How does the Use of Light Rail Park-and-ride Facilities in Charlotte Influence Vehicle Emissions and Create more Vehicle Miles Traveled?

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How does the Use of Light Rail Park-and-ride Facilities in Charlotte

Influence Vehicle Emissions and Create more Vehicle Miles

Traveled?

By

David Cook

A thesis submitted to the University of North Carolina at Charlotte

Graduate Department of Geography and Earth Sciences

Charlotte

Spring 2011

Professor Michael Duncan

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Abstract

How does the Use of Light Rail Park-and-ride Facilities in Charlotte Influence Vehicle

Emissions and Create more Vehicle Miles Traveled?

Park-and-ride lots are often promoted as an easy, cheap and equitable way of providing

transit to a much broader population that increases transit ridership. The purpose of this thesis is

to determine an estimate as to what extent VMT and vehicle emissions have increased or

decreased as a result of light rail park-and-ride usage and, additionally, to find out if the literature

discussed holds true in the Charlotte, NC example. For this study, two estimates will be created,

one indicating VMT and emissions of park-and-ride users with existing conditions and another

indicating VMT and emissions with conditions before the light rail was built, assuming that all

park-and-ride users drove to their destination. Another component of this thesis was to observe

and estimate to what extent the VMT savings would be if all park-and-ride lots were replaced

with transit-oriented developments of varying densities.

The final analysis of the two estimates indicated a 50% drop in vehicle miles traveled

among park-and-ride users by traveling to park-and-ride lots instead of directly in the CBD.

Emission reductions varied by vehicle type and emission type but were 11 – 37 percent range for

each emission type. However, this impact is considered to be minimal in the overall reduction of

VMT and emissions, as the amount of people using park-and-ride and light rail as an option for a

trip is small compared to the amount of similar trips being made overall by all modes. Overall

regional VMT reduction is estimated to be about 0.2 percent as a result of park-and-ride users

using park-and-ride and light rail instead of driving the full distance.

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List of Figures

Figure 1: Study Area Locater Map Page 9

Figure 2: Average VMT by Station, All Stations and All Park-and-Ride

Stations Page 25


Stations Page 27


Stations (hypothetical) Page 30

Figure 5: Absolute Change in VMT between Scenario 1 (Home-Station) and

Scenario 2 (Home-Final Destination) among Survey Respondents Page 32


Stations (hypothetical) Page 34

Figure 7: Difference in VMT between Survey Sample all Users for

Hypothetical Scenario Page 35

Figure 8: Absolute Change in VMT between Scenario 1 (Home-Station)

and Scenario 2 (Home-Final Destination) for all estimated

Park-and-Ride Users in a given day Page 37

Figure 9: Total CO Emissions, CO Emissions per Mile, and CO Emissions

per User in grams for a 2001 average age vehicle for both LDV

and LDT Vehicle Types Page 40

Figure 10: Total HC Emissions, HC Emissions per Mile, and HC Emissions



Figure 11: Total NOx Emissions, NOx Emissions per Mile, and NOx Emissions

per User in grams for a 2001 average age vehicle for both LDV and

LDT Vehicle Types Page 42

Figure 12: Total CO Emissions for Cars and Trucks for each Station Page 44

Figure 13: Average per User CO Emissions assuming Cars and Trucks

for Each Station Page 45

Figure 14: Total HC Emissions for Cars and Trucks for each Station Page 46

Figure 15: Average per User HC Emissions for Cars and Trucks for Each Station Page 47

Figure 16: Total NOx Emissions for Cars and Trucks for each Station Page 48

Figure 17: Average per User NOx Emissions for Cars and Trucks for Each Station Page 49

Figure 18: Total CO Emissions, CO Emissions per Mile, and CO Emissions



Figure 19: Total HC Emissions, HC Emissions per Mile, and HC Emissions



Figure 20: Total NOx Emissions, NOx Emissions per Mile, and NOx Emissions

per User in grams for a 2001 average age vehicle for both LDV and

LDT Vehicle Types Page 54

Figure 21: Total CO Emissions for Cars and Trucks for each Potential Station

(Home – Final Destination) Page 57

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Figure 22: Average Per User CO Emissions for Cars and Trucks for Each

Potential Station (Home – Final Destination) Page 58

Figure 23: Total HC Emissions for Cars and Trucks for each Potential Station


Figure 24: Average Per User HC Emissions for Cars and Trucks for Each


Figure 25: Total NOx Emissions for Cars and Trucks for each Potential Station


Figure 26: Average Per User NOx Emissions for Cars and Trucks for Each


Figure 27: Aggregate CO Emissions for Cars (LDV’s) for the Home – Station

Scenario and the Home – Final Destination Scenario Page 65

Figure 28: Average Per User CO Emissions for Cars (LDV’s) for the

Home – Station Scenario and the Home – Final Destination Scenario Page 65

Figure 29: Aggregate CO Emissions for Trucks (LDT’s) for the Home – Station

Scenario and the Home – Final Destination Scenario Page 66

Figure 30: Average Per User CO Emissions for Cars (LDV’s) for the

Home – Station Scenario and the Home – Final Destination Scenario Page 66

Figure 31: Total Hot Soak HC Emissions (in grams) by Station for all

Park-and-Ride Users assuming 100 percent ideal sunny and partly

cloudy weather Page 70

Figure 32: Total Running and HC Emissions in the Home – Station Scenario

for Cars (LDV’s) 100 percent ideal sunny and partly cloudy weather Page 71


for Trucks (LDT’s) 100 percent ideal sunny and partly cloudy weather Page 71

Figure 34: Total Running and HC Emissions in the Home – Final Scenario




Figure 36: Total Hot Soak HC Emissions (in grams) by Station for all

Park-and-Ride Users assuming 55.9 percent ideal sunny and partly

cloudy weather Page 74


for Cars (LDV’s) 55.9 percent ideal sunny and partly cloudy weather Page 75


for Trucks (LDT’s) 55.9 percent ideal sunny and partly cloudy weather Page 75





Figure 41: VMT Reduction Amounts by Scenario (500 People) Page 81

Figure 42: VMT Reduction Amounts by Scenario (1,000 People) Page 82

Figure 43: Differences in VMT reduction for the two density level scenarios Page 83

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List of Tables

Table 1: Estimated VMT for 2009 Park-and-Ride User Survey Respondents

from Home TAZ to nearest Park-and-Ride TAZ (existing conditions) Page 25

Table 2: Estimated VMT for All Park-and-Ride Users in a Given Day from

Home TAZ to nearest Park-and-Ride TAZ (existing conditions) Page 27

Table 3: Estimated VMT for 2009 Park-and-Ride User Survey Respondents

from Home TAZ to Final Destination TAZ (pre-existing conditions Page 30

Table 4: Absolute and Percent Changes in overall VMT among different Station

Categories (Survey Respondents) Page 33

Table 5: Estimated VMT for all potential 2009 Park-and-Ride Users from

Home TAZ to Final Destination TAZ (pre-existing conditions) Page 34

Table 6: Absolute and Percent Changes in overall VMT among different Station

Categories (All Estimated Users) Page 37

Table 7: Total, per user and per mile emissions by type of emission from home

to station using the 2001 model year as a base for start and running

emissions rate (all Park-and-ride users) Page 39

Table 8: Total and Average per User CO Emissions assuming Cars and Trucks

for each Park-and-Ride Station (Home – Station) Page 44

Table 9: Total and Average per User HC Emissions for Cars and Trucks for

each Park-and-Ride Station (Home – Station) Page 46

Table 10: Total and Average per User NOx Emissions for Cars and Trucks for

each Park-and-Ride Station (Home – Station) Page 48

Table 11: Total, per user and per mile emissions by type of emission from home

to final destination using the 2001 model year as a base for start and

running emissions rate (all Park-and-ride users Page 51

Table 12: Total and Average per User CO Emissions for Cars and Trucks for

each Home Park-and-Ride Station (Home – Final Destination) Page 57

Table 13: Total and Average per User HC Emissions for Cars and Trucks for

each Home Park-and-Ride Station (Home – Final Destination) Page 59

Table 14: Total and Average per User NOx Emissions for Cars and Trucks for

each Potential Park-and-Ride Station (Home – Final Destination) Page 61

Table 15: Total Hot Soak HC Emissions (in grams) by Station for all

Park-and-Ride User’s assuming 100 percent ideal sunny and partly

cloudy weather: Page 68

Table 16: Total Hot Soak HC Emissions (in grams) by Station for all

Park-and-Ride User’s assuming 55.9 percent ideal sunny and partly Page 74

cloudy weather:

Table 17: VMT reduction among TOD residents for five reduction rate scenarios

for 500 people (300 households) Page 81

Table 18: VMT reduction among TOD residents for five reduction rate scenarios

for 1,000 people (600 households) Page 82

Table 19: Average start and running emissions for cars and trucks by model year Page 90

Table 20: Survey results and data sheet (Home – Station Scenario) Page 91

Table 21: Survey results and data sheet (Home – Final Destination Scenario) Page 98

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List of Abbreviations

ATAZ Attraction Transportation Analysis Zone

CATS Charlotte Area Transit System

CBD Central Business District

CDOT Charlotte Department of Transportation

CO Carbon Monoxide

CO2 Carbon Dioxide

CTPP Census Transportation Planning Products

DMV Department of Motor Vehicles

EPA Environmental Protection Agency

GIS Geographic Information System

HC Hydrocarbons

LDV Light Duty Vehicle

LDT Light Duty Truck

MUMPO Mecklenburg-Union Metropolitan Planning Organization

NOAA National Oceanic and Atmospheric Administration

NOx Mono-Nitrogen Oxides

PTAZ Production Transportation Analysis Zone

TAZ Traffic Analysis Zone

TOD Transit-Oriented Development

TransCAD Urban Transportation Modeling System

VHT Vehicle Hours Traveled

VMT Vehicle Miles Traveled

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Chapter 1: Introduction and Problem Statement

Park-and-ride commuter parking lots are important facilitators for the LYNX Light Rail

in Charlotte and encourage people not living in nearby Transit-Oriented Developments (mixed-

use developments centered around transit stations) to use their cars and to utilize free parking

adjacent to a transit station. Park-and-ride lots are a cheap, easy, and more equitable way of

providing transit access to a much broader population and thus increase transit ridership.

Increasing transit ridership is also a very important goal of transit agencies. Does park-and-ride

reduce VMT or does it create more VMT, thus generating more congestion and emissions and, if

so, to what extent?

The purpose of this research problem is to determine an estimate as to what extent

commuter VMT has changed (increased or decreased) as a result of having the light rail park-

and-ride. How has the aggregate commuter travel length (vehicle miles traveled) been affected

by having the light rail park-and-ride facilities? By estimating the impact of light rail park-and-

ride lots on VMT it is possible to estimate preliminary environmental effects such as cold start

(higher emissions per capita in shorter trips) and hot soak evaporative emissions using national

coefficients provided by the EPA. Using network data provided by the CDOT, it was possible to

estimate the amount of VMT change as a result of having light rail park-and-ride lots. The origin

of the park-and-ride user is important in understanding the spatial scope of park-and-ride

catchment areas and the travel behavior of park-and-ride users and will be a core part of the

proposed research question.

The following figure indicates a map of the study area, which includes every park-and-

ride station along the light rail in South Charlotte along South Boulevard. The map of the study

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area includes a general overview of the study area, location of the light rail in Charlotte, location

of light rail facilities, and the location of park-and-ride facilities, which are indicated by red

buffers. For this study, the study area will include the light rail line from Uptown to I-485 at

Pineville as well as the extended area in which park-and-ride users live around each park-and-

ride lot. The extended area is based on a 2009 on-board user survey in which the origin location

for all park-and-ride users has been recorded and grouped by Traffic Analysis Zone (TAZ).

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Figure 1: Study area locator map indicating all stations and stops along the light rail and

commuter park-and-ride lots circled in red

GIS Data and layers Source: Charlotte-Mecklenburg Planning Department

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Chapter 2: Literature Review

Rail and bus-based park-and-ride lots first appeared in the late 1960s and early 1970s in

the United Kingdom (Parkhurst 2000). Throughout the 1970s, park-and-ride lots became very

popular in the United Kingdom and eventually the concept spread into other Western European

countries and North America. Today park-and-ride lots are expanding rapidly across the world,

including Asia and South America. They have, in most cases, proven successful in attracting

auto commuters and increasing transit ridership. These lots are a more equitable (those with

access to an automobile) way of providing transit access to a much larger population than that of

transit-oriented developments and thus increase transit ridership (Burgess 2008, Parkhurst 1995

and 2000).

However, congestion remains persistent and continues to grow in cities that have park-

and-ride lots. There is a debate as to whether these facilities have actually led to an increase in

vehicle miles traveled rather than a decrease. There is a modest body of literature on the subject

of the direct and indirect effects of park-and-ride usage on increasing congestion and VMT

(vehicle miles traveled) in the UK experience but relatively little in the context of the US

experience (Parkhurst 1995 and 2000). This makes the research questions of how and to what

extent VMT has been affected in the Charlotte region an important contribution to the existing

literature. There is some evidence that park-and-ride facilities inadvertently increase congestion

and VMT through both direct and indirect effects such as induced demand (in which newly

freed-up sections of roadway created through the interception of automobiles by park-and-ride

lots are quickly filled up by new users and new types of trips) and traffic relocation (Parkhurst

1995 and 2000).

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Ideally, park-and-ride lots serve to intercept would-be trips into the center city by

capturing auto-based traffic along the way and enabling people to utilize transit for part of the

trip. In a perfect situation, every car parked at a park-and-ride lot would mean one less car

traveling into the center city. According to Parkhurst (1995), park-and-ride lots almost certainly

lead to an increase in congestion and auto traffic. Trips removed from the roadway network due

to park-and-ride auto-interception have been replaced by new trips encouraging new auto users

to quickly fill up the newly created open spaces on the road, many of which are going into the

center city (Parkhurst 1995). However, the induced demand effect can also occur if the park-and-

ride lots are replaced with transit-oriented development (TOD’s) since new TOD residents would

also be driving less. There has been modest body of articles published over the past 30 years

about the negative effects of park-and-ride lots and the indirect impacts they have on increasing

auto-based roadway congestion and traffic (Burgess 2008, Parkhurst 1995 and 2000). This

literature review will summarize scholarly papers that relate to the question of park-and-ride

impacts on VMT in Charlotte, North Carolina and draw conclusions as to why this study is

important in the broader context of the literature.

Parkhurst (1995) notes many drawbacks with regard to park-and-ride lots, including its

ineffectiveness in reducing traffic downstream of a parking site (induced demand effect). This

effect can also occur by building TOD’s. Another drawback includes abstraction of former

transit users to the automobile (former bus user’s now using park-and-ride lots). Other

drawbacks include the environmental impact of building large surface parking lots and decks on

sensitive land on the urban fringe, political inequity since it is not an open option to all even

though many times the entire population pays for its subsidies through taxes, and the increased

parking capacity which may create additional trip-end opportunities (Parkhurst 1995). After

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producing a survey for Oxford and York, Parkhurst (1995) concluded that there was a significant

amount of transit abstraction (i.e. former bus users who have switched to driving with the advent

of light rail park-and-ride lots) and that those who used park-and-ride lots were more likely to

make more discretionary trips than regular auto commuters (Parkhurst 1995). He also concluded

that there was no “decongestion dividend” in which VMT could be offset (Parkhurst 1995). He

recommended that in order for park-and-ride to be successful, it is important to consider the

region as a whole by investigating the impact of traffic growth, trip generation, and residential

self selection (Parkhurst 1995). The largest contribution of park-and-ride lots to congestion

growth was the induced demand effect in which park-and-ride creates more road space further

upstream of a station, encouraging new users and new trips for those not using the same network

beforehand (Parkhurst 1995). He also recommended policy packaging such as road pricing and

traffic calming coupled with park-and-ride lots as a means to reduce congestion and VMT

(Parkhurst 1995).

In a later article Parkhurst (2000) looks more in depth at the travel behavior of park-and-

ride users and the negative congestion/VMT-related effects of the lots. Parkhurst (2000) notes

that there is still a debate as to the true effectiveness of reducing congestion and VMT through

park-and-ride and that there is little evidence supporting the contention that it does indeed reduce

congestion (Parkhurst 2000). He also notes that in cases in which traffic-reducing policies have

been successfully implemented with park-and-ride lots the change in VMT has been slight if at

all (Parkhurst 2000). Parkhurst (2000) found that auto trips ranged from 1.5 to 12.7 kilometers

(0.9 to 7.9 miles) and that many park-and-ride users would have used transit anyway if park-and-

ride did not exist (Parkhurst 2000).

