTOM DESIGN OUTPUT BY LINK
171
Table B-1: Sample of Results of D-Design by Link
D-Design
Link Length Volume Speed Density Trucks Grade CO2 (kg)
1 2638.9 5011 17.56 285.3645 0.15 0.05 3699.05
2 5285.6 4812 18.01 267.1849 0.15 0.05 7107.241
3 5289.9 4520 18.4 245.6522 0.15 0.05 6679.387
4 5279.2 4295 18.42 233.1705 0.15 0.05 6334.071
5 5299.2 3952 18.54 213.1607 0.15 0.05 5850.692
6 5274.2 3779 18.35 205.9401 0.15 0.05 5567.998
7 5274.5 3561 18.21 195.5519 0.15 0.05 5247.698
8 5268.8 3415 18.4 185.5978 0.15 0.05 5026.34
9 5210 2942 18.78 156.656 0.15 0.05 4280.491
10 5166 2881 18.82 153.0818 0.15 0.05 4153.322
11 2463.6 2667 18.84 141.5605 0.15 0.05 1835.875
1 2638.9 6002 69.35 86.5465 0.15 0 2060.332
2 5285.6 6040 61.75 97.81377 0.15 0 3892.354
3 5289.9 5895 50.78 116.089 0.15 0 3933.883
4 5279.2 5792 31.18 185.7601 0.15 0 4680.189
5 5299.2 5434 37.48 144.984 0.15 0 3939.77
6 5274.2 5393 32.49 165.9895 0.15 0 4322.119
7 5274.5 5274 27.72 190.2597 0.15 0 4392.595
8 5268.8 5209 24.98 208.5268 0.15 0 4482.564
9 5210 4806 46.13 104.1838 0.15 0 3292.8
10 5166 4865 55.74 87.28023 0.15 0 3051.504
11 2463.6 4753 59.87 79.38868 0.15 0 1409.421
1 2638.9 3460 73.06 47.35834 0 0.04 1020.624
2 5285.6 3709 67.41 55.02151 0 0.04 2142.341
3 5289.9 3638 70.79 51.39144 0 0.04 2141.199
4 5279.2 3652 69.11 52.84329 0 0.04 2126.806
5 5299.2 3344 68.75 48.64 0 0.04 1951.504
6 5274.2 3687 69.57 52.99698 0 0.04 2150.194
7 5274.5 3829 69.8 54.85673 0 0.04 2235.66
8 5268.8 3960 16.79 235.8547 0 0.04 2700.317
9 5210 3056 37.33 81.86445 0 0.04 1672.532
10 5166 3230 66.54 48.54223 0 0.04 1813.839
11 2463.6 3145 71.73 43.84497 0 0.04 866.5194
1 2638.9 6593 40.05 164.6192 0.15 0 2035.817
2 5285.6 6382 35.49 179.8253 0.15 0 4024.912
172
D-Design
Link Length Volume Speed Density Trucks Grade CO2 (kg)
3 5289.9 6133 33.53 182.9108 0.15 0 4044.448
4 5279.2 5991 30.21 198.3118 0.15 0 4192.578
5 5299.2 5722 32.9 173.921 0.15 0 3900.056
6 5274.2 5628 32.32 174.1337 0.15 0 3896.018
7 5274.5 5508 30.56 180.2356 0.15 0 3841.128
8 5268.8 5443 28.67 189.85 0.15 0 3849.253
9 5210 5098 48.63 104.8324 0.15 0 2984.853
10 5166 5197 55.43 93.75789 0.15 0 2855.09
11 2463.6 5090 64.78 78.57363 0.15 0 1376.142
1 2638.9 7004 65.92 106.25 0 0 1233.147
2 5285.6 6970 61.9 112.601 0 0 2393.494
3 5289.9 6836 65.13 104.9593 0 0 2388.292
4 5279.2 6797 57.87 117.4529 0 0 2318.349
5 5299.2 6581 57.52 114.4124 0 0 2256.771
6 5274.2 6575 57.77 113.8134 0 0 2241.268
7 5274.5 6397 45.11 141.8089 0 0 2313.323
