New Calibration Methodology for Microscopic Traffic Simulation Using Enhanced Simultaneous Perturbation Stochastic Approximation Approach
Abstract
Traffic simulation models can represent real-world conditions such as delays, travel times, queues, and flows. However, accurate evaluation of these traffic conditions depends on the selection of model parameters and the calibration methodology. Most previous calibration studies have focused on minimizing the sum of the differences between the observed data and simulation output during a certain time period on a typical day. However, to capture a realistic distribution of all possible traffic conditions, a more general calibration methodology that can be used with any traffic condition is required. This paper proposes a new calibration methodology–-the Bayesian sampling approach in conjunction with the application of the simultaneous perturbation stochastic approximation (SPSA) optimization method [enhanced SPSA (E-SPSA)]–-that enables the modeler to enhance calibration by considering statistical data distribution. Instead of calibrating with input data for certain time periods, calibration is performed with data obtained from a complete input distribution. Mean square variation (MSV) was used to evaluate the accuracy of the proposed E-SPSA calibration approach. On the basis of the MSV of flows, the MSV value of the E-SPSA methodology was found to be 0.940, which was greater than the variation of speed obtained from SPSA-only (0.897) or from a variation approach (0.888). Thus, this proposed methodology not only makes it possible to overcome some of the limitations of previous calibration approaches, but also improves the results of simulation model calibration by accurately capturing a wide range of real-world conditions. Future work will focus on testing the proposed calibration methodology using more extensive data sets and models developed using simulation tools other than PARAMICS.
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References
1. Lee D.-H., Yang X., and Chandrasekar P. Parameter Calibration for PARAMICS Using Genetic Algorithm. Presented at 80th Annual Meeting of the Transportation Research Board, Washington, D.C., 2001.
2. Schultz G. G., and Rilett L. R. Analysis of Distribution and Calibration of Car-Following Sensitivity Parameters in Microscopic Traffic Simulation Models. In Transportation Research Record: Journal of the Transportation Research Board, No. 1876, Transportation Research Board of the National Academies, Washington, D.C., 2004, pp. 41–51.
3. Park B., and Qi H. Development and Evaluation of a Procedure for the Calibration of Simulation Models. In Transportation Research Record: Journal of the Transportation Research Board, No. 1934, Transportation Research Board of the National Academies, Washington, D.C., 2005, pp. 208–217.
4. Kim S.-J., Kim W., and Rilett L. R. Calibration of Microsimulation Models Using Nonparametric Statistical Techniques. In Transportation Research Record: Journal of the Transportation Research Board, No. 1935, Transportation Research Board of the National Academies, Washington, D.C., 2005, pp. 111–119.
5. Ma T., and Abdulhai B. Genetic Algorithm-Based Optimization Approach and Generic Tool for Calibrating Traffic Microscopic Simulation Parameters. In Transportation Research Record: Journal of the Transportation Research Board, No. 1800, Transportation Research Board of the National Academies, Washington, D.C., 2002, pp. 6–15.
6. Ciuffo B. F., Punzo V., and Torrieri V. Comparison of Simulation-Based and Model-Based Calibrations of Traffic-Flow Microsimulation Models. In Transportation Research Record: Journal of the Transportation Research Board, No. 2088, Transportation Research Board of the National Academies, Washington, D.C., 2008, pp. 36–44.
7. Kim K.-O., and Rilett L. R. Simplex-Based Calibration of Traffic Microsimulation Models with Intelligent Transportation Systems Data. In Transportation Research Record: Journal of the Transportation Research Board, No. 1855, Transportation Research Board of the National Academies, Washington, D.C., 2003, pp. 80–89.
8. Kundé K. K. Calibration of Mesoscopic Traffic Simulation Models for Dynamic Traffic Assignment. Master's thesis. Massachusetts Institute of Technology, Cambridge, 2002.
9. Ding L. Evaluation of ITS Technologies in South Jersey Priority Corridor Project. Master's thesis. Rutgers, State University of New Jersey, Piscataway, 2003.
