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

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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

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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

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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

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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

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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

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MethodologyMacroscopic

Travel Demand Modeling Emmision Factor Modeling Dispersion Modeling

Microscopic

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NB/SB: 20,750 veh/day

EB/WB: 27,538 veh/day

Methodology

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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

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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

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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

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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

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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.