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VISUM SIMULATION BASED ON-ROAD-VEHICLE CO CONCENTRATION PREDICTION
CEE610 Computer Method in Transportation Term Project
Instructor: Dr. Heng WeiPrepared by: Zhuo Yao
01/08/2010
Problem Statement
10/18/2017
American Lung Association (ALA) and Environmental Protection Agency (EPA) data shows that Cincinnati’s air quality tends to be at “Moderate” Air Quality Index (AQI) level, which means air quality is acceptable; however, for some pollutants there may be a moderate health concern for vulnerable group of people (i.e. Seniors and infants).
Problem Statement
10/18/2017
2005 U.S. EPA’s data shows 136,224 tons of on road vehicle CO emissions in Hamilton County which occupies 69% of the total CO emission sources.
Roadside air quality is a function of differences of traffic in density with time, vehicle type, vehicle classification, fuel type, terrain and meteorological conditions. In most of the urban centers, over 90% of the CO emissions are solely emitted by motor vehicles.
CO can cause harmful health effects by reducing oxygen delivery to the body's organs and tissues. It can also have cardiovascular effects; central nervous system effects and contributes to the formation of smog ground level ozone, which can trigger serious respiratory problems
Objectives
10/18/2017
Goal: To investigate on-road-vehicle CO Concentration the based on Travel
Demand Modeling (VISUM). Objective: Investigate traffic volume at intersection in future scenarios base on Travel
Demand Modeling. Investigate the CO concentration at a study intersection base on the
predicted traffic volume. Comparison of typical weekday 1 or 8 hours CO concentration Choropleth
map profile to NAAQS concentration.
Scope of Work
10/18/2017
Study Site
MLK and Clifton Intersection
Advantages/Reasons:
1. Average ped exposure time 30 seconds.
2. Relative high number of pedestrian (156 peds/hr, PM peak, 2008).
3. Volume and signal timing data available can serve as baseline scenario.
Scope of Work
10/18/2017
Task1: Data collection and integration in ArcGIS environment; deliverables include: GIS shapefile contains all necessary data for VISUM modeling through data flow.
Task2: VISUM network buildup and travel demand modeling; deliverables include: aggregated weekday 24 hour volume data for MLK and Clifton; VMT fraction and VMT by hour.
Task3: Mobile 6 (MOVES 2010) emission factor modeling; deliverables include: Vehicle Specific Power (VSP); weekday 24 hour based CO emission factor.
Task4: Dispersion modeling in CalRoads View; deliverables include: 24 hour intersection CO concentration choropleths; CO concentration at receptor’s location.
A final report should be complete to summarize all the findings and recapping possible future works.
Methodology
10/18/2017
Methodology Modeling the travel demand in VISUM to get
forecasted traffic patterns Updating truck prediction using a proposed
method Trips disaggregated based on vehicle type and
links (e.g. Car, Truck, Bus etc) and hourly disaggregated link miles for each vehicle type
Using an Source Emission Model to calculate on-road source emission
Investigate a Dispersion Analysis to evaluate pollutions in target area
Investigate the impact on the Human Health and Climate Change
VISUMTDM
Trip Disaggregation
Source Emission Model
Emission Dispersion Analysis
Climate Change/Air
Quality Assessment
10/18/2017
MethodologyMacroscopic
Travel Demand Modeling Emmision Factor Modeling Dispersion Modeling
Microscopic
10/18/2017
NB/SB: 20,750 veh/day
EB/WB: 27,538 veh/day
Methodology
10/18/2017
Methodology
CO Concentration Choropleth
Source: http://www.lamma.rete.toscana.it/eng/aria/conc.html
Source: http://www.weblakes.com/products/calroads/features.html
Work Plan
10/18/2017
Task DaysWinter 2010
Week 1 Week 2 Week 3 Week 4 Week 5 Week 6 Week 7 Week 8 Week 9 Week 10
1 52 103 104 10
Report 5
In the period of ten weeks, four tasks will be implemented as illustrated.
References
10/18/2017
1) American Lung Association. “Most Polluted U.S. Cities by Year Round Particle Pollution”, <http://www.stateoftheair.org> (Jan. 5, 2010).2) U.S. Environmental Protection Agency, National Oceanic and Atmospheric Administration, National Park Service. “Local air quality conditions and
forecasts.” <http://www.airnow.gov/> (Jan. 5, 2010).3) U.S. Environmental Protection Agency (EPA). “Transportation and air quality.” Available at <http://www.epa.gov/omswww/> (Jan. 5, 2010).4) U.S. Environmental Protection Agency (EPA). “State and County Emission Summaries.” <http://www.epa.gov/air/emissions/co.htm> (Jan. 5, 2010).5) U.S. Environmental Protection Agency (EPA). “Carbon Monoxide: Chief Causes for Concern.” <http://www.epa.gov/air/urbanair/co/chf1.html>
(Jan. 5, 2010).6) U.S. Environmental Protection Agency (EPA). “Health and Environmental Impacts of CO.” <http://www.epa.gov/air/urbanair/co/hlth1.html> (Jan.
5, 2010).7) Baldauf, R. (2008). “Traffic and meteorological impacts on near-road air quality: summary of methods and trends from the Raleigh near road
study.” Journal of air and waste management association. Vol. 58 Issue 7, 865-878. 8) Venkatram, A., Isakov, V., Seila, R., and Baldauf, R. (2009). “Modeling the impacts of traffic emissions on air toxics concentrations near roadways.”
Atmospheric Environment, Volume 43, Issue 20, 3191-3199.9) Reis, S., Simpson, D., Friedrich, R., Jonson, J., Unger, S., and Obermeier, A., (2000). “Road traffic emissions – predictions of future contributions to
regional ozone levels in Europe.” Atmospheric Environment, Volume 34, Issue 27, 4701-4710.10) Negahban, B., Fonyo, C., Boggess, Jones, J., Campbell, K., and Kiker, G., (1995). “A GIS-based decision support system for regional environmental
planning.” Ecological Engineering, Volume 5, Issues 2-3, Pages 391-40411) Qiao, F., and Yu, L., (2005). “On-Road Vehicle Emission and Activity Data Collection and Evaluation in Houston, Texas.” Journal of the
Transportation Research Board, No. 1941, 60–71.12) Berkowicz, R., Winther, M., and Ketzel, M. (2006). “Traffic pollution modelling and emission data.” Environmental Modelling & Software, Volume
21, Issue 4, 454-460.13) Dai, J., and Rocke, D., (2000). “A GIS-based approach to spatial allocation of area source solvent emissions.” Environmental Modelling and
Software, Volume 15, Issue 3, 293-302.14) Jin,T., and Fu L., (2005). “Application of GIS to modified models of vehicle emission dispersion.” Atmospheric Environment, Volume 39, Issue 34,
6326-6333.15) Kanaroglou, P., and Buliung, R., (2008). “Estimating the contribution of commercial vehicle movement to mobile emissions in urban areas.”
Transportation Research Part E: Logistics and Transportation Review, Volume 44, Issue 2, 260-276.
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