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Much of the research on park-and-ride lots comes from the United Kingdom since it has

the longest history of any country of using these lots. The spatial redistributing of traffic from a

regional scale to a more localized scale can create induced discretionary traffic (Meek, Ison, and

Enoch 2007). This induces access trips further upstream of park-and-ride lots and creates new

discretionary trips among park-and-ride users (Meek, Ison, and Enoch 2007). They also point out

that although those park-and-ride lots serve to increase auto-based traffic by themselves; they

may have an impact on reducing congestion and VMT if implemented as part of a package with

traffic-reducing policies such as road pricing, traffic calming, and parking control in the center

city, thus reducing the induced demand effect (Meek, Ison, and Enoch 2007). They also point out

that there is a dearth in the literature as to the origin (i.e. home addresses) of park-and-ride users

(Meek, Ison, and Enoch 2007). The origin is important in understanding the spatial scope of

park-and-ride catchment areas and the travel behavior of park-and-ride users and will be a core

part of the proposed research question.

The effectiveness of park-and-ride has been explored in many other works of literature.

Whitefield and Cooper (1998) indicated that the long-range effects of park-and-ride are more

complex than generally acknowledged and there is a lack of evidence showing that park-and-ride

lots reduce car-based trips (Whitefield & Cooper 1998). A research paper done by RPS (2009)

concluded that much of the benefit of park-and-ride is economic rather than sustainable in nature

(RPS 2009). In an article by Sherwin (1998), the author wrote of significant indications that

park-and-ride lots rely on auto ownership, which promotes car use and may increase journey

lengths or VMT through both direct and indirect effects (Sherwin 1998). In a more recent article

by Meek, Ison and Enoch (2009), the authors point out that in a survey of towns and cities with

park-and-ride lots 33 percent reported a VMT increase and 20 percent reported no increase or

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decline, with all other respondents indicating a slight decrease or no answer (Meek, Ison, &

Enoch 2009).

Park-and-ride lots generally do little to discourage auto ownership and many non-work

based trips will be made anyway and in greater frequency since park-and-ride rail lines often do

not accommodate efficient trip chaining. Dickins (1991) draws many of the same conclusions as

Parkhurst (1995 and 2000), Meek (2007 and 2009) and Burgess (2008), but also explains that

American cities are far more dependent on and susceptible to the negative effects of park-and-

ride lots than European cities due to the differences in urban structure between the two regions

(Dickins 1991).

One of the articles most relevant to the research question I have proposed was written by

Elizabeth Deakin and Manish Shirgaokar (2005) and investigates the travel behavior of park-

and-ride users in San Francisco and its impact on regional VMT. Deakin and Shirgaokar (2005)

provided detailed survey-based information on the origin and characteristics of park-and-ride

users in the US context (Deakin & Shirgaokar 2005). This information differs in many regards

compared to the UK examples explained before, but shares many of the same base assumptions

and results. Deakin and Shirgaokar (2005) found that nearly all of the park-and-ride users

commuted to the stations by automobile alone (Deakin & Shirgaokar 2005). Parking was

provided for free at nearly every station and the number of stations was growing rapidly (Deakin

& Shirgaokar 2005). The survey provided the first true look at the types of people using the park-

and-ride lots in the San Francisco Bay area and their travel behavior characteristics. Deakin and

Shirgaokar’s (2005) results showed that 93 to 100 percent of the users, depending on the station,

started their trips at home and 94 to 97 percent were using park-and-ride lots for work purposes

only, which differ from the examples in the Oxford/York studies (Deakin & Shirgaokar 2005).

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Those studies found that only 44 to 48 percent were using the park-and-ride lots for work only

(though even in the UK context this still represented the largest type of trip being made by park-

and-ride users) (Parkhurst 1995). Deakin and Shirgaokar (2005) also revealed that around 67 to

93 percent of all park-and-ride users in the San Francisco area were using park-and-ride lots four

or more days a week and 89 to 100 percent used it two to three days a week (Deakin &

Shirgaokar 2005). The average travel time to a station was found to be 16 minutes (Deakin &

Shirgaokar 2005). The key findings were that most persons were single person commuters who

worked full time and used the same park-and-ride lots for four or more days a week. When added

to the network, their contribution in decreasing VMT was almost negligible due to the high costs

associated with single person usage of park-and-ride lots and the induced demand effect (Deakin

& Shirgaokar 2005).

There is a modest amount of scholarly peer-reviewed research indicating that park-and-

ride lots do little to reduce VMT and are more likely than not to increase congestion due to many

indirect effects of park-and-ride usage. There was no literature found to support park-and-ride as

a tool to reduce congestion by itself. Parkhurst provides a context for the UK experience that is

also relevant to the US experience due to the similar types of people using park-and-ride lots and

similar indirect effects such as induced demand and redistribution of traffic. All of the papers

agreed that park-and-ride is appealing to auto users, increases economic activity in the center city

by reducing the need for central parking in prime land, and that it can serve as a politically sound

way of increasing ridership, but generally remains ineffective in reducing VMT. There also

appears to be a dearth in the research as to the environmental effects of park-and-ride lots,

including hot soak, in which evaporative emissions from cars parked on surface lots can create

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pollution, and the disproportionately high amount of emissions expelled per capita in a short

three-mile trip when compared to a longer 10-mile trip due to the effect of start emissions.

Emissions from park-and-ride users may be higher as a result of the very nature of the

type of trip being made, indicating that emissions per VMT have a high correlation with trip

distance. Shorter trips to a nearby park-and-ride lot have disproportionately larger amounts of

emissions per capita than cars traveling the entire length of the trip to a CBD or center city.

Related to this, Burgess (2008) indicates in his article that 84 percent of hydrocarbons and 54

percent of nitrogen oxide are burned in the first three miles of a 10-mile trip and claims that high

per capita emissions of park-and-ride users is just as important as other indirect effects of park-

and-ride such as induced demand (Burgess 2008). The EPA currently uses the MOBILE6 model

and MOVES Motor Vehicle Emissions Simulator model to calculate both start and running

emissions at the national level based on a number of variables. The MOBILE6 vehicle emissions

modeling software was developed by the EPA and has been used by EPA papers as well as

Houk’s paper “Making Use of MOBILE6’s Capabilities for Modeling Start Emissions” to model

running and start emissions (EPA website, EPA 2009, and Houk 2004).

These scholarly papers are important in the context of Charlotte as well since there have

been a limited number of studies in the US and none in Charlotte as to the direct and indirect

effects of park-and-ride and its impact on vehicle emissions and VMT. One of the main points

of this research was to determine if the Charlotte experience with park-and-ride matches the

general consensus of the literature. As there is relatively little literature regarding the US

experience, examining how park-and-ride has had an effect on emissions and VMT in the

Charlotte region is an important addition.

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Chapter 3: Data and Methodology

The main element being researched in this study is the connection/relationship between

vehicle miles traveled and the use of park-and-ride lots by automobile commuters. This study

aims to determine the estimated difference between vehicle miles travelled and emissions among

light rail park-and-ride trips versus the amount of vehicles miles travelled and emissions that

would have occurred had those trips continued to their final destination (typically in the CBD).

There are several methods by which could have been collected for this study, including on-site

license plate data collection (to determine origin location and vehicle type mix) and a survey. For

the purpose of this study, an on-board user survey provided by CDOT will be used since it has

the origin and destination already available for park-and-ride users and because it is financially

more feasible than on-site data collection. Many of the before-mentioned authors used surveys to

identify the travel behavior and characteristics of park-and-ride users, especially in the study

done by Deakin and Shirgaokar (2005) in the San Francisco Bay Area. This survey-based study

helped to identify the characteristics of park-and-ride users in the San Francisco Bay Area,

including how far they had traveled and from where the commuters were coming (Deakin and

Shirgaokar 2005). This thesis used TransCAD, which is traditionally used as an urban

transportation modeling system, as a simple database management system, to help organize the

data and answer this question.

Determining the origin for park-and-ride users is essential to determining the distance

they traveled to reach a station and the impact they have on regional vehicle miles traveled. The

spring 2009 on-board user survey compiled origin (home) and destination (Final non-station

destination) data grouped by TAZ (Traffic Analysis Zone) for 309 park-and-ride users. The

survey also provides the method of entry for each light rail station (e.g. park-and-ride, carpool,

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walk, kiss-and-ride, and other) as well as the direction of the light rail user and the final station

stop from station of entry. For the purpose of this study, only VMT and emissions for the 309

park-and-ride users will be calculated. The survey indicates the origin as production TAZ

(PTAZ) and the destination as attraction TAZ (ATAZ) between home and final-destination

locations. Using the PTAZ survey results it will be possible to geocode the origin for all 309

park-and-ride users for the purpose of estimating a network distance between home addresses

and their corresponding final-destination location and park-and-ride facility. For the purpose of

this study, an estimate was created to observe the effects of redistributing the traffic created by

park-and-ride users from trips made directly to Uptown Charlotte to trips being made to park-

and-ride lots. Once the VMT has been estimated for both scenarios, it will be possible to expand

the survey results to include all of the estimated daily park-and-ride users based on the

Combined ExpFactor Linked field in the survey. The Combined ExpFactor Linked field is a

weighted sum of all the estimated number of park-and-ride users represented by each of the park-

and-ride survey respondents. This will indicate an approximate VMT amount for all estimated

light rail park-and-ride users in a given day.

For this study, two transportation network estimates will be created based on the data

provided by CDOT. One will indicate the amount of VMT as a result of having select trips to

park-and-ride lots (existing conditions). The other model will indicate the amount of VMT if

those same trips were instead being made directly to their final destination (generally in the CBD

and surrounding areas), with a new assumption that all Uptown commuters are now going from

their home addresses directly to the Uptown area. The difference between the VMT results of

these two models will give some indication as to the extent of impact that park-and-ride lots are

having in the Charlotte area in reducing or increasing VMT. When calculating emissions

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amounts, both estimates will also account for hot soak and cold start to estimate the difference in

total vehicle emissions between the two scenarios.

To determine the origin and destination of park-and-ride commuters in order to build a

network distance for these park-and-ride commuters, two variables must be used in the study,

including the home TAZ of park-and-ride users (origin) and the exact TAZ of the light rail park-

and-ride lots as well as work locations (destination). The origin-destination pair forms a matrix

by which a network distance between points can be estimated yielding the amount of additional

VMT being created by these trips. The network distance data provided by CDOT that are used to

estimate the distance between origin and destination pairs are based on numerous model

coefficients that were calculated by CDOT. These coefficients are in-vehicle travel time, out-of-

vehicle travel time, initial wait time, transfers for transit and drive access time, auto time, auto

costs (in cents), auto distance, income, and HOV savings for automobile. The data provided by

CDOT is an estimated TAZ Origin-Destination distance matrix for the Charlotte metropolitan

area. Light rail park-and-ride address locations will be collected from CATS and used to

determine the stations’ TAZ location for the first scenario.

Though this estimate is a simple and effective way of measuring the effects of light rail

park-and-ride facilities on VMT and emissions, it does have many problems and assumptions

affiliated with it. The study is expanded to capture all park-and-ride users by using the Combined

ExpFactor Linked field in the survey. This estimates how many people are actually using park-

and-ride lots for every one survey respondent. The estimates also assume their route is fixed

from origin to final destination with no intermediate stops. In addition, because the estimate uses

a national coefficient to estimate the amount of emissions from hot soak and cold start, it is also

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an assumption that driving behavior and the types of automobile being used by park-and-ride

users are similar to the national average.

Coefficient national averages provided by the EPA are essential in measuring the amount

of vehicle emissions being given off by park-and-ride users for the purpose of this research.

There are two variables that will be used to measure the environmental impacts of park-and-ride

lots. The first is hot soak, which will measure the amount of evaporative emissions being given

off as cars sit idly in park-and-ride lots as well as cool down evaporative emissions. All stations

except the I-485 station, which is an enclosed parking deck, will measure the affects of hot soak

over a nine hour period. Since the I-485 station has an enclosed parking deck, cars parked here

will be excluded from the total nine hours and hot soak will only be measured for one hour, or

the amount of time it takes a car to cool down. It will be assumed that all of the persons using

park-and-ride lots from the survey are working a normal nine-hour day. It will also be assumed

that it is a non-cloudy day conducive for hot soak emissions which will give an overestimated

hot soak result for the average day in a year. Finally it is assumed that the amount of evaporative

emissions in Charlotte surface lots is similar to the national average since the estimate will be

using a national coefficient to help determine the amount of hot soak. In the case of cold start, it

will be possible to estimate vehicle emissions based on trip length using an EPA national cold

start coefficient as well as the national average age of a vehicle which is about nine years old.

Since the estimate will be using national coefficient averages and has several assumptions

attached to it, the results will not be exact but rather provide an idea as to the amount of

emissions being expelled as a result of cold start and hot soak evaporative emissions.

Additionally, the estimate assumes that cars and trucks in the Charlotte area are about the same

age as cars and trucks at the national level (i.e. nine years old).

21

As mentioned before, the EPA currently uses the MOBILE6 model and MOVES model

to calculate both start and running emissions at the national level. The MOVES model (Motor

Vehicle Emissions Simulator) was developed by the EPA office of Transportation and Air

Quality (OTAQ) and is used to estimate emissions (both start and running) for mobile sources

based on the mix of vehicles (type, year, etc.), the amount of time the vehicle has been parked,

ambient temperature of the vehicle, and the fuel the vehicle is using (EPA OTAQ website). This

model can be used at any scale (national, regional, local, etc) but is used by the EPA to

determine national emission averages for start and running emissions for each year (EPA 2009).

According to the EPA website, MOBILE6 is an emission factor model for predicting gram per

mile emissions of Hydrocarbons (HC), Carbon Monoxide (CO), Nitrogen Oxides (NOx), Carbon

Dioxide (CO2), Particulate Matter (PM), and toxics from on-road vehicles (EPA website). Both

of these models are used in calculating on-road vehicle start and running emissions and have

yielded interesting emission results (EPA website).

According to an article released by the EPA, cold start emissions occur within the first

505-860 seconds of driving or roughly a length of 3.59-3.9 vehicle miles before the normal

running emissions begin (EPA 2009 and Houk 2004). Cold start occurs when an automobile has

been sitting (generally overnight) for a period of 6 hours or more (EPA 2009). According to

Houk, start emissions in the summer (mainly cold start) accounts for 28% of the total on-road

volatile organic compound (VOC), 31% of carbon monoxide (CO), and 20% of nitrogen oxides

(NOx) emissions in 2001 (Houk 2004). In the winter, start emissions account for around 50% of

all CO emissions (Houk 2004). Start emissions (particularly cold start) will be major concerns in

this study since typical trips made by park-and-ride users are expected to be much shorter than if

those same users travelled directly into the CBD instead, especially in the wintertime. One major

22

assumption as expressed in the Houk article is that the MOBILE6 vehicle emissions modeler

assumes that all morning commute trips to work are calculated as a single start from the home

end and does not account for any stops a commuter may have on the way to work for breakfast

or coffee and does not account for multiple starts in the morning. According to the EPA, start

emissions and running emissions vary greatly from year to year for all on-road vehicles in the

US. In 2000, the average car in the US emitted 10.24 grams of CO, 1.294 grams of HC

(Hydrocarbons), and 0.983 grams of NOx during the start phase (cold start phase) for a total of

12.517 grams in emissions (EPA 2009). In 2010, the average car emitted only 3.65 grams of CO,

0.423 grams of HC, and 0.119 grams of NOx for a total of 4.192 grams in start emissions, a

reduction of 8.325 grams in emissions during the start phase or 66.5% (EPA 2009). However,

since the average age of a vehicle in the US is 9.4 years old, it is assumed that the current start

emissions for all vehicles on the road will be equivalent to the start and running emission rate of

a car produced in 2001 (RITA 2009).

According to the EPA, the average car built in 2001 emits 0.2196 grams of cumulative

evaporative HC emissions in a nine hour period in 2001 (EPA 2001). This rate will be applied to

all of the park-and-ride users except the I-485 station users where evaporative HC emissions

were calculated for a one hour cool down time. In 2001, the average start emissions for an LDV

(light duty vehicle or car) were 8.86 grams of CO, 0.932 grams of HC, and 0.672 grams NOx.

