Vehicle Emission Computation Through Microscopic Traffic Simulation Calibrated Using Genetic Algorithm

Open access


Vehicle emission calculation is critical for evaluating motor vehicle related environmental protection policies. Currently, many studies calculate vehicle emissions from integrating the microscopic traffic simulation model and the vehicle emission model. However, conventionally vehicle emission models are presented as a stand-alone software, requiring a laborious processing of the simulated second-by-second vehicle activity data. This is inefficient, in particular, when multiple runs of vehicle emission calculations are needed. Therefore, an integrated vehicle emission computation system is proposed around a microscopic traffic simulation model. In doing so, the relational database technique is used to store the simulated traffic activity data, and these data are used in emission computation through a built-in emission computation module developed based on the IVE model. In order to ensure the validity of the simulated vehicle activity data, the simulation model is calibrated using the genetic algorithm. The proposed system was implemented for a central urban region of Nanjing city. Hourly vehicle emissions of three types of vehicles were computed using the proposed system for the afternoon peak period, and the results were compared with those computed directly from the IVE software with a trivial difference in the results from the proposed system and the IVE software, indicating the validity of the proposed system. In addition, it was found for the study region that passenger cars are critical for controlling CO, buses are critical for controlling CO and VOC, and trucks are critical for controlling NOx and CO2. Future work is to test the proposed system in more traffic management and control strategies, and more vehicle emission models are to be incorporated in the system.

[1] A. Kendall, L. Price, Incorporating time-corrected life cycle greenhouse gas emissions in vehicle regulations, Environmental Science and Technology, 46(5), 2012, 2557-2563.

[2] V. Franco, M. Kousoulidou, M. Muntean, L. Ntziachristos, S. Hausberger, P. Dilara, Road vehicle emission factors development: A review, Atmospheric Environment, 70(70), 2013, 84-97.

[3] N. Maykut, J. Lewtas, E. Kim, T. Larson, Source apportionment of PM2.5 at an urban improve site in Seattle, Washington, Environmental Science and Technology, 37(22), 2003, 5135-5142.

[4] X. Querol, M. Viana, A. Alastuey, F. Amato, T. Moreno, S. Castillo, P. Salvador, Source origin of trace elements in PM from regional background, urban and industrial sites of Spain, Atmospheric Environment, 41(34), 2007, 7219-7231.

[5] N. Janssen, G. Hoek, M. Simic-Lawson, P. Fischer, L. Bree, H. Brink, M. Keuken, R. Atkinson, H. Anderson, B. Brunekreef, F. Cassee, Black carbon as an additional indicator of the adverse health effects of airborne particles compared with PM10 and PM2.5, Environmental Health Perspectives, 119(12), 2011,1691-1699.

[6] R. Laumbach, H. Kipen, Respiratory health effects of air pollution: Update on biomass smoke and traffic pollution, Journal of Allergy and Clinical Immunology, 129(1), 2012, 12-3.

[7] People’s Republic of China(PRC) Environmental Protection Agency, China motor vehicle pollution prevention and control annual report, Beijing, 2015.

[8] European Commission, White paper - roadmap to a single European transport area-towards a competitive and resource efficient transport system - impact assessment, European Commission, Brussels, Belgium, 2011.

[9] T. Chang, S. Modzelewski, J. Norbeck, W. Pierson, Tunnel air quality and vehicle emissions, Atmospheric Environment, 15(6), 1981, 1011-1016.

[10] K. Ahn, H. Rakha, A. Trani, M. Aerde, Estimating vehicle fuel consumption and emissions based on instantaneous speed and acceleration levels, Journal of Transportation Engineering, 128(2), 2002, 182-190.

[11] F. Stathopoulos, R. Noland, Induced travel and emissions from traffic flow improvement projects, Transportation Research Record, No.1842, 2003, 57-63.

[12] L. Ntziachristos, Z. Samaras, Copert III, computer programme to calculate emissions from road transport, European Environment Agency, Copenhagen, 2000.

[13] J. Hickman, D. Hassel, R. Joumard, Z. Samaras, S. Sorenson, Methodology for calculating transport emissions and energy consumption, European Commission, Brussels, Belgium, 1999.

