VISUM SIMULATION BASED ON-ROAD-VEHICLE CO CONCENTRATION PREDICTION

Transcription

1 Instructor: Dr. Heng Wei Prepared by: Zhuo Yao VISUM SIMULATION BASED ON-ROAD-VEHICLE CO CONCENTRATION PREDICTION 01/08/2010 CEE610 Computer Method in Transportation Term Project

2 Problem Statement 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).

3 Problem Statement 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

4 Objectives 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.

5 Scope of Work 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.

6 Scope of Work 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.

7 Methodology 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 onroad source emission Investigate a Dispersion Analysis to evaluate pollutions in target area Investigate the impact on the Human Health and Climate Change VISUM TDM Trip Disaggregation Source Emission Model Emission Dispersion Analysis Climate Change/Air Quality Assessment

8 Methodology Macroscopic Microscopic Travel Demand Modeling Emmision Factor Modeling Dispersion Modeling

9 Methodology EB/WB: 27,538 veh/day NB/SB: 20,750 veh/day

10 Methodology CO Concentration Choropleth Source: Source:

11 Work Plan In the period of ten weeks, four tasks will be implemented as illustrated. Task Days Report 5 Winter 2010 Week 1 Week 2 Week 3 Week 4 Week 5 Week 6 Week 7 Week 8 Week 9 Week 10

12 References 1) American Lung Association. Most Polluted U.S. Cities by Year Round Particle Pollution, < (Jan. 5, 2010). 2) U.S. Environmental Protection Agency, National Oceanic and Atmospheric Administration, National Park Service. Local air quality conditions and forecasts. < (Jan. 5, 2010). 3) U.S. Environmental Protection Agency (EPA). Transportation and air quality. Available at < (Jan. 5, 2010). 4) U.S. Environmental Protection Agency (EPA). State and County Emission Summaries. < (Jan. 5, 2010). 5) U.S. Environmental Protection Agency (EPA). Carbon Monoxide: Chief Causes for Concern. < (Jan. 5, 2010). 6) U.S. Environmental Protection Agency (EPA). Health and Environmental Impacts of CO. < (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, ) 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, ) 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, ) 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 ) 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, ) Berkowicz, R., Winther, M., and Ketzel, M. (2006). Traffic pollution modelling and emission data. Environmental Modelling & Software, Volume 21, Issue 4, ) 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, ) Jin,T., and Fu L., (2005). Application of GIS to modified models of vehicle emission dispersion. Atmospheric Environment, Volume 39, Issue 34, ) 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,