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First published online January 1, 2012

Numerical Analysis for Adaptive Traffic Simulation

Abstract

Because of the increased interest in modeling multiresolution traffic simulation, the adaptive simulation concept and approach are addressed. Adaptive simulation does not predefine the spatial-temporal domain of high-fidelity simulation rules, but rather uses driving conditions and driver response. Adaptive simulation is intended not to couple two or more simulation models of known resolution but to create a finer scale of resolution with a range of rules to allow smoother transition in the spatial-temporal resolution domain. The adaptive simulation concept is implemented in the anisotropic mesoscopic simulation framework, in which a lane-based simulation framework and lane-changing rules, as well as a triggering mechanism, are devised. The numerical experiments highlight the difference between hybrid and adaptive simulation. The general property, such as sensitivity of the adaptive simulation model with respect to its parameter, is also tested, and its performance reported.

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References

1. The McGraw-Hill Dictionary of Scientific and Technical Terms. McGraw-Hill, New York, 2002.
2. Qiu L., Yang Y. R., Zhang Y., and Xie H. On Self Adaptive Routing in Dynamic Environments: An Evaluation and Design Using a Simple, Probabilistic Scheme. Proc., 12th IEEE International Conference on Network Protocols, Berlin, 2004.
3. Wu E. H.-K., and Huang Y.-Z. Dynamic Adaptive Routing for a Heterogeneous Wireless Network. Mobile Networks and Applications, Vol. 9, 2004, pp. 219–233.
4. Gao S. Optimal Adaptive Routing and Traffic Assignment in Stochastic Time-Dependent Networks. PhD dissertation. Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, Cambridge, 2005.
5. Quod Financial. Adaptive Smart Order Router (ASOR). 2011. http://www.quodfinancial.com/products/qf-asor.html.
6. Mirchandani P., and Head L. A Real-Time Traffic Signal Control System: Architecture, Algorithms, and Analysis. Transportation Research Part C, Vol. 9, 2001, pp. 416–432.
7. Hidas P. Modelling Lane Changing and Merging in Microscopic Traffic Simulation. Transportation Research Part C, Vol. 10, 2001 pp. 351–371.
8. Buckley D. J. A Semi-Poisson Model of Traffic Flow. Transportation Science, Vol. 2, 1968, pp. 107–132.
9. Botma H. Traffic Flow Models: State-of-the-Art Report. SWOV Institute for Road Safety Research, Leidschendam, Netherlands, 1978.
10. Nelson P., and Sopasakis A. The Prigogine-Herman Kinetic Model Predicts Widely Scattered Traffic Flow Data at High Concentrations. Transportation Research Part B, Vol. 32, 1998, pp. 589–604.
11. Gawron C. An Iterative Algorithm to Determine the Dynamic User Equilibrium in a Traffic Simulation Model. International Journal of Modern Physics C, Vol. 9, 1998, pp. 393–407.
12. Cetin N., Nagel K., Raney B., and Voellmy A. Large-Scale Multi-Agent Transportation Simulations. Computer Physics Communications, Vol. 147, 2002, pp. 559–564.
13. Jayakrishnan R., Mahmassani H. S., and Hu T. Y. An Evaluation Tool for Advanced Traffic Information and Management System in Urban Networks. Transportation Research Part C, Vol. 2, 1994, pp. 129–147.
14. Ben-Akiva M., Bierlaire M., Burton D., Koutsopoulos H.N., and Mishalani R. Network State Estimation and Prediction for Real-Time Traffic Management. Networks and Spatial Economics, Vol. 1, 2001, pp. 293–318.
15. Mahmassani H. S. Dynamic Network Traffic Assignment and Simulation Methodology for Advanced System Management Applications. Networks and Spatial Economics, Vol. 1, 2001, pp. 267–292.