8 5268.8 6302 35.37 178.1736 0 0 2368.654
9 5210 5928 56.13 105.612 0 0 2009.722
10 5166 5953 59.78 99.5818 0 0 1984.806
11 2463.6 5830 66.63 87.49812 0 0 965.9035
1 2638.9 1951 19.89 98.08949 0.15 0.05 1633.111
2 5285.6 2121 19.4 109.3299 0.15 0.05 3575.065
3 5289.9 1983 19.21 103.2275 0.15 0.05 3347.666
4 5279.2 1941 19.16 101.3048 0.15 0.05 3270.704
5 5299.2 1576 19.11 82.46991 0.15 0.05 2666.68
6 5274.2 1826 19.13 95.45217 0.15 0.05 3074.124
7 5274.5 1904 19.08 99.79036 0.15 0.05 3205.919
8 5268.8 2139 19.05 112.2835 0.15 0.05 3598.324
9 5210 1432 18.9 75.7672 0.15 0.05 2383.515
10 5166 1545 19.18 80.55266 0.15 0.05 2545.998
11 2463.6 1401 19.11 73.3124 0.15 0.05 1102.646
1 2638.9 2996 31.81 94.18422 0.105 0.05 1713.127
2 5285.6 3183 31.04 102.5451 0.105 0.05 3641.974
3 5289.9 3058 30.88 99.0285 0.105 0.05 3502.086
4 5279.2 3059 30.48 100.3609 0.105 0.05 3495.57
5 5299.2 2667 30.61 87.12839 0.105 0.05 3059.85
6 5274.2 2902 30.73 94.43541 0.105 0.05 3313.152
7 5274.5 2970 30.6 97.05882 0.105 0.05 3390.493
173
D-Design
Link Length Volume Speed Density Trucks Grade CO2 (kg)
8 5268.8 3180 26.95 117.9963 0.105 0.05 3670.471
9 5210 2433 26.6 91.46617 0.105 0.05 2781.078
10 5166 2615 30.54 85.62541 0.105 0.05 2922.365
11 2463.6 2482 30.82 80.53212 0.105 0.05 1324.715
1 2638.9 5122 17.56 291.6856 0.105 0.015 2280.683
2 5285.6 4838 17.53 275.984 0.105 0.015 4315.347
3 5289.9 4522 17.69 255.6246 0.105 0.015 4024.688
4 5279.2 4314 17.64 244.5578 0.105 0.015 3835.697
5 5299.2 3974 18.07 219.9225 0.105 0.015 3517.986
6 5274.2 3818 18.29 208.7479 0.105 0.015 3349.122
7 5274.5 3611 18.54 194.7681 0.105 0.015 3152.948
8 5268.8 3490 18.76 186.0341 0.105 0.015 3032.159
9 5210 3032 18.82 161.1052 0.105 0.015 2602.417
10 5166 2953 18.82 156.9075 0.105 0.015 2511.498
11 2463.6 2727 18.81 144.9761 0.105 0.015 1107.669
1 2638.9 5312 17.11 310.4617 0 0 1388.643
2 5285.6 5113 17.86 286.2822 0 0 2600.457
3 5289.9 4805 18.38 261.4255 0 0 2400.437
4 5279.2 4480 17.8 251.6854 0 0 2281.317
5 5299.2 4132 17.89 230.967 0 0 2105.485
6 5274.2 4003 18.12 220.9161 0 0 2012.546
7 5274.5 3793 18.32 207.0415 0 0 1893.256
8 5268.8 3668 18.56 197.6293 0 0 1813.506
9 5210 3128 18.83 166.1179 0 0 1515.15
10 5166 3014 18.83 160.0637 0 0 1446.62
11 2463.6 2780 18.83 147.6367 0 0 637.1382
174
Table B-2: Sample of Results of I-Design by Link
I-Design
Link Length Volume Speed Density Trucks Grade CO2 (kg)
1 2638.9 1930 19.87 97 0 0 549.7146