The average running emissions for an LDV in 2001 per mile was 0.579 grams of CO per mile,

0.016 grams of HC per mile, and 0.080 grams of NOx per mile. The amount of start and running

emissions for cars in this study represents a lower bound emissions estimate. The study is

repeated for all LDTs (light duty trucks) to show an upper end of emission results, as these

vehicles have much higher start and running emissions rates than LDVs. Among LDTs in 2001

23

the average start emissions were 14.15 grams of CO, 1.45 grams of HC, and 1.12 grams NOx.

The average running emissions for an LDT in 2001 were 0.848 grams of CO per mile, 0.0486

grams of HC per mile, and 0.144 grams of NOx per mile. The results below assume a total of

8.86 grams of CO, 0.932 grams of HC, and 0.672 grams of NOx for all car (LDV) trips (start

emissions) and 14.15 grams of CO, 1.45 grams of HC, and 1.12 grams of NOx for all truck

(LDT) trips. Following this, the running emissions rate is added on a per mile rate for all trips

over 3.90 miles (after the cold start period). The true amount of emissions for the Charlotte is

likely to be somewhere between the lower bound car (LDV) estimate and the upper bound truck

(LDT estimate).

24

Chapter 4: Model Analysis

4.1 Results of the Analysis (VMT)

The first step of the analysis was to determine an estimate VMT for two separate

scenarios. The estimates take into account the return trip for each park-and-ride user as a separate

trip from the origin – destination trip and assume a similar trip path on the return trip. One

scenario indicates the existing conditions whereby park-and-ride users drove from their origin

TAZ to the nearest light rail park-and-ride stations (park-and-ride TAZ). The other scenario

indicates pre-existing light rail conditions whereby all potential park-and-ride users drove all the

way to their final destination TAZ. There are two sections of estimated results including the

existing home-station VMT followed by the pre-existing home-final destination VMT. Each

section will have two subset tables of results, one of which will indicate the estimated VMT for

the 309 park-and-ride survey users, followed by the estimated VMT results for all park-and-ride

users based on the Combined ExpFactor Linked field from the 2009 On-board User Survey.

Indicated below in the first set of data results is the estimated VMT for the 304 park-and-

ride survey respondents for the home – station (existing conditions) scenario and 309 survey

respondents for the home – final destination (hypothetical) scenario for the current existing

home-light rail park-and-ride lot conditions. Since all interzonal (e.g. trips made from within the

same TAZ) are excluded and there were slightly more interzonal trips made in the home – station

scenario, there is a slight difference in the survey size. The estimated VMT results are broken up

by station and then indicated for all stations combined (one indicating all light rail stations

combined and one indicating all park-and-ride stations combined). Most of these trips were

headed to the CBD as well as some trips ending further along the light rail line but outside of the

25

CBD as final point of station departure. Also indicated is the estimated minimum to maximum

VMT for the 309 park-and-ride users for each park-and-ride station as well as all park-and-ride

stations and all the light rail stations combined. Finally, a figure indicating the average VMT for

each station, VMT for all park-and-ride stations, and VMT for all stations will be shown in the

table below.

Table 1: Estimated VMT for 2009 Park-and-Ride User Survey Respondents from Home TAZ to

nearest Park-and-Ride TAZ (existing conditions):

Home Station Records Mean VMT Min-Max VMT

I-485 Station 165 8.3445 0.58 – 25.46

Sharon Road West Station 30 5.6808 1.06 – 24.76

Arrowood Station 30 6.6307 0.74 – 24.63

Archdale Station 4 1.6240 1.27 – 2.57

Tyvola Station 22 4.3961 0.79 – 25.69

Woodlawn Station 14 3.7559 0.45 – 27.83

Scaleybark Station 29 6.9538 0.45 – 34.08

Non-Park-and-Ride Stations 10 6.7018 1.04 – 30.51

All Stations 304 7.1457 0.45 – 34.08

All Park-and-Ride Stations 294 7.1608 0.45 – 34.08

CDOT 2009 On-Board User Survey and Network OD Matrix

Figure 2: Average VMT by Station, All Stations and All Park-and-Ride Stations:


0123456789

26

The results in the table 1 and figure 2 above indicate the average and minimum–

maximum estimated VMT for the 304 survey respondents for each park-and-ride station, all

park-and-ride stations combined, and all stations combined (including all non-park-and-ride

stations). There were 10 survey respondents located at non-park-and-ride lots which are assumed

to be the remaining TOD stations further up the line. It is likely that these survey respondents

drove to a differenct parking facility near these TOD stations that were not official CATS park-

and-ride facilities. The overall average per person estimated VMT is approximately 7.15 miles

for all stations and 7.16 miles for all park-and-ride stations among the 304 park-and-ride survey

respondents. The minimum distance traveled was an estimated 0.45 miles located at Woodlawn

Station and Scaleybark and the longest was an estimated 34.08 miles located at Scaleybark

Station. The station with the highest average estimated VMT was the I-485 station at

approximately 8.34 miles and the lowest was Archdale station with an average estimated trip

being 1.62 miles from home to station. The I-485 station made up the largest share of users

among the survey respondents, accounting for 165 survey respondents or about 54 percent of the

survey sample.

After completing the analysis for the survey sample for all park-and-ride users going

from the home origin TAZ to the station destination TAZ (survey sample existing conditions),

the analysis was then expanded using the Combined ExpFactor Linked field in the survey which

aims to estimate VMT for all park-and-ride users by expanding each survey respondent to match

the actual number of park-and-ride users they represent. Indicated below is a table and figure

indicating the estimated average and minimum–maximum VMT for all estimated park-and-ride

users in a single day broken down by park-and-ride lot as well as all stations and all park-and-

ride stations combined. After using the Combined ExpFactor Linked field from the 2009 On-

27

Board User Survey, there are an estimated 4,642 park-and-ride users on an average given day

represented by the survey sample of 304 respondents. The results take into account the return trip

as a separate trip and assume a similar trip path was made on the return trip.

Table 2: Estimated VMT for All Park-and-Ride Users in a Given Day from Home TAZ to

nearest Park-and-Ride TAZ (existing conditions):


I-485 Station 2260.13 8.2777 0.58 – 25.46

Sharon Road West Station 545.06 5.2129 1.06 – 24.76

Arrowood Station 437.60 5.6458 0.74 – 24.62

Archdale Station 72.41 1.9417 1.28 – 2.58

Tyvola Station 317.40 3.3745 0.79 – 25.69

Woodlawn Station 434.09 4.3746 0.45 – 27.83

Scaleybark Station 410.77 6.9014 0.45 – 34.08

Non-Park-and-ride Stations 164.03 6.1404 1.04 – 30.51

All Stations 4641.50 6.2678 0.45 – 34.08

All Park-and-ride Stations 4477.46 6.2722 0.45 – 34.08


Figure 3: Average VMT by Station, All Stations and All Park-and-Ride Stations:


0

2

4

6

8

10

28

The results in the table 2 and figure 3 above indicate the average and minimum–

maximum estimated VMT for all the estimated 4,642 park-and-ride users for each park-and-ride

station, all park-and-ride stations combined, and all stations combined (including all non-park-

and-ride stations). The overall average per person estimated VMT is approximately 6.27 miles

for all stations and for all park-and-ride stations among the estimated 4,642 park-and-ride users.

This is slightly lower than the estimated 7.16 miles for the survey respondents indicating that the

average overall park-and-ride user is travelling less than the average survey user. The minimum

distance traveled was an estimated 0.45 miles located at Woodlawn Station and Scaleybark

Station and the longest was an estimated 34.08 miles located at Scaleybark Station. The station

with the highest average estimated VMT was the I-485 station at approximately 8.28 miles and

the lowest was Archdale station with an average estimated trip being 1.94 miles from home to

station. The I-485 station made up the largest share of users among all estimated park-and-ride

users accounting for 2,260 users or about 49 percent of all estimated users.

The estimated results from table 2 provide a much clearer picture of the true amount of

VMT incurred on an average day by the estimated daily number of light rail park-and-ride users.

The approximate total VMT calculated for the current existing conditions for all light rail park-

and-ride users is roughly 30,973 miles travelled out of approximately 15,962,423 (EPA 2003) for

Charlotte as a whole in a given day or about 0.2 percent of all VMT in a given day. According to

a CATS 2009 survey, approximately 56 percent of all trips made on the light rail are home-based

work trips (either traveling from home to work or work to home as trip type) (CATS 2009). If we

assume that 56 percent of park-and-ride users are also using light rail park-and-ride as a means of

transportation then an estimated 2,600 park-and-ride users are using park-and-ride as a mode of

commuting. In 2010, about 70,000 – 75,000 people worked in the Charlotte CBD (Charlotte

29

Center City Partners), so this makes up roughly 3.5 – 3.7 percent of all mode choice types for

CBD workers assuming that all people who commute by park-and-ride work in the CBD.

The second part of the analysis was to estimate the hypothetical pre-light rail conditions

in which VMT is estimated for all potential park-and-ride trips that are now redirected to their

final destination most typically in the CBD but also along the light rail line in neighborhoods

such as Southend. The amount of VMT was first estimated for the survey respondents and then

expanded to include the estimated total number of potential park-and-ride users using the

Combined ExpFactor Linked Field from the 2009 User Survey. Seen below are a table and figure

for all those survey respondents who would have used park-and-ride stations (existing) but

instead travelled directly to their final destination (hypothetical). The survey sample size is

slightly larger for the home – final destination scenario with 309 survey respondents than the

home – station scenario which included 304 survey respondents. The estimated results are

broken up in a fashion similar to the home-station results in which estimated results are indicated

for each park-and-ride station, all stations combined, and all park-and-ride stations combined.

The stations in this case are same affiliating stations as the survey users in the home – station

scenario but do not represent the final destination of the user. The final destination is most

typically found further along the line or in the CBD. Also shown below is a figure which shows

the average amount of VMT for this scenario for each category.

30

Table 3: Estimated VMT for 2009 Park-and-Ride User Survey Respondents from Home TAZ to

Final Destination TAZ (pre-existing conditions):


I-485 Station 165 17.6185 0.35 – 35.79

Sharon Road West Station 30 13.4896 8.42 – 22.35

Arrowood Station 30 12.5320 0.42 – 22.67

Archdale Station 5 7.3353 3.70 – 9.80

Tyvola Station 23 10.0035 6.75 – 26.18

Woodlawn Station 15 7.5512 4.57 – 19.16

Scaleybark Station 31 8.2579 1.63 – 31.26

Non-Park-and-Ride Stations 10 7.7719 1.31 – 29.06

All Stations 309 14.2442 0.35 – 35.79

All Park-and-Ride Stations 299 14.4606 0.35 – 35.79


Figure 4: Average VMT by Station, All Stations and All Park-and-Ride Stations (hypothetical):


02468

101214161820

31

The results in the table 3 and figure 4 above indicate the average and minimum –

maximum estimated VMT for the 309 park-and-ride survey respondents for each park-and-ride


and-ride stations) for the hypothetical home – final destination scenario. The overall average per

person estimated VMT is approximately 14.24 miles for all stations and 14.46 for all park-and-

ride stations among the 309 potential park-and-ride users. This is an estimated 99 percent higher

than in the first scenario (existing conditions) for all stations combined and an estimated 102

percent higher than all park-and-ride stations combined. The minimum distance traveled was an

estimated 0.35 miles located at the I-485 station and the longest was an estimated 35.79 miles

located at I-485 Station as well. The short trip distance of 0.35 miles in the I-485 example

indicates that this park-and-ride user lived close to the center city (Origin TAZ of 10010) and

their final destination (destination TAZ of 10009) and chooses to drive to the I-485 station

instead of a closer station according to the survey results which are a very rare situation. This is

further substantiated in the survey as only about two additional people made a similar trip if you

expand the survey using the Combined ExpFactor Linked field. The station with the highest

average estimated VMT was the I-485 stations at approximately 17.62 miles and the lowest was

Archdale station with an average estimated trip being 7.34 miles from home to final non-station

destination.

Indicated in the figure below is the average estimated VMT for all station categories

(each individual station, all stations combined and all park-and-ride stations combined) between

the existing conditions scenario (home-station) and the second hypothetical scenario (home-final

destination) among survey respondents. Also seen is a table showing the percent increase in

VMT when switching trip type from scenario one to scenario two. The amount of VMT increase

32

ranged from 16 to 351 percent. The overall average increase in VMT when switching from the

home – park-and-ride scenario to the hypothetical home – final destination scenario for all

stations was an estimated 99 percent or double the amount of VMT. Average VMT tended to

steadily decrease from station to station in the home – final destination scenario starting with the

station with the highest estimated VMT (I-485) until reaching all non-park-and-ride lots which

had the second lowest average VMT.

Figure 5: Absolute Change in VMT between Scenario 1 (Home-Station) and Scenario 2 (Home-

Final Destination) among Survey Respondents:


0

2

4

6

8

10

12

14

16

18

20

Scenario 1

Scenario 2

33

Table 4: Absolute and Percent Changes in overall VMT among different Station Categories

(Survey Respondents)*:

Station Absolute Percentage

I-485 Station +9.3 111.1 %

Sharon Road West Station +7.8 137.5 %

Arrowood Station +5.9 89.0 %

Archdale Station +5.7 351.7 %

Tyvola Station +5.6 127.6 %

Woodlawn Station +3.8 101.0 %

Scaleybark Station +1.3 18.8 % Non-Park-and-Ride Stations +1.2 16.0 %

All Stations +7.2 99.3 % All Park-and-Ride Stations +7.3 101.9 %


*This represents the percent and absolute per user VMT change in VMT by station for the survey

respondents when they travel to their final destination entirely by car instead of travelling part of

the way by light rail

The final part of the VMT section of the analysis estimates the VMT for all potential

park-and-ride users based on the survey sample as they travel in a hypothetical trip from home to

their final destination by using the Combined ExpFactor Linked field. The table and figure below

indicates the estimated average amount of VMT and minimum – maximum VMT categorized by

individual station, all stations combined, and all park-and-ride stations combined. After using the

Combined ExpFactor Linked Field, there was an estimated 4,767 potential park-and-ride users

(pre-existing conditions) represented by the sample size of 309 in an average day. Also

presented below is a figure showing the difference in overall estimated VMT between the survey

sample and the total estimated potential park-and-ride users in the hypothetical pre-light rail

scenario.

34

Table 5: Estimated VMT for all potential 2009 Park-and-Ride Users from Home TAZ to Final

Destination TAZ (pre-existing conditions):


I-485 Station 2260.13 17.7195 0.35 – 35.79

Sharon Road West Station 545.06 13.0116 8.42 – 22.35

Arrowood Station 429.56 12.1475 0.42 – 22.67

Archdale Station 82.96 7.7155 3.70 – 9.80

Tyvola Station 357.77 8.8240 6.75 – 26.18

Woodlawn Station 475.23 6.6698 4.67 – 19.16

Scaleybark Station 452.62 7.7643 1.63 – 31.26

Non-Park-and-Ride Stations 164.03 7.1270 1.31 – 29.06

All Stations 4767.37 13.4264 0.35 – 35.79

All Park-and-Ride Stations 4603.34 13.6509 0.35 – 35.79


Figure 6: Average VMT by Station, All Stations and All Park-and-Ride Stations (hypothetical):


0

2

4

6

8

10

12

14

16

18

20

35

Figure 7: Difference in VMT between Survey Sample all Users for Hypothetical Scenario:


The results in the table and figures above indicate the average and minimum – maximum

estimated VMT for all the estimated 4,767 potential park-and-ride users for each park-and-ride


and-ride stations) in the hypothetical home – final destination trip type scenario. The overall

average per person estimated VMT is approximately 13.43 miles for all stations and 13.65 miles

for all park-and-ride stations among the estimated 4,767 potential park-and-ride users. This is

slightly lower than the estimated 14.24 miles for all park-and-ride home stations and 14.46 miles

for all home stations for the survey respondents, indicating that the average overall potential

park-and-ride user is travelling less than the average survey user. The minimum distance traveled

was an estimated 0.35 miles located at I-485 Station and the longest was an estimated 35.79

miles located at the I-485 Station as well. The station with the highest average estimated VMT

was the I-485 station at approximately 17.72 miles and the lowest was Archdale station with an

0

2

4

6

8

10

12

14

16

18

20

Survey

All

36

average estimated trip being 7.72 miles from home to station. The I-485 station made up the

largest share of users among all estimated park-and-ride users, accounting for 2,260 users or

about 47 percent of all estimated users.

