ABSTRACT
This paper presents a study on quantifying the exact impact of state estimation error on adaptive traffic signal control. The exact impact of state estimation error can be derived using the vertical queuing model and dynamic programming. The vertical queuing model, albeit a simplistic assumption, can be defined unambiguously, and when used to describe the system dynamics in the dynamic programming formulation, enables an exact global optimum to be computed. We further present a model to estimate the impact of state estimation error on multiple cycles of traffic signal operation, which will then be validated by the results obtained from the dynamic programming process based on the vertical queuing model.
- Robertson, D. I., Bertherton, R. D. 1974. Optimum control of an intersection for any known sequence of vehicular arrivals, Proceedings of the 2nd IFAC-IFIP-IFORS Symposium on Traffic Control and Transportation system, Monte Carlo.Google Scholar
- Gartner, N. H. 1982. Demand-responsive Decentralized Urban Traffic Control, Part 1: Single-intersection Policies, DOT/RSPA/DPB-50/81/24, U. S. Department of Transportation.Google Scholar
- Gartner, N. H. 1983a. Demand-responsive Decentralized Urban Traffic Control, Part 2: Network Extensions, DOT/RSPA/P-34/85/009, U. S. Department of Transportation.Google Scholar
- Gartner, N. H., 1983b. OPAC: A demand-responsive strategy for traffic signal control, Transportation Research Record 906, 75--81.Google Scholar
- Henry, J. J., Farges, J. L., Tuffal, J., 1983. The PRODYN real time traffic algorithm, Proceedings of the 4th IFAC-IFIP-IFORS conference on Control in Transportation Systems, 307--311.Google ScholarCross Ref
- Mirchandani, P., Head, L. 2001. RHODES: a real-time traffic signal control system: architecture, algorithms, and analysis, Transportation Res. C, 9(6), 415--432.Google ScholarCross Ref
- Cai, C., Wong, C. K., Heydecker, B. G. 2009. Adaptive traffic signal control using approximate dynamic programming, Transportation Research Part C, 17(5), 456--474.Google ScholarCross Ref
- Cai, C., Hengs T, B., Ye, G., Huang, E., Wang, Y., Aydos, C., Geers, G. 2009. On the performance of adaptive traffic signal control, in the proceedings of the 2nd International Workshop on Computational Transportation Science, Seattle, U. S., pp. 37--42. Google ScholarDigital Library
- Cai, C., Wang, Y., Geers, G. 2011. Traffic state estimation and signal control performance, in the proceedings of the 90 th Transportation Research Board Annual Meeting, Washington D. C., U. S.Google Scholar
- Robertson, D. I. 1969. TRANSYT: a traffic network study tool. Report LR253, Road Research Laboratory, Ministry of Transport, Crowthorne, Berkshire.Google Scholar
- Ahn, W. Y., 2004. Dynamic optimisation for isolated road junctions, Ph.D thesis, University of London.Google Scholar
- Bellman, R., Dreyfus, S. 1959. Functional approximations and dynamic programming, Mathematic Tables and Other Aids to Computation, 13(68), 247--251.Google Scholar
- Powell, W. B., 2007. Approximate Dynamic Programming: Solving the Curses of Dimensionality. John Wiley & Sons, Inc., Hoboken, New Jersey, ISBN 978-0-470-17155-4. Google ScholarDigital Library
- Cai, C., Le, T. (2010) Approximate dynamic programming controller for multiple intersections, G1-02202 in the proceedings of the 12th World Conference on Transport Research, Lisbon, Portugal, ISBN 978-989-96986-1-1.Google Scholar
Index Terms
- Quantifying the exact impact of state estimation error on traffic signal control
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