2 5285.6 2124 19.4 109 0 0 1230.352
3 5289.9 2015 19.17 105 0 0 1177.626
4 5279.2 2003 19.15 105 0 0 1169.085
5 5299.2 1626 19.09 85 0 0 954.823
6 5274.2 1882 19.11 98 0 0 1098.884
7 5274.5 1955 19.04 103 0 0 1144.293
8 5268.8 2192 18.99 115 0 0 1283.968
9 5210 1448 18.93 76 0 0 840.5872
10 5166 1556 19.13 81 0 0 888.8208
11 2463.6 1426 19.08 75 0 0 389.6333
1 2638.9 5947 23.3 255 0.1125 0 2315.363
2 5285.6 5773 24.27 238 0.1125 0 4424.06
3 5289.9 5481 24.11 227 0.1125 0 4215.949
4 5279.2 5253 24.17 217 0.1125 0 4028.38
5 5299.2 4965 23.94 207 0.1125 0 3837.876
6 5274.2 4794 23.5 204 0.1125 0 3715.84
7 5274.5 4593 23.68 194 0.1125 0 3548.709
8 5268.8 4458 23.89 187 0.1125 0 3428.348
9 5210 4055 24.99 162 0.1125 0 3027.757
10 5166 3964 25.06 158 0.1125 0 2929.512
11 2463.6 3766 25.06 150 0.1125 0 1328.985
1 2638.9 4433 44.28 100 0.075 0.025 1653.894
2 5285.6 4564 43.36 105 0.075 0.025 3412.976
3 5289.9 4467 43.25 103 0.075 0.025 3344.258
4 5279.2 4488 42.63 105 0.075 0.025 3356.051
5 5299.2 4119 42.98 96 0.075 0.025 3090.985
6 5274.2 4345 43.14 101 0.075 0.025 3243.659
7 5274.5 4307 32.85 131 0.075 0.025 3334.423
8 5268.8 4259 19.48 219 0.075 0.025 3795.785
9 5210 3511 34.79 101 0.075 0.025 2625.102
10 5166 3672 42.57 86 0.075 0.025 2685.673
11 2463.6 3557 43.37 82 0.075 0.025 1240.933
1 2638.9 3250 32 102 0.1125 0.0125 1374.213
2 5285.6 3450 31.33 110 0.1125 0.0125 2924.825
3 5289.9 3339 30.98 108 0.1125 0.0125 2835.824
175
I-Design
Link Length Volume Speed Density Trucks Grade CO2 (kg)
4 5279.2 3307 30.85 107 0.1125 0.0125 2803.89
5 5299.2 2885 30.8 94 0.1125 0.0125 2456.16
6 5274.2 3125 30.88 101 0.1125 0.0125 2646.741
7 5274.5 3211 30.76 104 0.1125 0.0125 2720.343
8 5268.8 3449 28.1 123 0.1125 0.0125 2975.785
9 5210 2694 27.94 96 0.1125 0.0125 2301.842
10 5166 2797 30.88 91 0.1125 0.0125 2319.143
11 2463.6 2653 30.95 86 0.1125 0.0125 1050.219
1 2638.9 4538 44.2 103 0.075 0.025 1693.238
2 5285.6 4655 43.27 108 0.075 0.025 3481.436
3 5289.9 4544 43.05 106 0.075 0.025 3402.807
4 5279.2 4562 42.31 108 0.075 0.025 3412.884
5 5299.2 4206 42.68 99 0.075 0.025 3157.548
6 5274.2 4475 39.66 113 0.075 0.025 3357.461
7 5274.5 4340 24.53 177 0.075 0.025 3561.707
8 5268.8 4232 17.42 243 0.075 0.025 3897.875
9 5210 3472 34.09 102 0.075 0.025 2606.51
10 5166 3643 42.47 86 0.075 0.025 2664.828
11 2463.6 3545 43.17 82 0.075 0.025 1237.073
1 2638.9 5802 69.09 84 0 0.0375 1843.338
2 5285.6 5862 65.09 90 0 0.0375 3611.874
3 5289.9 5784 66.38 87 0 0.0375 3619.748