The estimated results from table 5 provide a much clearer picture of the true amount of

VMT incurred in an average day by the estimated daily number of potential light rail park-and-

ride users. The approximate total VMT calculated for the hypothertical home – final destination

scenario for all light rail park-and-ride users is roughly 64,009 miles travelled (accounting for

round-trips) out of approximately 15,962,423 for Charlotte as a whole in a given day, or about

0.4 percent of all VMT in a given day, and twice the amount of VMT as the first home-station

scenario. This is a decrease of roughly 33,036 in VMT from the home – station scenario value,

which were 30,973 in VMT. Indicated in the figure below is the average estimated VMT for all

station categories between the existing conditions scenario (home-station) and the second

hypothetical scenario (home-final destination) among all estimated park-and-ride users after

using the Combined ExpFactor Linked field. Also included is a table showing the percent

increase in VMT when switching trip type from scenario one to scenario two. The amount of

VMT increase ranged from 12 to 297 percent. The overall average increase for all stations was

slightly sharper than the increase among survey respondents with a 114 percent increase (slightly

more than double). The absolute change decreases from roughly nine at I-485 to only one mile at

Scaleybark.

37

Figure 8: Absolute Change in VMT between Scenario 1 (Home-Station) and Scenario 2 (Home-

Final Destination) for all estimated Park-and-Ride Users in a given day:


Table 6: Absolute and Percent Changes in overall VMT among different Station Categories (All

Estimated Users):

Station Absolute Percentage

I-485 Station 9.4 114.1

Sharon Road West Station 7.8 149.6

Arrowood Station 6.5 115.2

Archdale Station 5.8 297.4

Tyvola Station 5.4 161.5

Woodlawn Station 2.3 52.5

Scaleybark Station 0.9 12.5

Non-Park-and-Ride Stations 1.0 16.1

All Stations 7.2 114.2

All Park-and-Ride Stations 7.4 117.6


0

2

4

6

8

10

12

14

16

18

20

Scenario 1

Scenario 2

38

4.3 Results of the Analysis (Emissions)

As mentioned the metholodgy section, cold start, which is defined as higher emissions

per mile travelled in the first few miles of driving after a vehicle is initially started, is a major

concern for shorter trips. Therefore, the emissions emitted from a vehicle are highest in the first

few miles (or usually during the first 505 seconds) at the start of a trip than the rest of the trip.

The result is higher emissions per VMT in the second model as a result of driving shorter trips.

The EPA states the cold start emissions phase occurs in the first 3.9 miles of travel after initially

starting the car after a soak period of at least six-seven hours (EPA 2009). After traveling the

initial 3.9 miles, a vehicle switches from the start emissions phase to the running emissions

phase, which is generally much lower.

As mentioned in the methodology, a second measure that increases emissions is hot soak,

in which cars left sitting all day in an open parking space create evaporative emissions. Hot soak

will be measured and estimated at an hourly rate for a typical nine hour work day for all vehicles

parked in park-and-ride stations except the I-485 station which is a covered deck and not

exposed to the sun. Since hot soak does occur during vehicle cool down, the rate of hot soak

emissions will be applied for one hour (average vehicle cool down time) for all vehicles parked

at the I-485 station to estimate real life conditions as closely as possible. The following table and

figures indicate estimated total and average amount of emissions by emission type including CO

emissions, HC emissions, and NOx emissions using the Combined ExpFactor Linked field as a

weight in the home – station current conditions. This represents total and average per user

emission amount for all users by emission type on all trips as they go from the home TAZ to

their affiliating station TAZ. As mentioned in the methodology section, the 2001 model year was

39

used to estimate vehicle emissions since the average age of a vehicle in the US is approximately

nine years old. Two types of vehicles are represented including cars (LDV’s) and trucks (LDT’s)

representing lower and upper estimates for each emission type. The HC emission results below

do not include hot soak HC emissions. Those results are inidicated at the final part of the

emissions section.

Table 7: Total, per user and per mile emissions by type of emission from home to station using

the 2001 model year as a base for start and running emissions rate (all Park-and-ride users):

2001 CO Emissions 2001 HC Emissions 2001 NOx Emissions

TOTAL Home-Station Grams TOTAL Home-Station Grams TOTAL Home-Station Grams

Car (LDV) 45,374.639 Car 4,586.623 Car 4,422.832

Truck (LDT) 71,903.133 Truck 7,522.194 Truck 7,545.219

PER MILE* PER MILE PER MILE

Car 1.465 Car 0.148 Car 0.143

Truck 2.321 Truck 0.243 Truck 0.244

PER PandR USER PER PandR USER PER PandR USER

Car 9.776 Car 0.988 Car 0.953

Truck 15.491 Truck 1.621 Truck 1.626 Source: EPA 2009 and CDOT 2009

*Per Mile estimates of emissions are usefull since they can give some indication of emissions for

varying trip distances on average.

40

Figure 9: Total CO Emissions, CO Emissions per Mile, and CO Emissions per User in grams for

a 2001 average age vehicle for both LDV and LDT Vehicle Types:

Source: EPA 2009 and CDOT 2009

0.00

20,000.00

40,000.00

60,000.00

80,000.00

Car (LDV) Truck (LDT)

Total 2001 CO Emissions (grams)

Total 2001CO Emissions(grams)

0.00

0.50

1.00

1.50

2.00

2.50


2001 CO Emissions Per Mile (grams)

2001 COEmissions PerMile (grams)

0.00

5.00

10.00

15.00

20.00


2001 CO Emissions Per User (grams)

2001 COEmissions PerUser (grams)

41

Figure 10: Total HC Emissions, HC Emissions per Mile, and HC Emissions per User in grams

for a 2001 average age vehicle for both LDV and LDT Vehicle Types:


0.00

2,000.00

4,000.00

6,000.00

8,000.00


Total 2001 HC Emissions (grams)

Total 2001 HCEmissions(grams)

0

0.05

0.1

0.15

0.2

0.25

0.3


2001 HC Emissions Per Mile (grams)

2001 HCEmissions PerMile (grams)

0

0.5

1

1.5

2


2001 HC Emissions Per User (grams)

2001 HCEmissions PerUser (grams)

42

Figure 11: Total NOx Emissions, NOx Emissions per Mile, and NOx Emissions per User in

grams for a 2001 average age vehicle for both LDV and LDT Vehicle Types:


0.00

2,000.00

4,000.00

6,000.00

8,000.00


Total 2001 NOX Emissions (grams)

Total 2001 NOXEmissions(grams)

0

0.1

0.2

0.3


2001 NOX Emissions Per Mile (grams)

2001 NOXEmissions PerMile (grams)

0

0.5

1

1.5

2


2001 NOX Emissions Per User (grams)

2001 NOXEmissions PerUser (grams)

43

It is estimated that among cars (LDV’s) the total estimated CO emission amount was

45,375 grams (lower range) and for trucks (LDT’s) it is estimated to be 71,903 grams (upper

range) for all 4,642 park-and-ride users on an average trip from home to the nearest station. In

terms of per mile CO emissions, that translates to 1.465 grams of CO per mile for cars and 2.321

grams of CO per mile for trucks. In terms of per user CO emissions, the value is roughly 9.775

grams of CO per user for cars and 15.491 grams of CO per user for trucks. The total estimated

amount of HC emissions in the home-station (existing conditions) scenario is estimated to be

4,587 grams in the case of cars and 7,522 grams in the case of trucks. In terms of per mile HC

emissions, this translates to roughly 0.148 grams of HC for cars and 0.243 grams of HC for

trucks. In terms of per user HC emissions the value is estimated to be 0.988 grams for cars and

1.621 grams for trucks. Finally, the estimated total amount of NOx emissions for a vehicle with

an average age of nine years is estimated to be 4,423 grams of NOx for cars and 7,545 grams of

NOx for trucks. In terms of per mile, this is roughly 0.143 grams of NOx per mile for cars and

0.244 grams of NOx per mile for trucks. In terms of per user, the value is estimated to be 0.953

grams of NOx per user for cars and 1.626 grams of NOx per user for trucks.

The following table and figures indicate the total and average amount (per user) of

emissions for each emission type for each park-and-ride station and all non-park-and-ride

stations combined in the home – station scenario. This intends to show the variation in estimated

total emissions and emissions per user for each station. Once again, the amount of emissions will

be broken up by cars (LDV’s) and trucks (LDT’s) to indicate a lower and upper bound for the

emission estimate for each emission type. Total amount of users is indicated in parentheses.

44

Table 8: Total and Average per User CO Emissions assuming Cars and Trucks for each Park-

and-Ride Station (Home – Station):

Station (Total Users)

Total CO Emissions (Car)

CO Emissions Per User (Car)

Total CO Emissions (Truck)

CO Emissions Per User (Truck)

I-485 Station (2,260) 22,381.20 9.90 35,432.09 15.68

Sharon Road West Station (545) 5,110.34 9.38 8,124.31 14.91

Arrowood Station (438) 4,099.37 9.37 6,517.51 14.89

Archdale Station (72) 641.56 8.86 1,024.62 14.15

Tyvola Station (317) 2,862.17 9.02 4,564.45 14.38

Woodlawn Station (434) 4,323.09 9.96 6,841.05 15.76

Scaleybark Station (411) 4,336.08 10.56 6,832.72 16.63

Non-Park-and-Ride Stations (164) 1,620.84 9.88 2,566.39 15.65


Figure 12: Total CO Emissions for Cars and Trucks for each Station:


0.00

5,000.00

10,000.00

15,000.00

20,000.00

25,000.00

30,000.00

35,000.00

40,000.00



45

Figure 13: Average per User CO Emissions assuming Cars and Trucks for Each Station:


0.00

2.00

4.00

6.00

8.00

10.00

12.00

14.00

16.00

18.00



46

Table 9: Total and Average per User HC Emissions for Cars and Trucks for each Park-and-Ride

Station (Home – Station):


Total HC Emissions (Car)

HC Emissions Per User (Car)

Total HC Emissions (Truck)

HC Emissions Per User (Truck)

I-485 Station (2,260) 2,272.78 1.01 3,782.44 1.67

Sharon Road West Station (545) 527.51 0.97 849.60 1.56

Arrowood Station (438) 426.03 0.97 689.78 1.58

Archdale Station (72) 67.49 0.93 105.00 1.45

Tyvola Station (317) 301.31 0.95 476.92 1.50

Woodlawn Station (434) 420.32 0.97 677.27 1.56

Scaleybark Station (411) 410.30 1.00 679.03 1.65

Non-Park-and-Ride Stations (164) 160.88 0.98 262.17 1.60


Figure 14: Total HC Emissions for Cars and Trucks for each Station:


0.00

500.00

1,000.00

1,500.00

2,000.00

2,500.00

3,000.00

3,500.00

4,000.00



47

Figure 15: Average per User HC Emissions for Cars and Trucks for Each Station:


0.00

0.20

0.40

0.60

0.80

1.00

1.20

1.40

1.60

1.80



48

Table 10: Total and Average per User NOx Emissions for Cars and Trucks for each Park-and-

Ride Station (Home – Station):


Total NOx Emissions (Car)

NOx Emissions Per User (Car)

Total NOx Emissions (Truck)

NOx Emissions Per User (Truck)

I-485 Station (2,260) 2,350.49 1.04 4,028.38 1.78

Sharon Road West Station (545) 463.82 0.85 786.04 1.44



Tyvola Station (317) 240.77 0.76 404.94 1.28





Figure 16: Total NOx Emissions for Cars and Trucks for each Station:


0.00

500.00

1,000.00

1,500.00

2,000.00

2,500.00

3,000.00

3,500.00

4,000.00

4,500.00

Total NOX Emissions (Car)

Total NOX Emissions (Truck)

49

Figure 17: Average per User NOx Emissions for Cars and Trucks for Each Station:


According to the results above, the station reporting the highest amount of emissions for

each emission type was the I-485 station, which also had the highest amount of users and VMT.

Total CO emissions for cars (LDV’s), which represents a lower bound emission limit for this

study, ranged from an estimated 642 grams of CO emissions at the Archdale park-and-ride

station to 22,381 grams at the I-485 station. Total CO per user for cars ranged from 8.86 grams at

the Archdale station to 10.56 grams at the Scaleybark station. Interestingly, the stations with the

highest amount of CO emissions were those further along the light rail line located closer to the

CBD and stations on both ends of the line tended to be higher in terms of per user average CO

0.00

0.20

0.40

0.60

0.80

1.00

1.20

1.40

1.60

1.80

2.00

NOX Emissions Per User (Car)

NOX Emissions Per User (Truck)

50

emissions than the ones in the middle. In terms of HC emissions for cars, the absolute estimated

total HC emission value ranged from 68 grams at Archdale station to 2,273 grams at the I-485

station. In terms of per user HC emissions, the station with the lowest average per user HC

emission value was Archdale with 0.93 grams and the highest was the I-485 station with 1.01

grams. In terms of NOx emissions, the amount of total estimated NOx emissions for cars ranged

from 49 grams at Archdale to 2,351 grams at the I-485 station. The amount of total per user NOx

emissions for cars ranged from 0.67 grams at Archdale station to 1.04 grams at the I-485 station.

On the other end, total and average emissions of CO, HC, and NOx for trucks (LDT’s)

were substantially higher and represent an upper bound emission limit for this study. In terms of

total CO emissions, the station with the lowest overall CO emissions was Archdale station with

an estimated 1,025 grams of CO for all users while the highest overall CO emissions for any

station was the I-485 with 35,432 grams. Average per user CO emissions for trucks ranged from

14.15 grams at Archdale station to 16.63 grams at Scaleybark station. In terms of HC emissions

for trucks, the station with the lowest overall HC emission value for trucks was Archdale station

with 105 grams of HC emissions and the station with the highest was the I-485 station with an

aggregate 3,782 grams. The per user truck emission value for HC emissions ranged from 1.45

grams at Archdale station to 1.67 at the I-485 station. Finally, in terms of NOx emissions for

trucks, the total estimated emissions ranged from 81 grams at Archdale station to 4,028 grams at

the I-485 station. The per user NOx emission value for trucks ranged from 1.12 grams at

Archdale station to 1.78 grams at the I-485 station. In every emission type case, the I-485 station

accounted for about half of all user emissions as a total. In terms of per user emissions the top

three stations were Scaleybark, Woodlawn and I-485 for CO emissions and I-485, Scaleybark

and non-park-and-ride stations for HC and NOx emissions.

51

The second part of the emissions estimate analysis was to determine the

approximate amount of emissions under the pre-existing hypothetical home – final destination

trip type. The following table and figures indicate estimated total and average amount of

emissions by emission type, including CO emissions, HC emissions, and NOx emissions, using

the Combined ExpFactor Linked field as a weight in the home – station current conditions. This

represents total and average emissions for all users by emission type on all trips as they go from

the home TAZ to their nearest station TAZ. As mentioned in the methodology section, the 2001

model year was used to estimate vehicle emissions since the average age of a vehicle in the US is

approximately nine years old. Two types of vehicles are represented, including cars (LDV’s) and

trucks (LDT’s) representing lower and upper bound limit estimates for each emission type. The

HC emission results below do not include hot soak HC emissions. Those results are inidicated at

the final part of the emissions section.