[14] ISSRC, IVE model users manual version 2.0 retrieved, International Sustainable Systems Research Center, La Habra, CA, 2014.

[15] United States Environmental Protection Agency(USEPA), MOVES 2004 users guideł-draft, Office of Transportation and Air Quality, US Environmental Protection Agency, Washington, DC, 2004.

[16] N. Davis, J. Lents, M. Osses, N. Nikkila, M. Barth, Part 3: Developing countries: Development and application of an international vehicle emissions model, Transportation Research Record, Np.1939, 2005, 155-165.

[17] A. Halati, H. Lieu, S. Walker, CORSIM-corridor traffic simulation model, The Annual Meeting of the Transportation Research Board, Washington DC, 1997.

[18] M. Madireddy, B. Coensel, A. Can, B. Degraeuwe, B. Beusen, I. Vlieger, D. Botteldooren, Assessment of the impact of speed limit reduction and traffic signal coordination on vehicle emissions using an integrated approach, Transportation Research Part D, 16(7), 2011, 504-508.

[19] PTV, VISSIM user manual-version 3.70, Karlsruhe, Germany, 2003.

[20] Z. Zhou, Application of TJTS simulation system, China Journal of Highway and Transport, 14(z1), 2001, 92-96.

[21] J. Duan, Microscopic traffic simulation of high speed road, Journal of Highway and Transportaion Research and Development, 15(3), 1998, 21-24.

[22] Z. He, Z. Yang, X. Jiang, J. Miao, W. Chen, Research on general simulation system of urban traffic, China Journal of Highway and Transport, 16(1), 2003, 95-98.

[23] J. Park, R. Noland, J. Polak, Microscopic model of air pollutant concentrations: Comparison of simulated results with measured and macroscopic estimates, Transportation Research Record, No.1750, 2001, 64-73.

[24] H. Rakha, K. Ahn, Integration modeling framework for estimating mobile source emissions, Journal of Transportation Engineering, 130(2), 2004, 183-193.

[25] O. Servin, K. Boriboonsomsin, M. Barth, An energy and emissions impact evaluation of intelligent speed adaptation, IEEE conferenc on Intelligent Transportation Systems, Toronto, Canada, 2006, 1257-1262.

[26] Y. Xie, M. Chowdhury, P. Bhavsar, Y. Zhou, Integrated tool for modeling impact of alternative-fueled vehicles on traffic emissions: Case study of Greenville, South Carolina, The Annual Meeting of the Transportation Research Board, Washington DC, 2011.

[27] A. Papson, S. Hartley, K. Kuo, Analysis of emissions at congested and uncongested intersections with motor vehicle emission simulation 2010, Transportation Research Record, No.2270, 2012, 124-131.

[28] H. Abou-Senna, E. Radwan, K. Westerlund, C. Cooper, Using a traffic simulation model (VISSIM) with an emissions model (MOVES) to predict emissions from vehicles on a limited-access highway, Journal of the Air and Waste Management Association, 63(7), 2013, 819-831.

[29] H. Abou-Senna, E. Radwan, Vissim/moves integration to investigate the effect of major key parameters on CO2 emissions, Transportation Research Part D, 21(2), 2013, 39-46.

[30] Y. Zhao, A. Sadek, Computationally-efficient approaches to integrating the moves emissions model with traffic simulators, Procedia Computer Science, 19, 2013, 882-887.

[31] X. Zhou, S. Tanvir, H. Lei, J. Taylor, B. Liu, N. Rouphail, H. Frey, Integrating a simplified emission estimation model and mesoscopic dynamic traffic simulator to efficiently evaluate emission impacts of traffic management strategies, Transportation Research Part D, 37(12), 2015, 123-136.

[32] Quadstone, Quadstone PARAMICS V4.2 processor user manual, Edinburgh, Scotland, 2003

Journal of Artificial Intelligence and Soft Computing Research

The Journal of Polish Neural Network Society, the University of Social Sciences in Lodz & Czestochowa University of Technology

Journal Information

CiteScore 2017: 5.00

SCImago Journal Rank (SJR) 2017: 0.492
Source Normalized Impact per Paper (SNIP) 2017: 2.813


All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 71 71 58
PDF Downloads 55 55 45