Microscopic traffic simulation: A tool for the design, analysis and evaluation of intelligent transport systems
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Abstract
This paper summarises some of the main modelling and interface developments made recently in the AIMSUN microscopic traffic simulator to provide a better response to the requirements for the assessment of ITS systems, advanced transport analysis and ATMS. The description addresses two main areas: improvements on the dynamic assignment capabilities, and the embedding of the simulator in the AIMSUN/ISM (Intermodal Strategy Manager) a versatile graphic environment for model manipulation and simulation based traffic analysis and evaluation of advanced traffic management strategies. AIMSUN/ISM includes two specific tools, the Scenario Analysis Module to generate and simulate the traffic management strategies, and the (ODTool) to generate and manipulate the Origin-Destination matrices describing the mobility patterns required by the dynamic analysis of traffic conditions. The matrix calculation procedures have been implemented on basis to a flexible interface with the EMME/2 transport planning software.
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traffic simulation traffic management intelligent transport systemsPreview
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References
- Banks, J. eds. 1998Handbook of Simulation: Principles, Methodology, Advances, Applications and PracticeWileyNew YorkGoogle Scholar
- Barceló, J.: 2000, The role of simulation in advanced traffic management systems, in: Proc. of the 7th World Conf. on ITS, Torino.Google Scholar
- Barceló, J.: 2001, GETRAM/AIMSUN: A software environment for microscopic traffic analysis, in: Proc. of the Workshop on Next Generation Models for Traffic Analysis, Monitoring and Management, Tucson, AZ, September.Google Scholar
- Barceló, J. and Casas, J.: 1999, The use of neural networks for short-term prediction of traffic demand, in: A. Zeder (ed.), Transportation and Traffic Theory, Pergamon.Google Scholar
- Barceló, J., Casas, J. 2003Dynamic network simulation with AIMSUNProc. of the Internat. Symposium on Transport SimulationKluwerYokohamaGoogle Scholar
- Barceló, J., Casas, J., and Ferrer, J. L.: 1999a, Analysis of dynamic guidance systems by microsimulation with AIMSUN2, in: Proc. of the 6th World Conf. on ITS, Toronto.Google Scholar
- Barceló, J., Casas, J., Ferrer, J. L., García, D. 1999Modeling advanced transport telematic applications with microscopic simulators: The case of AIMSUN2Brilon, W.Huber, F.Schreckenberg, M.Wallentowitz, H. eds. Traffic and Mobility, Simulation, Economics, EnvironmentSpringerBerlinGoogle Scholar
- Barceló, J., Ferrer, J. L., Martin, R. 1999Simulation assisted design and assessment of vehicle guidance systemsInternat. Trans. Oper. Res.6123143Google Scholar
- Barceló, J., Ferrer, J. L., Grau, R., Florian, M., Chabini, I., and Le Saux, E.: 1995, A route based variant of the AIMSUN2 microsimulation model, in: Proc. of the 2nd World Congress on Intelligent Transport Systems, Yokohama.Google Scholar
- Barceló, J., Ferrer, J., Casas, J., Montero, L., and Perarnau, J.: 2000a, Microscopic simulation with AIMSUN for the assessment of incident management strategies, in: Proc. of the e-Safety Conference, Lyon, September.Google Scholar
- Barceló, J., García, D., and Kirschfink, H.: 2000b, Scenario analysis a simulation based tool for regional strategic traffic management, in: Traffic Technology International 2001, Special Issue.Google Scholar
- Ben-Akiva, M. and Bierlaire, M.: 1999, Discrete choice methods and their applications to short term travel decisions, in: Transportation Science Handbook, Kluwer.Google Scholar
- Cascetta, E., Nuzzolo, A., Russo, F., and Vitetta, A.: 1996, A modified logit route choice model overcoming path overlapping problems, in: Proc. of the 13th Internat. Symposium on Transportation and Traffic Flow Theory, Pergamon.Google Scholar
- Chang, G., Wu, J. 1994Recursive estimation of time-varying origin-destination flows from traffic counts in feeway corridorsTransport. Res. B28141160Google Scholar
- Florian, M., Chen, Y. 1995A coordinate descent method for the bi-level OD matrix adjustment problemInternat. Trans. Oper. Res.2165175Google Scholar
- Florian, M., Mahut, M., and Tremblay, N.: 2001, A hybrid optimization-mesoscopic simulation dynamic traffic assignment model, in: Proc. of the 2001 IEEE Intelligent Transport Systems Conference, Oakland, pp. 120–123.Google Scholar
- Gabbard, J. F. 1991Car-following modelsPapageorgiou, M. eds. Concise Encyclopedia of Traffic and Transportation SystemsPergamonOxfordGoogle Scholar
- Gipps, P. G. 1981A behavioral car-following model for computer simulationTransport. Res. B15105111Google Scholar
- Gipps, P. G. 1986A model for the structure of lane-changing decisionsTransport. Res. B20403414Google Scholar
- INRO Consultants: 1999, EMME/2 User’s Manual, Software release 9.0.Google Scholar
- Mahut, M.: 2000, Behavioral car following models, PhD Thesis, Chapter 3, Centre de Recherche sur les Transports, CRT, Université de Montréal.Google Scholar
- Nihan, N. L., Davis, G. A. 1989Application of prediction-error minimization and maximum likelihood to estimate intersection OD matrices from traffic countsTransport. Sci.237790Google Scholar
- Spiess, H.: 1990, A gradient approach for the OD matrix adjustment problem, Publication No. 693, Centre de Recherche sur les Transports, Université de Montréal.Google Scholar
- TSS: 2002, User’s Manuals, Transport Simulation Systems, GETRAM/AIMSUN Version 4.1.Google Scholar
- Zijp, N. J., Hammerslag, R. 1996Improved Kalman filtering approach for estimating origin-destination matrices for freeway corridorsTransport. Res. Record14435464Google Scholar
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