Table 11: Total, per user and per mile emissions by type of emission from home to final

destination using the 2001 model year as a base for start and running emissions rate (all Park-

and-ride users):

2001 CO Emissions 2001 HC Emissions 2001 NOx Emissions

TOTAL Home-Final D. Grams TOTAL Home-Final D. Grams TOTAL Home-Final D. Grams

Car (LDV) 68,707.770 Car 5,174.624 Car 6,860.859

Truck (LDT) 106,224.436 Truck 9,134.424 Truck 11,922.390

PER MILE PER MILE PER MILE

Car 1.073 Car 0.081 Car 0.107


PER USER PER USER PER USER

Car 14.412 Car 1.085 Car 1.439



52

Figure 18: Total CO Emissions, CO Emissions per Mile, and CO Emissions per User in grams



0.000

20,000.000

40,000.000

60,000.000

80,000.000

100,000.000

120,000.000

Car (LDV) Truck(LDT)

Total 2001 CO Emissions (Grams)

Total 2001 COEmissions(Grams)

0.000

0.500

1.000

1.500

2.000


2001 CO Emissions Per Mile (Grams)

2001 COEmissions PerMile (Grams)

0.000

5.000

10.000

15.000

20.000

25.000


2001 CO Emissions Per User (Grams)

2001 COEmissions PerUser (Grams)

53

Figure 19: Total HC Emissions, HC Emissions per Mile, and HC Emissions per User in grams



0.000

2,000.000

4,000.000

6,000.000

8,000.000

10,000.000


Total 2001 HC Emissions (Grams)

Total 2001 HCEmissions(Grams)

0.000

0.050

0.100

0.150


2001 HC Emissions Per Mile (Grams)

2001 HCEmissions PerMile (Grams)

0.000

0.500

1.000

1.500

2.000

2.500


2001 HC Emissions Per User (Grams)

2001 HCEmissions PerUser (Grams)

54

Figure 20: Total NOx Emissions, NOx Emissions per Mile, and NOx Emissions per User in

grams for a 2001 average age vehicle for both LDV and LDT Vehicle Types:


0.000

5,000.000

10,000.000

15,000.000

Car (LDV) Truck(LDT)

Total 2001 NOX Emissions (Grams)

Total 2001 NOXEmissions(Grams)

0.000

0.050

0.100

0.150

0.200


2001 NOX Emissions Per Mile (Grams)

2001 NOXEmissions PerMile (Grams)

0.000

0.500

1.000

1.500

2.000

2.500

3.000


2001 NOX Emissions Per User (Grams)

2001 NOXEmissions PerUser (Grams)

55

It is estimated that among cars (LDV’s) the total estimated CO emission amount was

68,708 grams (lower range) and for trucks (LDT’s) it is estimated to be 106,224 grams (upper

range) for all 4,767 park-and-ride users on an average trip from home to final destination. In the

first home – station scenario, the aggregate CO emissions were 45,375 for cars and 71,903 grams

for trucks. In the home – final destination scenario (pre-light rail), the aggregate CO emission

amount was 34 percent higher for cars and 32 percent higher for trucks. In terms of per mile

traveled, the value is approximately 1.073 grams of CO per mile for cars and 1.660 grams of CO

per mile for trucks. This is roughly a 36 percent reduction for cars and a 40 percent reduction for

trucks in grams per mile from the first home – station scenario, indicating the strong per mile

impact of CO emissions for the shorter average trip length in the home – station scenario versus

the home – final destination scenario. In terms of per user from home – final destination, the

emissions value is roughly 14.412 grams of CO per user for cars and 22.282 grams of CO per

user for trucks, a 32 percent and 31 percent increase for cars and trucks, respectively, from the

first home – station estimate per user CO emission value.

The total estimated amount of HC emissions in the home-station (existing conditions) is

estimated to be 5,175 grams of CO in the case of cars and 9,134 grams of CO in the case of

trucks. This represents an 11 percent increase for cars and an 18 percent increase for trucks from

the home – station scenario. In terms of per mile HC emissions, the value is roughly 0.081 grams

of HC for cars and 0.143 grams of HC for trucks. This represents an 83 percent decrease in cars

and a 70 percent decrease in trucks for grams of HC per mile from the first home – station

scenario. In terms of per user HC emissions, the value is an estimated 1.085 grams of HC for

cars and 1.916 grams of HC for trucks. This is roughly a 9 percent increase for cars and a 15

percent increase for trucks from the first home – station scenario. Finally, the estimated total

56

amount of NOx emissions for a vehicle with an average age of nine years is estimated to be

6,861 grams of NOx for cars and 11,922 grams of NOx for trucks for all trips in their journey

from home to final destination. This represents an estimated 36 percent increase for cars and a 37

percent increase for trucks in total NOx emissions from the first home – station scenario. In

terms of per mile NOx emissions, the value is roughly 0.107 grams of NOx per mile for cars and

0.186 grams of NOx per mile for trucks, a 33 percent decrease for cars and a 31 percent decrease

for trucks from the first home – station scenario. In terms of per user NOx emissions, the value is

estimated to be 1.439 grams of NOx per user for cars 2.501 grams of NOx per user for trucks, a

34 percent increase for cars and a 35 percent increase for trucks from the first home – station

scenario.

The following table and figures indicate the total and average amount (per user) of

emissions for each emission type for each potential park-and-ride station and all potential non-

park-and-ride stations combined in the home – final destination scenario. This intends to show

the variation in estimated total emissions and emissions per user for each potential station. Once

again, the amount of emissions will be broken up by cars (LDV’s) and trucks (LDT’s) to indicate

a lower and upper bound for the emission estimate for each emission type. Total amount of

potential park-and-ride users is indicated in parentheses.

57

Table 12: Total and Average per User CO Emissions for Cars and Trucks for each Home Park-

and-Ride Station (Home – Final Destination):

Home Station (Total Users)





I-485 Station (2,260) 38,162.32 16.89 58,545.02 25.90

Sharon Road West Station (545) 7,704.81 14.14 11,924.16 21.88

Arrowood Station (438) 5,876.82 13.68 9,111.34 21.21

Archdale Station (72) 919.54 11.08 1,444.12 17.41

Tyvola Station (317) 4,189.86 11.71 6,556.36 18.33

Woodlawn Station (475) 4,972.66 10.46 7,840.70 16.50

Scaleybark Station (411) 5,086.66 11.24 7,981.13 17.63

Non-Park-and-Ride Stations (164) 1,795.10 10.94 2,281.61 17.20


Figure 21: Total CO Emissions for Cars and Trucks for each Potential Station (Home – Final

Destination):


0.00

10,000.00

20,000.00

30,000.00

40,000.00

50,000.00

60,000.00

70,000.00



58

Figure 22: Average Per User CO Emissions for Cars and Trucks for Each Potential Station

(Home – Final Destination):


0.00

5.00

10.00

15.00

20.00

25.00

30.00



59

Table 13: Total and Average per User HC Emissions for Cars and Trucks for each Home Park-

and-Ride Station (Home – Final Destination):






I-485 Station (2,260) 2,607.65 1.15 4,799.62 2.12

Sharon Road West Station (545) 587.46 1.08 1,031.71 1.89



Tyvola Station (317) 361.63 1.01 604.39 1.69





Figure 23: Total HC Emissions for Cars and Trucks for each Potential Station (Home – Final

Destination):


0.00

1,000.00

2,000.00

3,000.00

4,000.00

5,000.00

6,000.00



60

Figure 24: Average Per User HC Emissions for Cars and Trucks for Each Potential Station



0.00

0.50

1.00

1.50

2.00

2.50



61

Table 14: Total and Average per User NOx Emissions for Cars and Trucks for each Potential

Park-and-Ride Station (Home – Final Destination):


Total NOx Emissions (Car)

NOx Emissions Per User (Car)

Total NOx Emissions (Truck)

NOx Emissions Per User (Truck)

I-485 Station (2,260) 4,024.86 1.78 7,042.24 3.12

Sharon Road West Station (545) 763.59 1.40 1,325.63 2.43



Tyvola Station (317) 381.36 1.07 645.38 1.83





Figure 25: Total NOx Emissions for Cars and Trucks for each Potential Station (Home – Final

Destination):


0.00

1,000.00

2,000.00

3,000.00

4,000.00

5,000.00

6,000.00

7,000.00

8,000.00

Total NOX Emissions (Car)

Total NOX Emissions (Truck)

62

Figure 26: Average Per User NOx Emissions for Cars and Trucks for Each Potential Station



0.00

0.50

1.00

1.50

2.00

2.50

3.00

3.50

NOX Emissions Per User (Car)

NOX Emissions Per User (Truck)

63

According to the results above, the station reporting the highest amount of emissions for

each emission type was once again the I-485 station which, as noted before, also had the highest

amount of users and VMT. Total CO emissions for cars (LDV’s), which represents a lower

bound emission limit for this study, ranged from an estimated 920 grams of CO emissions at the

Archdale park-and-ride station to 38,162 grams at the I-485 station. Total CO per user for cars

ranged from 10.46 grams at the Woodlawn station to 16.89 grams at the I-485 station. CO

emissions per user was the highest at the I-485 station and tended to decline at each ensuing

station further in toward the CBD. In terms of HC emissions for cars, the absolute estimated total

HC emission value ranged from 82 grams at Archdale station to 2,608 grams at the I-485 station.

In terms of per user HC emissions, the station with the lowest average per user HC emission

value was Woodlawn with 0.98 grams and the highest was the I-485 station with 1.15 grams. In

terms of NOx emissions, the amount of total estimated NOx emissions for cars ranged from 81

grams at Archdale to 4,025 grams at the I-485 station. The amount of total per user NOx

emissions for cars ranged from 0.96 grams at the non park-and-ride stations to 1.78 grams at the

I-485 station.

Total and average emissions of CO, HC, and NOx for trucks (LDT’s) were substantially

higher and represent an upper bound emission limit for this study. In terms of total CO emissions

for the home – final destination scenario, the station with the lowest overall CO emissions was

Archdale station with an estimated 1,444 grams of CO for all users, while the highest overall CO

emissions for any station was the I-485 station with 58,545 grams. Average per user CO

emissions for trucks ranged from 16.50 grams at Archdale station to 25.90 grams at Scaleybark

station. In terms of HC emissions for trucks, the station with the lowest overall HC emission

value for trucks was Archdale station with 136 grams of HC emissions and the station with the

64

highest was the I-485 station with an aggregate 4,900 grams. The estimated per user truck

emission value for HC emissions ranged from 1.58 grams at Woodlawn station to 2.12 grams at

the I-485 station. Finally, in terms of estimated NOx emissions for trucks, the total estimated

emissions ranged from 139 grams at Archdale station to 7,042 grams at the I-485 station. The per

user NOx emission value for trucks ranged from 1.52 grams at Woodlawn station to 3.12 grams

at the I-485 station. In terms of per user emissions, the top three stations were I-485, Sharon

Road West, and Arrowood for CO and NOx emissions and I-485, Sharon Road West, and

Woodlawn stations for HC emissions. The following figures indicate the difference between total

and average per user emissions for CO emissions for both cars (LDV’s) and trucks (LDT’s).

65

Figure 27: Aggregate CO Emissions for Cars (LDV’s) for the Home – Station Scenario and the

Home – Final Destination Scenario:


Figure 28: Average Per User CO Emissions for Cars (LDV’s) for the Home – Station Scenario

and the Home – Final Destination Scenario:


0.005,000.00

10,000.0015,000.0020,000.0025,000.0030,000.0035,000.0040,000.0045,000.00

Total CO Emissions Home -Station (Car)

Total CO Emissions Home - FinalDestination (Car)

0.002.004.006.008.00

10.0012.0014.0016.0018.00

Per User CO Emissions Home -Station (Car)

Per User CO Emissions Home -Final Destination (Car)

66

Figure 29: Aggregate CO Emissions for Trucks (LDT’s) for the Home – Station Scenario and the

Home – Final Destination Scenario:


Figure 30: Average Per User CO Emissions for Cars (LDV’s) for the Home – Station Scenario

and the Home – Final Destination Scenario:


0.00

10,000.00

20,000.00

30,000.00

40,000.00

50,000.00

60,000.00

70,000.00

Total CO Emissions Home -Station (Truck)

Total CO Emissions Home - FinalDestination (Truck)

0.00

5.00

10.00

15.00

20.00

25.00

30.00

Per User CO Emissions Home -Station (Truck)

Per User CO Emissions Home -Final Destination (Truck)

67

In the final part of the emissions analysis, hot soak was calculated for all survey users and

expanded users (using the Combined ExpFactor Linked field) of park-and-ride lots. The hot soak

time was calculated for nine hours for all surface parking lots (typical work day), and for one

hour at the I-485 parking deck, which approximates cool down time. Hot soak evaporative

emissions were calculated for Sharon Road West, Arrowood, Archdale, Tyvola, Woodlawn,

Scaleybark and non-park-and-ride (assumed to be open-air) stations for nine hours, accounting

for 144 of the 309 survey respondents or roughly 46.6 percent of all park-and-ride users in the

survey (CDOT) and one hour for the remaining 165 users of the I-485 station. Once again, cold

start emissions are evaporative emissions from cars left sitting in an open parking space during

the day (applies to all but I-485 station) as well as cool down time (applies to all stations). It is

important to note that hot soak assumes sunny conditions with no overcast, as evaporative

emissions are highest during these types of days. Based on this assumption, all cloudy days will

be excluded from the purpose of this study and an average percent based on the average number

of sunny days in a year will be used for the evaporative emissions section. According to the

NOAA National Data Center, there are an average of 152 cloudy days in Charlotte and 204 clear

or partly cloudy days (NOAA). This means that approximately 55.9 percent of all days are clear

or partly cloudy. The first part of the study will assume 100 percent sunny conditions as a base

estimate while the second part will assume that approximately 55.9 percent of hot soak emissions

are escaping into the atmposhere (based approximate existing conditions). For the purpose of this

study, the same assumption will be applied to the results calculated. According to the EPA, the

average car emitted 0.2196 grams of cumulative evaporative emissions in a nine-hour period in

2001 (EPA 2001). This rate will be applied to all of the park-and-ride users. It is also important

to note that hot soak emissions are a type of HC emissions.

68

The total amount of estimated hot soak evaporative emissions for all 144 survey users

parked in open decks is roughly 31.622 grams of HC emissions. The amount is 4.026 grams of

HC emissions for the 165 users at the I-485 station representing the cool down period only for

that station. The amount of hot soak emissions for all park-and-ride users (using the Combined

ExpFactor Linked field variable) is approximately 550.589 grams for all open air lots and 55.147

grams at the I-485 station for an estimated total of 605.736 grams of HC emissions. The total

amount of evaporative hot soak HC emissions is small when compared to the total HC cold start

and running emissions on a park-and-ride user’s trip. Based on 100 percent clear and partly

cloudy days, the total HC estimated emission value for all trips from home – station is 4,587

grams for cars and 7,522 for trucks. After including the HC emissions for hot soak, the new

estimated total is roughly 5,193 grams for cars and 8,128 grams for trucks. This means that hot

soak emissions accounted for an estimated 11.7 percent for cars (lower HC emission bound) and

7.5 percent for trucks (upper HC emission bound). Shown below is a table and figure indicating

estimated total hot soak HC emissions for each park-and-ride station and all non-park-and-ride

stations combined for 100 percent clear and partly cloudy days. The total number of users is

indicated in parentheses in figure 31.

Table 15: Total Hot Soak HC Emissions (in grams) by Station for all Park-and-Ride User’s

assuming 100 percent ideal sunny and partly cloudy weather:

Station (Users) HC Hot Soak Emissions

I-485 Station (2,260) 55.147

Sharon Road West Station (545) 119.696

Arrowood Station (430) 94.332

Archdale Station (83) 18.217

Tyvola Station (358) 78.567

Woodlawn Station (434) 104.36

Scaleybark Station (452) 99.395

Non-Park-and-Ride Stations (164) 36.021


69

Figure 31: Total Hot Soak HC Emissions (in grams) by Station for all Park-and-Ride Users



The graphs below shows the share of estimated hot soak HC emissions among all HC

emissions for both the home – station scenario and the home – final destination scenario for both

cars (LDV’s) and trucks (LDT’s) based on 100 percent ideal sunny/partly cloudy conditions.