10. Ma J., Dong H., and Zhang H. M. Calibration of Microsimulation with Heuristic Optimization Methods. In Transportation Research Record: Journal of the Transportation Research Board, No. 1999, Transportation Research Board of the National Academies, Washington, D.C., 2007, pp. 208–217.
11. Balakrishna R., Antoniou C., Ben-Akiva M. E., Koutsopoulos H. N., and Wen Y. Calibration of Microscopic Traffic Simulation Models: Methods and Application. In Transportation Research Record: Journal of the Transportation Research Board, No. 1999, Transportation Research Board of the National Academies, Washington, D.C., 2007, pp. 198–207.
12. Hollander Y., and Liu R. The Principles of Calibrating Traffic Micro-simulation Models. Transportation, Vol. 35, No. 3, 2008, pp. 347–362.
13. Lee J. B., and Ozbay K. Calibration of a Macroscopic Traffic Simulation Model Using Enhanced Simultaneous Perturbation Stochastic Approximation Methodology. Presented at 87th Annual Meeting of the Transportation Research Board, Washington, D.C., 2008.
14. Waller S. T., Schofer J. L., and Ziliaskopoulos A. K. Evaluation with Traffic Assignment Under Demand Uncertainty. In Transportation Research Record: Journal of the Transportation Research Board, No. 1771, TRB, National Research Council, Washington, D.C., 2001, pp. 69–74.
15. Molina G., Bayarri M. J., and Berger J. O. Statistical Inverse Analysis for a Network Microsimulator. American Statistical Association and the American Society for Quality Technometrics, Vol. 47, No. 4, 2005.
16. Spall J. C. Multivariate Stochastic Approximation Using a Simultaneous Perturbation Gradient Approximation. IEEE Transactions on Automatic Control, Vol. 37, No. 3, 1992, pp. 332–341.
17. Robbins H., and Monro S. A Stochastic Approximation Method. Annals of Mathematical Statistics, Vol. 22, No. 3, 1951, pp. 400–407.
18. Spall J. C. Introduction to Stochastic Search and Optimization. Wiley-Interscience, Hoboken, N.J., 2003.
19. Spall J. C. Implementation of the Simultaneous Perturbation Algorithm for Stochastic Optimization. IEEE, Vol. 34, No. 3, 1998, pp. 817–823.
20. Spall J. C. Traffic-Responsive Signal Timing for System-Wide Traffic Control. Transportation Research Part C, Vol. 5, No. 3/4, 1997, pp. 154–163.
21. Luo J. Dynamic Multi-Ramp Metering Control with Simultaneous Perturbation Stochastic Approximation (SPSA). PhD thesis. New Jersey Institute of Technology, Newark, 2003.
22. Skabardonis A., Petty K., Varaiya P., and Bertini R. Evaluation of the Freeway Service Patrol (FSP) in Los Angeles, California. PATH Research Report. UCB-ITS-PRR-98-31. 1998.
24. Miller I., Freund J. E., and Johnson R. A. Probability and Statistics for Engineers, 4th ed., Prentice Hall, Englewood Cliffs, N.J., 1990.
25. www.itl.nist.gov/div898/handbook/eda/section3/eda35g.htm. Accessed May 30, 2008.
26. Wikipedia. http://en.wikipedia.org/wiki/Main_Page/. Accessed May 25, 2008.
27. Gardes Y., May A. D., Dahlgren J., and Skabardonis A. Freeway Calibration and Application of the PARAMICS Model. Presented at 81st Annual Meeting of the Transportation Research Board, Washington, D.C., 2002.
28. Toledo T., Ben-Akiva M. E., Darda D., Jha M., and Koutsopoulos H. N. Calibration of Microscopic Traffic Simulation Models with Aggregate Data. In Transportation Research Record: Journal of the Transportation Research Board, No. 1876, Transportation Research Board of the National Academies, Washington, D.C., 2004, pp. 10–19.
29. Sanwal K. K., Petty K., Walrand J., and Fawaz Y. An Extended Macroscopic Model for Traffic Flow. Transportation Research Part B, Vol. 30, No. 1, 1996, pp. 1–9.
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Article first published online: January 1, 2009
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