16. Daganzo C. F. Requiem for Second-Order Fluid Approximations of Traffic Flow. Transportation Research Part B, Vol. 29, 1995, pp. 277–286.
17. Chiu Y.-C., Zhou L., and Song H. Development and Calibration of the Anisotropic Mesoscopic Simulation Model for Uninterrupted Flow Facilities. Transportation Research Part B, Vol. 44, 2010, pp. 152–174.
18. Shelton J. I-10 East Corridor Improvement Study Using Multi-Resolution Dynamic Traffic Simulation Approach: Final Report. Texas Transportation Institute, Texas A&M University System, El Paso, 2007.
19. Zheng H., Chiu Y.-C., Mirchandani P. B., and Hickman M. Modeling of Evacuation and Background Traffic for Optimal Zone-Based Vehicle Evacuation Strategy. In Transportation Research Record: Journal of the Transportation Research Board, No. 2196, Transportation Research Board of the National Academies, Washington, D.C., 2010, pp. 65–74.
21. Jayakrishnan R., Mahmassani H. S., and Hu T. Y. An Evaluation Tool for Advanced Traffic Information and Management Systems in Urban Networks. Transportation Research Part C, Vol. 2, No. 3, 1994, pp. 129–147.
22. Jayakrishnan R., Oh J.-S., and Sahraoui A.-E.-K. Calibration and Path Dynamics Issues in Microscopic Simulation for Advanced Traffic Management and Information Systems. In Transportation Research Record: Journal of the Transportation Research Board, No. 1771, TRB, National Research Council, Washington, D.C., 2001, pp. 9–17.
23. Burghout W. Hybrid Microscopic-Mesoscopic Traffic Simulation. Department of Infrastructure, Division of Transportation and Logistics, Royal Institute of Technology, Stockholm, Sweden, 2004.
24. Ben-Akiva M., Koutsopoulos H.N., Toledo T., Yang Q., Choudhury C.F., Antoniou C., and Balakrishna R. Traffic Simulation with Mitsimlab. Fundamentals of Traffic Simulation, Vol. 145, 2010, pp. 233–268.
25. Burghout W., and Wahlstedt J. Hybrid Traffic Simulation with Adaptive Signal Control. In Transportation Research Record: Journal of the Transportation Research Board, No. 1999, Transportation Research Board of the National Academies, Washington, D.C., 2007, pp. 191–197.
26. Yang Q., and Slavin H. High Fidelity, Wide Area Traffic Simulation Model. Caliper Corporation, Boston, Mass., 2002.
27. Casas J., Ferrer J. L., Garcia D., Perarnau J., and Torday A. Traffic Simulation with Aimsun. Fundamentals of Traffic Simulation, Vol. 145, 2010, pp. 173–232.
28. Toledo T., Koutsopoulos H. N., and Ben-Akiva M. E. Modeling Integrated Lane-Changing Behavior. In Transportation Research Record: Journal of the Transportation Research Board, No. 1857, Transportation Research Board of the National Academies, Washington, D.C., 2003, pp. 30–38.
29. Hidas P. Modelling Lane Changing and Merging in Microscopic Traffic Simulation. Transportation Research Part C, Vol. 10, 2002, pp. 351–371.
30. Freeway Lane Selection Algorithm: NGSIM Factsheet. FHWA-HRT-06–136. FHWA, U.S. Department of Transportation, 2006.
31. Rickert M., Nagel K., Schreckenberg M., and Latour A. Two Lane Traffic Simulations Using Cellular Automata. Physica A, Vol. 231, 1996, pp. 534–550.

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Article first published online: January 1, 2012
Issue published: January 2012

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© 2012 National Academy of Sciences.
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Authors

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Ye Tian
Department of Civil Engineering and Engineering Mechanics, University of Arizona, Civil Engineering Building, Suite 100, 1209 East Second Street, Tucson, AZ 85721.
Yi-Chang Chiu
Department of Civil Engineering and Engineering Mechanics, University of Arizona, Civil Engineering Building, Suite 100, 1209 East Second Street, Tucson, AZ 85721.

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