0

20

40

60

80

100

120

140

HC Hot Soak Emissions


70

Figure 32: Total Running and HC Emissions in the Home – Station Scenario for Cars (LDV’s)



Figure 33: Total Running and HC Emissions in the Home – Station Scenario for Trucks (LDT’s)



0.00

500.00

1,000.00

1,500.00

2,000.00

2,500.00

HC hot soak

HC Runing Car

0.00500.00

1,000.001,500.002,000.002,500.003,000.003,500.004,000.004,500.00

HC hot soak

HC Running Truck

71

Figure 34: Total Running and HC Emissions in the Home – Final Destination Scenario for Cars

(LDV’s) assuming 100 percent ideal sunny and partly cloudy weather:


Figure 35: Total Running and HC Emissions in the Home – Final Scenario for Cars (LDV’s)

assuming 100 percent ideal non-cloudy sunny and partly cloudy weather:


0.00

500.00

1,000.00

1,500.00

2,000.00

2,500.00

3,000.00

HC hot soak

HC Running Car

0.00

1,000.00

2,000.00

3,000.00

4,000.00

5,000.00

6,000.00

HC hot soak

HC Running Truck

72

The second part of the estimated average amount of evaporative hot soak emissions will

assume thet 55.9 percent of the total evaporative emissions are escaping into the atmosphere in a

given day. This is based on the average number of sunny and partly cloudy days in a year in the

Charlotte region (NOAA). This is a more accurate look at the total estimated evaporative

emissions escaping into the atmosphere in a random day in Charlotte. Based on this new

estimate, the total amount of estimated hot soak evaporative emissions for all 144 survey users

parked in open decks is roughly 17.677 grams of HC emissions. The amount is 2.25 grams of HC

emissions for the 165 users at the I-485 station representing the cool down period only for that

station. The amount of hot soak emissions for all park-and-ride users (using the Combined

ExpFactor Linked field variable) is approximately 307.779 grams for all open air lots and 30.827

grams at the I-485 station for an estimated total of 338.606 grams of HC emissions. The total

amount of evaporative hot soak HC emissions is now even smaller when compared to the total

HC cold start and running emissions on a park-and-ride user’s trip. Based on 55.9 percent clear

and partly cloudy days, the total HC estimated emission value for all trips from home – station

was 4,587 grams for cars and 7,522 for trucks. After including the HC emissions for hot soak,

the new estimated total was roughly 4,926 grams for cars and 7,861 grams for trucks. This means

that hot soak emissions accounted for an estimated 6.9 percent for cars (lower HC emission

bound) and 4.3 percent for trucks (upper HC emission bound). Shown below is a table and figure

indicating estimated total hot soak HC emissions for each park-and-ride station and all non-park-

and-ride stations combined for 55.9 percent clear and partly cloudy days. The total number of

users is indicated in parentheses in figure 36.

73

Table 16: Total Hot Soak HC Emissions (in grams) by Station for all Park-and-Ride User’s

assuming 55.9 percent ideal non-cloudy weather:

Station (Users) HC Hot Soak Emissions

I-485 Station (2,260) 30.827

Sharon Road West Station (545) 66.910

Arrowood Station (430) 52.732

Archdale Station (83) 10.183

Tyvola Station (358) 43.919

Woodlawn Station (434) 58.337

Scaleybark Station (452) 55.562

Non-Park-and-Ride Stations (164) 20.136

Source: NOAA, EPA 2009 and CDOT 2009

Figure 36: Total Hot Soak HC Emissions (in grams) by Station for all Park-and-Ride Users

assuming 55.9 percent ideal non-cloudy weather:


The graphs below shows the share of estimated hot soak HC emissions among all HC

emissions for both the home – station scenario and the home – final destination scenario for both

cars (LDV’s) and trucks (LDT’s) based on 55.9 percent ideal sunny/partly-cloudy conditions.

This a better picture as to the impact hot soak HC emissions has on overall HC emissions.

01020304050607080



74

Figure 37: Total Running and HC Emissions in the Home – Station Scenario for Cars (LDV’s)

assuming 55.9 percent ideal sunny and partly cloudy weather:


Figure 38: Total Running and HC Emissions in the Home – Station Scenario for Trucks (LDT’s)

assuming 55.9 percent ideal sunny and partly cloudy weather:


0.00

500.00

1,000.00

1,500.00

2,000.00

2,500.00

HC hot soak

HC Runing Car

0.00

500.00

1,000.00

1,500.00

2,000.00

2,500.00

3,000.00

3,500.00

4,000.00

4,500.00

HC hot soak

HC Running Truck

75

Figure 39: Total Running and HC Emissions in the Home – Final Destination Scenario for Cars

(LDV’s) assuming 55.9 percent ideal sunny and partly cloudy weather:


Figure 40: Total Running and HC Emissions in the Home – Final Destination Scenario for

Trucks (LDT’s) assuming 55.9 percent ideal sunny and partly cloudy weather:


0.00

500.00

1,000.00

1,500.00

2,000.00

2,500.00

3,000.00

HC hot soak

HC Running Car

0.00

1,000.00

2,000.00

3,000.00

4,000.00

5,000.00

6,000.00

HC hot soak

HC Running Truck

76

Chapter 5: Concluding Proposals

The purpose of this thesis is to determine whether vehicle miles traveled and vehicle

emissions have increased or decreased in Charlotte as a result of light rail park-and-ride lot usage

among commuters and to what extent. The analysis of the estimates indicated an estimated 50%

drop in vehicle miles traveled among park-and-ride users by patronizing park-and-ride and light

rail for part of the trip instead of driving the entire trip. The final emissions result for the home –

final destination scenario had less of an impact per user mile due to the adverse affects of

running emissions on shorter home – station trips. Total CO emissions were reduced by an

estimated 34 percent for cars (LDV’s) and 32 percent for trucks (LDT’s) by driving to a park-

and-ride lot and using light rail instead of driving the entire trip. Aggregate NOx emissions were

reduced by 36 percent for cars and 37 percent for trucks among the estimated daily total park-

and-ride users by traveling part of the journey by light rail instead of driving directly to the final

destination. Finally, HC emissions had two factors, one accounting for HC running emissions

during the actual trip and the other accounting for hot soak emissions while the car is parked.

Accounting just for change in HC emissions for the trip alone, total HC emission was reduced by

an estimated 11 percent for cars and 18 percent for trucks among park-and-ride users by using

park-and-ride and light rail instead of driving the entire distance. However, when accounting for

hot soak and assuming park-and-ride users parked in open air parking locations at their final

destination, aggregate HC emissions decreased by 11 percent (relatively no difference between

the two estimates) for cars and decreased 20 percent for trucks among park-and-ride users by

using park-and-ride and light rail instead of driving to the final destination.

The greatest factor contributing to a higher emission rate among shorter home-station

trips was the impact of cold-start. The impact of hot soak was largely negligible, making up less

77

than about 12 percent of all car (LDV) HC emissions and 7 – 8 percent of all truck (LDT) HC

emissions. There are many assumptions equated with this study that detract from its overall

accuracy, including the use of national averages and coefficients to determine emissions and

vehicle age. In the case of emissions, the study provided emission estimates for both cars and

trucks to give a high and low range.

The true impact of reducing VMT and emissions among park-and-ride users is relatively

low due to the small amount of people using park-and-ride lots as a means of transportation into

the CBD or along the light rail line. It is estimated during the week of September 7th

through the

11th

, an average of 1,758 cars were parked at all park-and-ride stations, 960 of which were

located at the I-485/Pineville park-and-ride deck. All of the current light rail park-and-ride

facilities have a combined capacity of 3,191 parking spaces, which indicates that stations were

operating at approximately 55 percent capacity during the work week of September 7th

through

the 11th

in the middle of the day. If this sample represents the amount of people using the light

rail park-and-ride lots as a means of transportation to the final destination [incomplete sentence].

Indeed, the total VMT impact is estimated to be about 0.2 percent for the home – station

(existing conditions) scenario and 0.4 percent for the home – final destination (pre-existing

conditions) scenario of total VMT in Charlotte. This translates to roughly a 0.2 percent decrease

in aggregate VMT in Charlotte as a result of park-and-ride users choosing to patronize park-and-

ride and light rail instead of driving the entire trip. According to a CATS 2009 survey,

approximately 56 percent of all trips made on the light rail are home-based work trips (either

traveling from home to work or work to home as trip type). As a result, as suspected, the impact

would be a very slight decrease in VMT and emissions among all morning CBD commuters. The

recent on-board survey provided by the Charlotte Area Transit System and the CDOT estimated

78

how many people are actually boarding the light rail by station as of 2009 by using the

ExpFactor Linked field to expand the survey sample as well as what their average travel distance

is between origin TAZ and destination TAZ.

The final estimated VMT for the home – station scenario is about 30,973 miles and the

estimated VMT for the home – final destination scenario is roughly 64,009 miles. The absolute

VMT decrease is estimated to be about 33,036 miles travelled between both estimates and about

52 percent.

The final part of this study is intended to roughly estimate what the VMT change would

be if all park-and-ride lots were substituted with transit-oriented developments (TOD’s) for

varying scenarios. In 1995 the average VMT per person in Charlotte was roughly 21.7 miles

travelled (EPA 2003). However, it is likely that this level has increased on a per person average

basis and the overall VMT per person for Charlotte is estimated to be roughly 22.5 miles

travelled in 2010, which is similar to levels found in other southern cities including Atlanta,

Dallas, and Houston (Sorensen 2010). There are many different estimates for how much VMT

can be reduced among TOD dwellers by building a TOD around a light rail or commuter rail

stop but the averages seemed to range from 15 – 30 percent, according to the literature.

According to Ewing, doubling of densities can result in a 25 – 30 percent reduction in VMT

(Ewing 1997). Consistent with this, a report prepared by the Puget Sound Regional Council

found that by increasing densities at intersections by 10 percent resulted in a 0.5 percent decrease

in VMT (Litman 2011). According to Dill, TOD residents of Portland TOD’s saw an average

decrease of 19 – 30 percent in VMT versus the city as a whole (Dill 2004 and Dill 2006). In

another study by Ohland and Poticha, VMT was found to decrease by 43 percent among TOD

dwellers or a total of 9.80 VMT for TOD residents versus 17.34 VMT for the average resident in

79

the county as a whole. In a study by CCAP completed in 2003, it was found that the average

decline in VMT among Atlanta mixed-use projects ranged from 15 – 52 percent and that a mixed

use development located 0.1 miles away for a transit station saw an average 38 percent reduction

in VMT among residents (CCAP 2003). According to a report released by the TCRP, VMT

decreased by an average 15 – 25 percent for low density suburban areas in four case studies:

Washington DC, Philadelphia, Portland, and San Francisco (Arrington and Cervero 2008).

For the purpose of this study, ten different TOD scenarios are presented that estimate the

average VMT savings for all of the TOD residents if the seven park-and-ride facilities were

replaced with TOD’s based on average VMT reduction and housing density. The typical size of a

new TOD in Charlotte ranged from 269 units to 360 units tended to be about 300 units on

average or just over 500 residents. 300 housing units and 500 TOD residents will be used for a

lower bound density estimate with 600 units and 1,000 TOD residents being used as an upper

bound estimate. For each density level five different VMT reduction assumptions will be

estimated. The first scenario will assume a lower bound estimate shown in the literature (15

percent reduction), the second will indicate a middle-low bound estimate (20 percent reduction),

the third will show a middle-high bound (25 percent reduction), the fourth will indicate a upper

bound estimate (about 30 percent reduction), and the final will assume that the rate of VMT

reduction in Charlotte is similar to the rate of VMT reduction in Portland, Oregon among TOD

residents (around 43 percent). The following tables and figures indicate the results in estimated

VMT reductions for the ten scenarios and are based on an original non-TOD VMT per capita

estimate of 22.5 miles travelled for the average Charlotte resident.

80

Table 17: VMT reduction among TOD residents for five reduction rate scenarios for 500 people

(300 households):

VMT Reduction (%) VMT Reduction (ABS) TOD VMT Charlotte VMT

Low 15% 3.4 19.1 22.5

Middle-Low 20% 4.5 18.0 22.5

Middle-High 25% 5.6 16.9 22.5

High 30% 6.7 15.8 22.5

Portland 43% 9.8 12.7 22.5

Reduction Rate Households Population # Stations Total People

Charlotte VMT

TOD VMT

VMT Savings

Low (15%) 300 500 7 3,500 78,750 66,850 11,900

Middle-Low (20%) 300 500 7 3,500 78,750 63,000 15,750

Middle-High (25%) 300 500 7 3,500 78,750 59,150 19,600

High (30%) 300 500 7 3,500 78,750 55,300 23,450

Portland (43%) 300 500 7 3,500 78,750 44,450 34,300

Figure 41: VMT Reduction Amounts by Scenario (500 People):

0

5,000

10,000

15,000

20,000

25,000

30,000

35,000

40,000

VMT Savings (500 ppl)


81

Table 18: VMT reduction among TOD residents for five reduction rate scenarios for 1,000

people (600 households):

VMT Reduction (%) VMT Reduction (ABS) TOD VMT Charlotte VMT

Low 15% 3.4 19.1 22.5

Middle-Low 20% 4.5 18.0 22.5

Middle-High 25% 5.6 16.9 22.5

High 30% 6.7 15.8 22.5

Portland 43% 9.8 12.7 22.5

Reduction Rate Households Population # Stations Total People

Charlotte VMT

TOD VMT

VMT Savings

Low (15%) 600 1,000 7 7,000 157,500 133,700 23,800

Middle-Low (20%) 600 1,000 7 7,000 157,500 126,000 31,500 Middle-High (25%) 600 1,000 7 7,000 157,500 118,300 39,200

High (30%) 600 1,000 7 7,000 157,500 110,600 46,900

Portland (43%) 600 1,000 7 7,000 157,500 88,900 68,600

Figure 42: VMT Reduction Amounts by Scenario (500 People):

0

10,000

20,000

30,000

40,000

50,000

60,000

70,000

80,000

VMT Savings (1,000 ppl)


82

Figure 43: Differences in VMT reduction for the two density level scenarios:

According to the results above, a roughly estimated 11,900 VMT to 34,300 VMT would

be saved based on reduction rate for the 300 household scenario. A total estimate of 23,800 VMT

to 68,600 VMT would be saved based on the reduction rate for the higher density 600 household

scenario. It is likely that the average VMT decrease, based on findings in the literature, would be

roughly about 20 – 25 percent in the Charlotte context. Based on this assumption, the likely

VMT savings amount in the Charlotte area for TOD residents at the seven current park-and-ride

stations would be an estimated 15,750 – 19,600 miles traveled for the 500 resident scenario and

31,500 – 39,200 miles traveled in the 1,000 resident scenario. An even mix of 500 resident and

1,000 resident TOD’s would roughly result in a VMT savings of 23,525 miles traveled to 29,400

miles traveled based on a 20 – 25 percent reduction in VMT. However, the key findings of the

0

10,000

20,000

30,000

40,000

50,000

60,000

70,000

80,000

Low (15%) Middle-Low(20%)

Middle-High(25%)

High (30%) Portland(43%)



83

home – station versus home – final destination indicated a roughly higher VMT savings of an

estimated 33,036 miles traveled. In order to meet the estimated reduction in VMT of park-and-

ride, TOD savings for the 500 average TOD dwellers per station scenario would have to be 43

percent or greater (the very high Portland case study). Based on the estimate for VMT savings

for the average 1,000 (high density) average TOD dwellers per station scenario, the VMT

reduction amount would have to be roughly 20 percent or greater (middle-low VMT reduction

rate). Based on these findings, park-and-ride is more likely a better choice in the Charlotte

context for reducing VMT in more suburban parts of the city. If TOD’s in the city had a higher

density of 1,000 dwellers per TOD (twice the current average Charlotte TOD density) than the

VMT savings might be greater than the average savings created by park-and-ride.

84

Chapter 6: Future Research

This research has provided an indication as to the VMT and emission impact of park-and-

ride use in a rapidly growing US Sunbelt city. Very few studies have been done specifically

looking at the VMT costs of using park-and-ride lots and even fewer have been done in the US

context, with none observing the impact in Charlotte, NC. The minimal impact on VMT (about a

0.3 percent reduction) among park-and-ride users could challenge the park-and-ride role as a

congestion reducer and an effective way of getting people to use transit in Charlotte. Further

studies could explore the park-and-ride/Transit-Oriented Development tradeoff further to see

which is more effective in reducing VMT, emissions and congestion. Though TODs may not

necessarily increase ridership as much as park-and-ride, they have been shown to reduce

automobile congestion and emissions by reducing the need for development along the urban

fringe, preserving green fields, encouraging efficient trip chaining (combining all errands or trips

into one sensible trip), and boosting walking accessibility through the smart use of land around a

transit station. Additionally, another drawback to the study is that it fails to capture the true mix

of vehicles being used to reach Charlotte park-and-ride stations and uses only national

coefficients and averages to determine regional vehicle emissions. A future study that had a

better sample of the actual vehicle fleet in question would yield much more accurate results with

regard to vehicle age and emission rates. In this case, capturing license plate numbers and

collecting vehicle type and origin data would yield much more accurate results for estimating

regional emissions and determining better origin data for all park-and-ride users in a given day.