4 5279.2 5836 55.79 105 0 0.0375 3517.544
5 5299.2 5443 50.59 108 0 0.0375 3295.711
6 5274.2 5528 66.15 84 0 0.0375 3440.631
7 5274.5 5560 55.23 101 0 0.0375 3348.035
8 5268.8 5574 31.29 178 0 0.0375 3448.325
9 5210 5043 50.04 101 0 0.0375 3002.021
10 5166 5140 57.62 89 0 0.0375 3029.343
11 2463.6 5036 66.52 76 0 0.0375 1470.242
1 2638.9 5245 17.39 302 0.0375 0.04375 2285.125
2 5285.6 5038 18.04 279 0.0375 0.04375 4356.522
3 5289.9 4668 17.66 264 0.0375 0.04375 4060.76
4 5279.2 4449 18.04 247 0.0375 0.04375 3843.355
5 5299.2 4071 17.85 228 0.0375 0.04375 3539.588
6 5274.2 3931 17.82 221 0.0375 0.04375 3402.178
7 5274.5 3680 17.51 210 0.0375 0.04375 3198.138
8 5268.8 3544 17.94 198 0.0375 0.04375 3059.378
176
I-Design
Link Length Volume Speed Density Trucks Grade CO2 (kg)
9 5210 3032 18.73 162 0.0375 0.04375 2563.131
10 5166 2965 18.86 157 0.0375 0.04375 2479.779
11 2463.6 2741 18.84 145 0.0375 0.04375 1094.91
1 2638.9 6631 40.9 162 0.15 0 2037.005
2 5285.6 6483 42 154 0.15 0 3970.902
3 5289.9 6232 36.12 173 0.15 0 3915.121
4 5279.2 6063 35.32 172 0.15 0 3816.132
5 5299.2 5823 35.06 166 0.15 0 3684.509
6 5274.2 5742 33.08 174 0.15 0 3853.204
7 5274.5 5616 32.98 170 0.15 0 3787.325
8 5268.8 5539 30.36 182 0.15 0 3857.77
9 5210 5193 47.93 108 0.15 0 3055.306
10 5166 5239 52.82 99 0.15 0 2920.29
11 2463.6 5124 62.85 82 0.15 0 1360.79
1 2638.9 3864 31.82 121 0.0375 0.03125 1471.157
2 5285.6 4018 31.19 129 0.0375 0.03125 3067.182
3 5289.9 3899 30.88 126 0.0375 0.03125 2981.553
4 5279.2 3853 30.73 125 0.0375 0.03125 2941.582
5 5299.2 3415 30.74 111 0.0375 0.03125 2617.554
6 5274.2 3630 30.79 118 0.0375 0.03125 2768.153
7 5274.5 3642 27.96 130 0.0375 0.03125 2842.369
8 5268.8 3680 17.18 214 0.0375 0.03125 3400.089
9 5210 2861 26.47 108 0.0375 0.03125 2239.108
10 5166 3004 30.93 97 0.0375 0.03125 2241.855
11 2463.6 2845 31.09 92 0.0375 0.03125 1013.444
177
APPENDIX C
I-4 PROTOTYPE OPERATING MODE DISTRIBUTIONS
178
Table C-1: I-4 Prototype Model Links Output
Link#
Link
Volume
Ave
Speed Distance
1 6159 22.09 0.5
2 6311 27.31 1.001
3 6064 25.81 1.002
4 6062 34.07 1
5 5457 36.43 1.004
6 5478 30.3 0.999
7 5398 27.5 0.999
8 5373 26.33 0.998
9 4762 34.79 0.987
10 4903 36.86 0.978
11 4771 36.86 0.467
179
Figure C-2: Operating Modes on I-4 Prototype Model Links
180
Figure C-2: Operating Modes on I-4 Prototype Model Links
181
Figure C-2: Operating Modes on I-4 Prototype Model Links
182
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