Large-Scale Agent-Based Transport Simulation in Shanghai, China
Abstract
The activity-based model system is being coined as the next-generation demand-forecasting model. The agent-based transport simulation toolkit MATSIM is a fully integrated system that models decisions from the long term to the short term, and these decisions in MATSIM are activity-based models. This paper describes the application of MATSIM in a large-scale multiagent-based transport simulation for Shanghai, China. First, algorithms for integrating new data in Shanghai with MATSIM inputs such as synthetic population, facilities, and network are separately designed according to data characteristics. Then activity-based modeling is introduced to generate population plans, and activity replanning is employed to learn the better travel plans; a utility-based approach is used to model scoring for a plan. Finally, a full MATSIM-based simulation platform for the Shanghai scenario is built in detail. The scenario contains 200,000 synthetic persons simulated on a network with 50,000 links. The relaxed state of the simulation system is reached after 100 iterations of replanning procedures, and the mode choice, route choice, and activity time allocation modules are used to optimize agents’ activity plans. The feasibility of transport simulation in Shanghai by MATSIM is validated against the mode split and the observed counts. Extensive simulation results for the designed Shanghai simulation scenarios indicate that most of the observed counts match quite well with the traffic simulation volumes and demonstrate the potential of MATSIM for large-scale dynamic transport simulation.
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References
1. Pas E. I. Recent Advances in Activity-Based Travel Demand Modeling. Proc., Activity-Based Travel Forecasting Conference, U.S. Department of Transportation, 1996.
2. Ben-Akiva M., and Lerman S. R. Discrete Choice Analysis: Theory and Application Analysis. MIT Press, Cambridge, Mass., 1985.
3. Kitamura R. An Evaluation of Activity-Based Travel Analysis. Journal of Transportation, Vol. 15, No. 1, 1988, pp. 9–34.
4. Bowman J. L., and Ben-Akiva M. Activity-Based Disaggregate Travel Demand Model System with Activity Schedules. Transportation Research Part A: Policy and Practice, Vol. 35, No. 1, 2000, pp. 1–28.
5. Chapin F. S. Human Time Allocation in the City. Human Activity and Time Geography, Vol. 2, 1978, pp. 13–26.
6. Bunch D. S., Bradley M., Golob T. F., Kitamura R., and Occhiuzzo G. P. Demand for Clean-Fuel Vehicles in California: A Discrete-Choice Stated Preference Pilot Project. Transportation Research Part A: Policy and Practice, Vol. 27, No. 3, 1993, pp. 237–253.
7. Nagel K., and Flötteröd G. Agent-Based Traffic Assignment: Going from Trips to Behavioral Travelers. Proc., 12th International Conference on Travel Behavior Research, Jaipur, Rajasthan, India, Dec. 13–18, 2009.
8. Erath A., and Axhausen K. W. Urban Sustainability Transportation: Research Framework for Medium and Long-Term Transport Planning. Proc., 16th International Conference of the Hong Kong Society for Transportation Studies, Hong Kong, China, 2011.
9. Bekhor S., Dobler C., and Axhausen K. W. Integration of Activity-Based with Agent-Based Models: Case of Tel Aviv, Israel. In Transportation Research Record: Journal of the Transportation Research Board, No. 2255, Transportation Research Board of the National Academies, Washington, D.C., 2011, pp. 38–47.
10. Shanghai City Comprehensive Transportation Planning Institute. Fourth Travel Survey of Residents in Shanghai. Shanghai City Transportation Planning Institute, China, 2011.
11. MATSIM. 4.3.2 Network. http://www.matsim.org/node/608. Accessed 2011.
12. Institute, Shanghai City Comprehensive Transportation Planning. City Development and Traffic Demand. Shanghai Comprehensive Transportation Annual Report, China, 2011.
13. Meister K., Balmer M., Ciari F., Horni A., Rieser M., Waraich R. A., and Axhausen K. W. Large-Scale Agent-Based Travel Demand Optimization Applied to Switzerland, Including Mode Choice. Proc., 12th World Conference on Transportation Research, Lisbon, Portugal, 2010.
14. Müller K., and Axhausen K. W. Hierarchical IPF: Generating a Synthetic Population for Switzerland. Proc., 11th Swiss Transport Research Conference, Ascona, Switzerland, 2011.
15. Ciari F., Balmer M., and Axhausen K. W. A New Mode Choice Model for a Multi-Agent Transport Simulation. Proc., 8th Swiss Transport Research Conference, Ascona, Switzerland, 2008.
16. Lefebvre N., and Balmer M. Fast Shortest Path Computation in Time-Dependent Traffic Networks. Proc., 7th Swiss Transport Research Conference, Ascona, Switzerland, 2007.
17. Rieser M. Adding Transit to an Agent-Based Transportation Simulation. Swiss Federal Institute of Technology, Zurich, 2010.
18. Hu Q., Shi Q., Zhang W., and Zhang A. Ideal Decision-Making Method of Urban Public Traffic Line Network Optimization. Journal of Traffic and Transportation Engineering, Vol. 5, No. 1, 2005, pp. 82–85.
19. Balmer M., Raney B., and Nagel K. Adjustments of Activity Timing and Duration in an Agent-Based Traffic Flow Simulation. Progress in Activity-Based Analysis. Elsevier Science, London, 2005, pp. 91–114.
20. Miller E. J., Roorda M. J., and Carrasco J. A. A Tour-Based Model of Travel Mode Choice. Journal of Transportation, Vol. 32, No. 4, 2005, pp. 399–422.
21. Charypar D., and Nagel K. Generating Complete All-Day Activity Plans with Genetic Algorithms. Journal of Transportation, Vol. 32, No. 4, 2005, pp. 369–397.
22. Shanghai Statistical Yearbook, 2010. http://www.stats-sh.gov.cn/data/toTjnj.xhtml?y=2010.
23. Shanghai Open Street Map, 2011. Cloudmade. http://downloads.cloudmade.com/asia/eastern_asia/china/shanghai.
24. Rieser M., and Nagel K. Combined Agent-Based Simulation of Private Car Traffic and Transit. International Association for Travel Behaviour Research, 2009.
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Article first published online: January 1, 2013
Issue published: January 2013
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