Future studies could also look into the impact of induced demand and transit abstraction,

which are talked about frequently in the literature review as major indirect effects of increasing

85

levels of congestion and VMT among park-and-ride users. Since there was no data in the 2009

on-board user survey to indicate how many people were using transit before switching to park-

and-ride, it is hard to tell the true effect of transit abstraction in the Charlotte case study. For

every park-and-ride user that was using bus transit before the advent of light rail in Charlotte, a

switch from only bus to park-and-ride (auto/light rail) usage would result in an even higher VMT

and emission rate among the park-and-ride users. The induced demand effect (trips removed

from the roadway network due to park-and-ride auto-interception are being replaced by new trips

encouraging new auto users to quickly fill up the newly created open spaces on the road, many of

which are going into the center city) is also an important indirect effect that could be modeled in

future research. The induced demand effect could also be modeled if all park-and-ride stations

were replaced with TOD and the two scenarios could be compared to see which has the greater

impact in reducing induced traffic and congestion. The impact of induced demand could be

modeled in the future by using a transportation modeling system that accounts for feedback loops

and latent demand (Litman 2010) as well as incorporating the interrelationships between land

use, density, and transport (Litman 2010). There are several modeling systems that could be

employed to help model the impact of induced demand including the FHWA SMITE

(Spreadsheet Model for Induced Travel Estimation), TRANUS and MEPLAN (Litman 2010).

According to Litman’s paper, the main elements and variables needed to estimate induced

demand are generated traffic growth rates (e.g. elasticity of traffic volume with respect to road

volume), discount rate, maximum peak vehicles per lane, before average speed limit, after

average speed limit, volume of peak-period travel time, vehicle operating costs, annual lane

hours at capacity in both directions, diverted trips external costs, and induced travel external

costs. There was some general consensus in the literature that these indirect effects can possibly

86

lead to more congestion and more VMT if not addressed in the planning stages of park-and-ride

development.

87

References

1. Burgess, Jason. “A Comparative Analysis of the Park and Ride/Transit-Oriented

Development Tradeoff.” MCP Thesis, Massachusetts Institute of Technology, 2008.

2. Cervero, Robert. “Effects of TOD on Housing, Parking, and Travel.” Transportation

Research Board: Report 128, Washington D.C., 2008.

3. CPRE Campaign Brief. “Park and Ride – Its Role in Local Transport Policy.” Published

by the CPRE, London, UK, April 1998.

4. De Palma, A. and Y. Nesterov. “Park and Ride for the Morning and Evening Commute.”

Published by the American Society of Civil Engineers, June 2001.

5. Deakin, Elizabeth and Manish Shirgaokar. “Study of Park and Ride Facilities and Their

Use in the San Francisco Bay Area of California.” Transportation Research Record:

Journal of the Transportation Research Board 1927 (2005): 46-54.

6. Dickins, Ian S.J. “Park and ride Facilities on Light Rail Transit Systems.” Transportation

18 (1991): 23-36.

7. Glover, Edward L. “Hot Soak Emissions as a Function of Time.” Published by the US

Environmental Protection Agency Office of Transportation and Air Quality, Washington

D.C., April 2001.

8. Houk, Jeffrey A. “Making Use of MOBILE6’s Capabilities For Modeling Start

Emissions.” Paper submitted and presented at the 13th

Annual Emission Inventory

Conference, Clearwater, FL, June 10th

, 2004.

9. Lahtadirs, Igors and Jeļena Loseva. “Park and Ride in Riga: An Analysis of Demand

Determining Factors.” Published by SSE Riga Student Research Papers, Riga, Latvia,

November 2007.

10. Litman, Todd. “Generated Traffic and Induced Travel: Implications for Transport

Planning.” Victoria Transport Policy institute, Victoria, Canada, December 14, 2010.

11. Litman, Todd. “Land Use Impacts on Transport: How Land Use Factors Affect Travel

Behavior.” Victoria Transport Policy institute, Victoria, Canada, January 6, 2011.

12. Meek, Stuart D., Professor Stephen G. Ison, and Dr. Marcus P. Enoch. “Park and ride:

Lessons from the UK Experience.” Paper submitted and presented at the Transportation

Research Board, Washington D.C., November 6, 2007.

88

13. Meek, Stuart D., Professor Stephen G. Ison, and Dr. Marcus P. Enoch. “Stakeholder

perspectives on the current and future roles of UK bus-based Park and ride.” Journal of

Transport Geography 17 (6) (2009): 468-475.

14. National Oceanic and Atmospheric Administration. “Cloudiness – Mean Number of Days

(Clear, Partly Cloudy, Cloudy)” Data Table (1971 – 2000):

http://www1.ncdc.noaa.gov/pub/data/ccd-data/nrmavg.txt

15. RITA BTS. “National Transportation Statistics.” Published by Research and Innovative

Technology Administration Bureau of Transportation Statistics, Washington, D.C., 2009.

16. Parkhurst, Graham. “Park and Ride: Could it lead to an Increase in Car Traffic?”

Transport Policy 2(1) (1995): 15-23.

17. Parkhurst, Graham. “Influence of Bus-based Park and Ride facilities on Users’ Car

Traffic.” Transport Policy 7 (2000): 159-172.

18. RPS. “The effectiveness and Sustainability of Park and Ride.” Published by Historic

Towns Forum, Bristol, UK, June 12, 2009.

19. Semmens, John. “Does Light Rail Worsen Congestion and Air Quality?” Published by

Laissez Faire Institute, Chandler, Arizona, June 2005.

20. Sherwin, H. “Park and Ride – Its Role in Local Transport Policy.” Published by the

CPRE, London, UK (1998).

21. Sorensen, Paul. “Population Density versus Per-Capita VMT for the 14 Largest U.S.

Metropolitan Regions.” January 8, 2010:

http://www.newgeography.com/content/001317-population-density-vs-per-capita-vmt-

14-largest-us-metropolitan-regions

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VMT Weighting by Facility Type.” Published by the US Environmental Protection

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Planning Consultants (1998).

http://www.newgeography.com/content/001317-population-density-vs-per-capita-vmt-14-largest-us-metropolitan-regions

http://www.newgeography.com/content/001317-population-density-vs-per-capita-vmt-14-largest-us-metropolitan-regions

89

Appendices

Table 19: Average start and running emissions for cars and trucks by model year:

90

Table 20: The following table indicates the number of survey users (indicated by #) for each

station, the origin and destination TAZ (PTAZ and ATAZ respectively) for each user, the

estimated number of additional people using the light rail and travelling on a similar path (the

Combined ExpFactor Linked field), and VMT per user (determined by the average trip length

from the PTAZ to ATAZ using the OD pair matrix provided by CDOT), and the VMT for all

estimated park-and-ride users (determined by multiplying the VMT survey result by the the

Combined ExpFactor Linked field) for the home – station scenario:

# FINAL_ON_Station PTAZ ATAZ Combined ExpFactor Linked

VMT Survey VMT All

1 Archdale Station 11029 11029 11.0 2.6 28.3




5 Arrowood Station 11030 11030 0.6 24.6 14.9




























91



35 I-485 Station 10522 10522 16.0 25.5 407.2

36 I-485 Station 10522 10522 16.0 21.7 347.0

37 I-485 Station 10522 10522 7.9 20.6 162.4

38 I-485 Station 10522 10522 16.0 19.9 318.8

39 I-485 Station 10522 10522 7.9 17.9 141.3

40 I-485 Station 10522 10522 16.0 17.6 281.3

41 I-485 Station 10522 10522 16.0 16.7 266.4

42 I-485 Station 10522 10522 16.0 15.9 254.0

43 I-485 Station 10522 10522 16.0 15.8 253.0

44 I-485 Station 10522 10522 29.7 15.7 466.3

45 I-485 Station 10522 10522 7.9 15.0 118.3

46 I-485 Station 10522 10522 16.0 15.0 239.6

47 I-485 Station 10522 10522 14.7 14.7 216.8

48 I-485 Station 10522 10522 16.0 14.3 228.6

49 I-485 Station 10522 10522 7.9 14.0 110.6

50 I-485 Station 10522 10522 0.7 13.8 9.2

51 I-485 Station 10522 10522 11.7 13.7 160.3

52 I-485 Station 10522 10522 7.9 13.7 107.6

53 I-485 Station 10522 10522 16.0 13.6 218.2

54 I-485 Station 10522 10522 14.7 13.3 195.8

55 I-485 Station 10522 10522 16.2 13.0 211.6

56 I-485 Station 10522 10522 14.7 12.8 188.3

57 I-485 Station 10522 10522 16.0 12.8 204.6

58 I-485 Station 10522 10522 16.0 12.8 204.6

59 I-485 Station 10522 10522 16.0 12.8 204.6

60 I-485 Station 10522 10522 7.9 12.5 98.8

61 I-485 Station 10522 10522 16.0 12.1 193.3

62 I-485 Station 10522 10522 16.0 12.0 191.5

63 I-485 Station 10522 10522 7.9 11.8 93.1

64 I-485 Station 10522 10522 14.7 11.7 172.5

65 I-485 Station 10522 10522 7.9 11.7 92.3

66 I-485 Station 10522 10522 16.0 11.7 186.6

67 I-485 Station 10522 10522 7.9 11.6 91.6

68 I-485 Station 10522 10522 16.0 11.6 186.0

69 I-485 Station 10522 10522 16.0 11.3 180.2

70 I-485 Station 10522 10522 7.9 11.1 87.1

71 I-485 Station 10522 10522 16.0 10.8 171.9

72 I-485 Station 10522 10522 16.0 10.7 171.1

73 I-485 Station 10522 10522 16.0 10.7 171.1

74 I-485 Station 10522 10522 7.9 10.5 82.9

75 I-485 Station 10522 10522 16.0 10.5 167.6

76 I-485 Station 10522 10522 7.9 10.3 81.3

77 I-485 Station 10522 10522 7.9 10.3 81.1

78 I-485 Station 10522 10522 7.9 10.3 81.1

79 I-485 Station 10522 10522 16.0 10.3 164.6

80 I-485 Station 10522 10522 14.7 10.2 149.9

92

81 I-485 Station 10522 10522 16.0 10.2 162.9

82 I-485 Station 10522 10522 14.7 10.0 146.8

83 I-485 Station 10522 10522 16.0 10.0 159.5

84 I-485 Station 10522 10522 14.7 9.9 145.9

85 I-485 Station 10522 10522 7.9 9.9 78.1

86 I-485 Station 10522 10522 7.9 9.9 78.1

87 I-485 Station 10522 10522 7.9 9.9 78.1

88 I-485 Station 10522 10522 16.0 9.9 158.5

89 I-485 Station 10522 10522 16.0 9.4 151.1

90 I-485 Station 10522 10522 1.9 9.4 18.1

91 I-485 Station 10522 10522 7.9 9.4 74.4

92 I-485 Station 10522 10522 7.9 9.4 74.4

93 I-485 Station 10522 10522 16.0 9.3 148.6

94 I-485 Station 10522 10522 7.9 8.9 70.2

95 I-485 Station 10522 10522 16.0 8.8 141.1

96 I-485 Station 10522 10522 16.0 8.8 141.1

97 I-485 Station 10522 10522 7.9 8.8 69.1

98 I-485 Station 10522 10522 7.9 8.8 69.1

99 I-485 Station 10522 10522 7.9 8.4 65.9

100 I-485 Station 10522 10522 14.7 8.2 121.1

101 I-485 Station 10522 10522 11.7 8.2 96.3

102 I-485 Station 10522 10522 11.7 8.2 96.3

103 I-485 Station 10522 10522 16.0 8.2 130.3

104 I-485 Station 10522 10522 14.7 8.1 119.5

105 I-485 Station 10522 10522 14.7 8.1 119.5

106 I-485 Station 10522 10522 7.9 8.1 63.9

107 I-485 Station 10522 10522 7.9 8.1 63.9

108 I-485 Station 10522 10522 16.0 8.1 129.2

109 I-485 Station 10522 10522 16.0 8.1 129.2

110 I-485 Station 10522 10522 16.0 8.1 129.2

111 I-485 Station 10522 10522 16.0 7.9 125.9

112 I-485 Station 10522 10522 16.0 7.9 125.9

113 I-485 Station 10522 10522 7.9 7.9 62.0

114 I-485 Station 10522 10522 16.0 7.9 125.9

115 I-485 Station 10522 10522 16.0 7.8 124.0

116 I-485 Station 10522 10522 16.0 7.8 124.0

117 I-485 Station 10522 10522 16.0 7.8 124.0

118 I-485 Station 10522 10522 16.0 7.8 124.0

119 I-485 Station 10522 10522 29.7 7.7 229.8

120 I-485 Station 10522 10522 16.0 7.7 123.9

121 I-485 Station 10522 10522 16.0 7.7 123.9

122 I-485 Station 10522 10522 7.9 7.7 61.0

123 I-485 Station 10522 10522 7.9 7.7 61.0

124 I-485 Station 10522 10522 14.7 7.7 112.7

125 I-485 Station 10522 10522 7.9 7.7 60.3

126 I-485 Station 10522 10522 16.0 7.7 122.4

127 I-485 Station 10522 10522 11.7 7.7 89.6

128 I-485 Station 10522 10522 29.7 7.5 221.1

93

129 I-485 Station 10522 10522 16.0 7.2 114.4

130 I-485 Station 10522 10522 29.7 6.9 205.7

131 I-485 Station 10522 10522 7.9 6.9 54.6

132 I-485 Station 10522 10522 7.9 6.9 54.6

133 I-485 Station 10522 10522 29.7 6.7 198.5

134 I-485 Station 10522 10522 16.0 6.7 107.0

135 I-485 Station 10522 10522 16.0 6.6 106.2

136 I-485 Station 10522 10522 14.7 6.6 97.7

137 I-485 Station 10522 10522 16.0 6.6 106.2

138 I-485 Station 10522 10522 7.9 6.6 52.3

139 I-485 Station 10522 10522 7.9 6.6 52.3

140 I-485 Station 10522 10522 7.9 6.6 52.3

141 I-485 Station 10522 10522 16.0 6.4 103.0

142 I-485 Station 10522 10522 14.7 6.3 93.1

143 I-485 Station 10522 10522 29.7 6.2 185.3

144 I-485 Station 10522 10522 7.9 6.2 49.2

145 I-485 Station 10522 10522 16.0 6.2 99.9

146 I-485 Station 10522 10522 7.9 6.2 49.2

147 I-485 Station 10522 10522 16.0 6.2 99.5

148 I-485 Station 10522 10522 29.7 6.1 180.9

149 I-485 Station 10522 10522 29.7 6.1 180.9

150 I-485 Station 10522 10522 14.7 6.1 89.8

151 I-485 Station 10522 10522 14.7 6.1 89.8

152 I-485 Station 10522 10522 14.7 6.1 89.8

153 I-485 Station 10522 10522 7.9 6.1 48.0

154 I-485 Station 10522 10522 7.9 6.1 48.0

155 I-485 Station 10522 10522 7.9 6.1 48.0

156 I-485 Station 10522 10522 16.0 6.1 97.5

157 I-485 Station 10522 10522 16.0 5.9 95.1

158 I-485 Station 10522 10522 16.0 5.9 95.1

159 I-485 Station 10522 10522 16.0 5.9 95.1

160 I-485 Station 10522 10522 16.0 5.9 95.1

161 I-485 Station 10522 10522 7.9 5.7 44.9

162 I-485 Station 10522 10522 7.9 5.7 44.9

163 I-485 Station 10522 10522 7.9 5.7 44.9

164 I-485 Station 10522 10522 16.0 5.7 91.2

165 I-485 Station 10522 10522 16.0 5.6 88.9

166 I-485 Station 10522 10522 14.7 5.6 81.9

167 I-485 Station 10522 10522 16.0 5.6 88.9

168 I-485 Station 10522 10522 16.0 5.6 88.9

169 I-485 Station 10522 10522 11.7 5.6 65.1

170 I-485 Station 10522 10522 7.9 5.4 42.8

171 I-485 Station 10522 10522 14.7 4.9 72.3

172 I-485 Station 10522 10522 7.9 4.9 38.7

173 I-485 Station 10522 10522 11.7 4.9 57.5

174 I-485 Station 10522 10522 14.7 4.6 68.3

175 I-485 Station 10522 10522 29.7 4.1 121.4

176 I-485 Station 10522 10522 14.7 4.1 60.2

94

177 I-485 Station 10522 10522 14.7 4.1 60.2

178 I-485 Station 10522 10522 7.9 4.1 32.2

179 I-485 Station 10522 10522 16.0 4.1 65.5

180 I-485 Station 10522 10522 16.0 3.9 62.5

181 I-485 Station 10522 10522 16.0 3.7 59.4

182 I-485 Station 10522 10522 7.9 3.7 29.2

183 I-485 Station 10522 10522 7.9 3.7 29.0

184 I-485 Station 10522 10522 7.9 3.6 28.4

185 I-485 Station 10522 10522 16.0 3.3 53.3

186 I-485 Station 10522 10522 14.7 3.2 46.8

187 I-485 Station 10522 10522 16.0 3.2 50.9

188 I-485 Station 10522 10522 7.9 2.6 20.8

189 I-485 Station 10522 10522 7.9 2.6 20.8

190 I-485 Station 10522 10522 16.0 2.4 38.5

191 I-485 Station 10522 10522 16.0 0.7 11.7

192 I-485 Station 10522 10522 16.0 0.7 11.7

193 I-485 Station 10522 10522 14.7 0.7 10.7

194 I-485 Station 10522 10522 16.0 0.7 11.7

195 I-485 Station 10522 10522 16.0 0.7 11.7

196 I-485 Station 10522 10522 16.0 0.7 11.7

197 I-485 Station 10522 10522 16.0 0.7 11.7

198 I-485 Station 10522 10522 4.7 0.7 3.4

199 I-485 Station 10522 10522 16.0 0.6 9.3

200 Non-Park and Ride Stations 10020 10020 12.7 30.5 388.7










210 Scaleybark Station 11027 11027 8.5 34.1 288.3















95















239 Sharon Road West Station 10523 10523 21.1 24.8 521.6






























269 Tyvola Station 11028 11028 1.5 25.7 37.6

270 Tyvola Station 11028 11028 10.7 9.4 100.6

271 Tyvola Station 11028 11028 16.7 9.4 156.7

272 Tyvola Station 11028 11028 10.7 9.4 100.3

96

273 Tyvola Station 11028 11028 16.7 6.5 109.0

274 Tyvola Station 11028 11028 16.7 6.1 101.7

275 Tyvola Station 11028 11028 16.7 5.1 84.7

276 Tyvola Station 11028 11028 16.7 4.1 67.7

277 Tyvola Station 11028 11028 0.8 2.5 2.0

278 Tyvola Station 11028 11028 16.7 2.3 39.1

279 Tyvola Station 11028 11028 40.4 1.8 74.2

280 Tyvola Station 11028 11028 16.7 1.8 30.7

281 Tyvola Station 11028 11028 10.7 1.8 19.7

282 Tyvola Station 11028 11028 8.3 1.6 13.7

283 Tyvola Station 11028 11028 10.7 1.4 14.7

284 Tyvola Station 11028 11028 10.7 1.4 14.7

285 Tyvola Station 11028 11028 16.7 1.4 22.9

286 Tyvola Station 11028 11028 20.0 1.3 26.0

287 Tyvola Station 11028 11028 15.9 1.1 17.9

288 Tyvola Station 11028 11028 10.7 1.1 11.5

289 Tyvola Station 11028 11028 15.9 0.8 12.6

290 Tyvola Station 11028 11028 16.7 0.8 13.2

291 Woodlawn Station 11068 11068 41.1 27.8 1144.9














SUM 2172.3 30973.4

97

Table 21: The following table indicates the number of survey users (indicated by #) for each

station, the origin and destination TAZ (PTAZ and ATAZ respectively) for each user, the

estimated number of additional people using the light rail and travelling on a similar path (the

Combined ExpFactor Linked field), and VMT per user (determined by the average trip length

from the PTAZ to ATAZ using the OD pair matrix provided by CDOT), and the VMT for all

estimated park-and-ride users (determined by multiplying the VMT survey result by the the

Combined ExpFactor Linked field) for the home – final destination scenario:

# FINAL_ON_Station PTAZ ATAZ Combined ExpFactor Linked

VMT Survey VMT All































98






36 I-485 Station 10929 10773 16.0 35.8 572.3

37 I-485 Station 3196 10773 16.0 33.7 538.7

38 I-485 Station 3295 10009 16.0 30.9 494.6

39 I-485 Station 9194 10010 16.0 29.1 464.6

40 I-485 Station 7006 10010 16.0 28.2 451.2

41 I-485 Station 3062 10011 16.0 27.4 438.7

42 I-485 Station 9283 10009 16.0 26.6 425.0

43 I-485 Station 3178 10011 7.9 26.4 207.6

44 I-485 Station 9202 10011 7.9 26.0 204.6

45 I-485 Station 3087 10009 16.0 25.4 406.3

46 I-485 Station 9204 10009 11.7 24.4 285.4

47 I-485 Station 9211 10010 14.7 24.1 355.0

48 I-485 Station 3038 10009 7.9 23.4 184.4

49 I-485 Station 10890 10010 16.0 23.2 370.2

50 I-485 Station 3190 10009 16.0 23.1 368.8

51 I-485 Station 9211 10011 7.9 22.7 178.6

52 I-485 Station 9282 10009 16.0 22.7 362.4

53 I-485 Station 10904 10009 7.9 22.5 177.4

54 I-485 Station 10893 10010 16.0 22.4 357.8

55 I-485 Station 9210 10009 7.9 22.3 175.8

56 I-485 Station 9210 10009 16.0 22.3 356.9

57 I-485 Station 9281 10010 7.9 22.3 175.7

58 I-485 Station 3199 10010 7.9 21.9 172.4

59 I-485 Station 10902 10010 7.9 21.8 172.1

60 I-485 Station 10927 10014 14.7 21.4 315.4

61 I-485 Station 10903 10010 16.0 21.3 340.3

62 I-485 Station 10963 10010 16.0 21.1 338.1

63 I-485 Station 10893 10011 14.7 20.9 308.1

64 I-485 Station 10927 10009 16.0 20.9 333.8

65 I-485 Station 9281 10011 7.9 20.9 164.4

66 I-485 Station 3204 10010 16.0 20.8 333.0

67 I-485 Station 3203 10012 14.7 20.8 305.7

68 I-485 Station 10902 10012 16.0 20.7 331.2

69 I-485 Station 10922 10010 14.7 20.6 303.5

70 I-485 Station 10294 10010 16.0 20.5 327.4

71 I-485 Station 10929 10010 16.0 20.3 324.2

72 I-485 Station 10929 10010 16.0 20.3 324.2

73 I-485 Station 10931 10010 14.7 20.3 298.3

74 I-485 Station 10931 10010 14.7 20.3 298.3

75 I-485 Station 10902 10002 1.9 20.2 38.7

76 I-485 Station 10905 10010 16.0 20.2 322.3

77 I-485 Station 10905 10010 16.0 20.2 322.3

78 I-485 Station 10906 10010 29.7 20.1 597.5

99

79 I-485 Station 10906 10010 16.0 20.1 322.1

80 I-485 Station 10906 10010 16.0 20.1 322.1

81 I-485 Station 10902 10009 7.9 20.1 158.6

82 I-485 Station 10932 10010 14.7 20.1 295.2

83 I-485 Station 3197 10010 14.7 20.0 293.9

84 I-485 Station 3197 10010 16.0 20.0 319.4

85 I-485 Station 10931 10016 29.7 19.9 591.3

86 I-485 Station 10930 10009 7.9 19.5 153.3

87 I-485 Station 10930 10009 7.9 19.5 153.3

88 I-485 Station 10933 10010 29.7 19.3 573.4

89 I-485 Station 10933 10010 7.9 19.3 152.3

90 I-485 Station 10931 10012 29.7 19.1 567.8

91 I-485 Station 10931 10012 14.7 19.1 281.7

92 I-485 Station 10921 10010 29.7 19.1 566.2

93 I-485 Station 10921 10010 16.0 19.1 305.2

94 I-485 Station 10919 10010 16.0 19.0 304.4

95 I-485 Station 10919 10010 14.7 19.0 280.2

96 I-485 Station 10919 10010 7.9 19.0 150.0

97 I-485 Station 10905 10014 16.0 19.0 303.8

98 I-485 Station 10928 10009 16.0 18.8 301.3

99 I-485 Station 10929 10011 7.9 18.8 148.3

100 I-485 Station 10931 10011 7.9 18.8 148.3

101 I-485 Station 10933 10117 7.9 18.8 148.1

102 I-485 Station 10294 10009 16.0 18.8 300.1

103 I-485 Station 10905 10011 16.0 18.7 299.2

104 I-485 Station 10906 10011 7.9 18.7 147.3

105 I-485 Station 10922 10019 11.7 18.7 218.5

106 I-485 Station 10922 10019 11.7 18.7 218.5

107 I-485 Station 10923 10010 29.7 18.6 553.1

108 I-485 Station 10924 10010 16.0 18.6 298.1

109 I-485 Station 10918 10010 16.0 18.6 297.7

110 I-485 Station 10932 10011 7.9 18.6 146.6

111 I-485 Station 10931 10020 7.9 18.6 146.6

112 I-485 Station 10931 10009 7.9 18.6 146.2

113 I-485 Station 3197 10011 16.0 18.5 296.3

114 I-485 Station 10906 10009 7.9 18.4 145.2

115 I-485 Station 10294 11025 16.0 18.4 293.7

116 I-485 Station 10932 10009 16.0 18.3 293.4

117 I-485 Station 10920 10010 16.0 18.3 293.3

118 I-485 Station 3197 10009 16.0 18.3 292.1

119 I-485 Station 10931 11025 16.0 18.2 290.4

120 I-485 Station 10932 10019 11.7 18.1 211.8

121 I-485 Station 10919 10014 7.9 17.9 140.9

122 I-485 Station 3196 10010 16.0 17.9 285.9

123 I-485 Station 10949 10010 16.0 17.7 282.4

124 I-485 Station 10926 10010 14.7 17.6 259.7

125 I-485 Station 10919 10011 16.0 17.6 281.4

126 I-485 Station 10919 10009 7.9 17.3 136.5

100

127 I-485 Station 10923 10011 7.9 17.2 135.5

128 I-485 Station 10911 10010 14.7 17.0 250.7

129 I-485 Station 10894 10149 29.7 17.0 504.1

130 I-485 Station 10924 10020 7.9 17.0 133.7

131 I-485 Station 10920 10011 16.0 16.9 270.3

132 I-485 Station 10917 10011 7.9 16.7 131.2

133 I-485 Station 10917 10011 7.9 16.7 131.2

134 I-485 Station 10917 10011 16.0 16.7 266.4

135 I-485 Station 10920 10009 16.0 16.6 266.0

136 I-485 Station 10913 10010 29.7 16.5 489.2

137 I-485 Station 10913 10010 14.7 16.5 242.7

138 I-485 Station 10532 10011 7.9 16.4 129.1

139 I-485 Station 10917 10009 7.9 16.4 129.1

140 I-485 Station 10920 11025 16.0 16.2 259.5

141 I-485 Station 10961 10009 7.9 16.2 127.6

142 I-485 Station 10531 10010 16.0 16.1 257.6

143 I-485 Station 3207 10011 7.9 16.0 126.3

144 I-485 Station 10926 10020 11.7 16.0 187.0

145 I-485 Station 10926 10009 7.9 15.9 125.5

146 I-485 Station 3207 10020 7.9 15.8 124.5

147 I-485 Station 3212 10010 16.0 15.6 249.2

148 I-485 Station 10951 10009 7.9 15.4 121.1

149 I-485 Station 10951 10009 16.0 15.4 245.9

150 I-485 Station 10913 10014 14.7 15.3 225.7

151 I-485 Station 10913 10011 16.0 15.1 240.7

152 I-485 Station 10943 10010 14.7 15.0 220.3

153 I-485 Station 10943 10010 14.7 15.0 220.3

154 I-485 Station 10912 10011 16.0 14.9 237.7

155 I-485 Station 10913 10009 7.9 14.8 116.4

156 I-485 Station 10938 10009 7.9 14.7 115.9

157 I-485 Station 10531 10011 7.9 14.7 115.6

158 I-485 Station 10909 10012 14.7 14.5 212.7

159 I-485 Station 10874 10011 7.9 14.4 113.6

160 I-485 Station 3209 10020 16.0 14.3 229.3

161 I-485 Station 10001 10536 7.9 14.3 112.7

162 I-485 Station 3209 10009 16.0 14.3 228.5

163 I-485 Station 10535 10024 16.0 14.2 226.9

164 I-485 Station 3212 10011 14.7 14.1 208.2

165 I-485 Station 3212 10011 16.0 14.1 226.2

166 I-485 Station 10909 10011 16.0 14.1 226.1

167 I-485 Station 10937 10009 16.0 14.1 225.6

168 I-485 Station 10588 10009 7.9 14.1 110.7

169 I-485 Station 3212 10009 16.0 13.9 221.9

170 I-485 Station 10537 10010 16.0 13.9 221.6

171 I-485 Station 10537 10010 16.0 13.9 221.6

172 I-485 Station 10537 10010 14.7 13.9 204.0

173 I-485 Station 10537 10010 16.0 13.9 221.6

174 I-485 Station 10537 10010 16.0 13.9 221.6

101

175 I-485 Station 10584 10011 7.9 13.8 108.5

176 I-485 Station 10943 10011 7.9 13.5 106.5

177 I-485 Station 10533 10020 7.9 13.4 105.4

178 I-485 Station 10533 10020 7.9 13.4 105.4

179 I-485 Station 10586 10010 16.0 13.3 212.5

180 I-485 Station 10943 10009 7.9 13.3 104.4

181 I-485 Station 3210 10012 16.0 13.2 211.0

182 I-485 Station 3212 10149 11.7 12.7 149.0

183 I-485 Station 10537 10012 16.0 12.7 203.5

184 I-485 Station 10011 10945 14.7 12.4 182.6

185 I-485 Station 10157 3211 16.2 12.4 201.4

186 I-485 Station 10419 10020 16.0 12.3 196.9

187 I-485 Station 11032 10011 16.0 12.3 196.3

188 I-485 Station 10537 10009 16.0 12.2 194.3

189 I-485 Station 10013 10279 0.7 11.8 7.8

190 I-485 Station 10897 10009 16.0 11.4 182.6

191 I-485 Station 10589 10009 16.0 11.0 176.2

192 I-485 Station 10469 10011 7.9 10.2 80.5

193 I-485 Station 10472 10020 16.0 9.7 155.1

194 I-485 Station 10529 10009 7.9 9.0 70.6

195 I-485 Station 10131 10002 14.7 6.6 96.7

196 I-485 Station 10537 10558 4.7 6.2 29.2

197 I-485 Station 10435 10014 14.7 5.9 87.0

198 I-485 Station 10043 10014 29.7 2.8 83.2

199 I-485 Station 10149 10009 16.0 1.9 30.9

200 I-485 Station 10010 10009 7.9 0.3 2.7























102

















































103


272 Tyvola Station 2030 10557 1.5 26.2 38.3

273 Tyvola Station 10551 10279 0.8 17.8 14.4

274 Tyvola Station 10531 11025 10.7 14.0 150.1

275 Tyvola Station 10586 10010 16.7 13.3 221.8

276 Tyvola Station 10589 10014 16.7 11.6 193.2

277 Tyvola Station 10589 10009 10.7 11.0 118.1

278 Tyvola Station 10475 10010 16.7 10.6 176.8

279 Tyvola Station 11029 10010 20.0 9.4 188.8

280 Tyvola Station 10506 10009 16.7 8.8 146.2

281 Tyvola Station 10514 10010 16.7 8.7 144.8

282 Tyvola Station 10562 10010 16.7 8.6 142.8

283 Tyvola Station 10558 10002 8.3 8.2 68.7

284 Tyvola Station 10562 10016 40.4 8.2 331.7

285 Tyvola Station 10507 10011 16.7 8.1 134.5

286 Tyvola Station 10516 10010 16.7 7.9 131.8

287 Tyvola Station 10507 10009 10.7 7.8 83.5

288 Tyvola Station 10507 10009 10.7 7.8 83.5

289 Tyvola Station 10494 10020 15.9 7.6 120.8

290 Tyvola Station 11028 10012 40.4 7.4 299.4

291 Tyvola Station 10562 10009 10.7 6.8 73.4

292 Tyvola Station 10509 11025 15.9 6.8 108.3

293 Tyvola Station 10509 11025 16.7 6.8 113.5

294 Tyvola Station 10557 10020 10.7 6.8 72.4
















SUM 4401.4 64008.7

Documents

How does the Use of Light Rail Park-and-ride Facilities in Charlotte Influence Vehicle Emissions and Create more Vehicle Miles Traveled?