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The Development of Maximum Likelihood Estimation Approaches for Adaptive Estimation of Free Speed and Critical Density in Vehicle Freeways | ||
| AUT Journal of Electrical Engineering | ||
| مقاله 5، دوره 44، شماره 2، دی 2012، صفحه 47-58 اصل مقاله (946.25 K) | ||
| نوع مقاله: Research Article | ||
| شناسه دیجیتال (DOI): 10.22060/eej.2012.362 | ||
| نویسندگان | ||
| Amin Ramezani* 1؛ Behzad Moshiri2؛ Ashkan Rahimi Kian3 | ||
| 1Assistant Professor, Control and Intelligent Processing Center of Excellence, School of ELec & Comp, Engineering, University of Tehran, Tehran, Iran | ||
| 2Professor, Control and Intelligent Processing Center of Excellence, School of ELec, & Comp. Engineering, University of Tehran, Tehran, Iran | ||
| 3Associate Professor, Control and Intelligent Processing Center of Excellence, School of ELec & Comp, Engineering, University of Tehran, Tehran, Iran | ||
| چکیده | ||
| The performance of many traffic control strategies depends on how much the traffic flow models are accurately calibrated. One of the most applicable traffic flow model in traffic control and management is LWR or METANET model. Practically, key parameters in LWR model, including free flow speed and critical density, are parameterized using flow and speed measurements gathered by inductive loop detectors and Closed-Circuit TV. The challenging problem here is continuous changes in these parameters due to traffic conditions (traffic composition, incidents) and environmental factors (dense fog, strong wind, snow) and missing data. In this paper Maximum Likelihood approaches are developed to the LWR model identification while inaccurate observations are available at the traffic control center. A Maximum Likelihood method is accomplished via the employment of an Expectation Maximization algorithm. To approximate first and second derivatives of optimal filter without sticking in analytical complexities, The EM algorithm is implemented based on particle filters and smoothers. Two convincing simulation results for two sets of field traffic data are used to demonstrate the effectiveness of the proposed approaches. | ||
| کلیدواژه‌ها | ||
| Maximum Likelihood Estimation؛ Free Speed؛ Critical Density؛ Expectation Maximization Algorithm | ||
| عنوان مقاله [English] | ||
| The Development of Maximum Likelihood Estimation Approaches for Adaptive Estimation of Free Speed and Critical Density in Vehicle Freeways | ||
| چکیده [English] | ||
| The performance of many traffic control strategies depends on how much the traffic flow models are accurately calibrated. One of the most applicable traffic flow model in traffic control and management is LWR or METANET model. Practically, key parameters in LWR model, including free flow speed and critical density, are parameterized using flow and speed measurements gathered by inductive loop detectors and Closed-Circuit TV. The challenging problem here is continuous changes in these parameters due to traffic conditions (traffic composition, incidents) and environmental factors (dense fog, strong wind, snow) and missing data. In this paper Maximum Likelihood approaches are developed to the LWR model identification while inaccurate observations are available at the traffic control center. A Maximum Likelihood method is accomplished via the employment of an Expectation Maximization algorithm. To approximate first and second derivatives of optimal filter without sticking in analytical complexities, The EM algorithm is implemented based on particle filters and smoothers. Two convincing simulation results for two sets of field traffic data are used to demonstrate the effectiveness of the proposed approaches. | ||
| کلیدواژه‌ها [English] | ||
| Maximum Likelihood Estimation, Free Speed, Critical Density, Expectation Maximization Algorithm | ||
| مراجع | ||
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[1] M. Papageorgiou, J. Blosseviller, and H. Hadj-Salem, “Modelling and real-time control of traffic flow on the southern part of boulevard peripherique in paris: Part i: modelling”, Transportation Research, vol. 24A, no. 5, pp. 345– 359, 1990. [2] Y. Wang, M. Papageorgiou, and A. Messmer, “A real-time freeway network traffic surveillance tool”, IEEE Trans. Control Systems Technology, vol. 14, no. 1, pp. 18– 32, 2006. [3] L. Ljung., “System identification, Theory for the user. System sciences series”, Prentice Hall, Upper Saddle River, NJ, USA, second edition, 1999. [4] T. Soderstrom and P. Stoica, “System identification. Systems and Control Engineering”, Prentice Hall, 1989. [5] Doucet, A., de Freitas, J.F.G. and Gordon N.J. (eds.), “Sequential Monte Carlo Methods in Practice”, New York: Springer- Verlag, 2001. [6] Guyader, A., LeGland, F. and Oudjane, N., “A particle implementation of the recursive MLE for partially observed diffusions”, Proceedings of the 13th IFAC Symposium on System Identfication, 1305- 1310, 2003. [7] Cerou F., LeGland F. and Newton N.J., “Stochastic particle methods for linear tangent equations in Optimal Control and PDE's- Innovations and Applications”, eds. J. Menaldi, E. Rofman, 2001. [8] Benveniste, A., Metivier, M. and Priouret, P..Adaptive Algorithms and Stochastic Approximation. New York: Springer- Verlag, 1990. [9] Spall J. C., “Adaptive stochastic approximation by the simultaneous perturbation method”, IEEE Trans. Autom. Contr., vol. 45, pp. 1839- 1853, 2000. [10] Bertsekas D., “Nonlinear Programming”, 2nd Edition, Athena Scientific,1999. [11] A. Coquelin, R. Deguest, and R. Munos, “Numerical methods for sensitivity analysis of Feynman- Kac models”, Technical Report INRIA- 00125427, INRIA, France, January, 2007. [12] Doucet, A., de Freitas, N. and Gordon, N. (Eds.), “Sequential Monte Carlo Methods in Practice”, Springer Verlag, 2001. [13] M. Papageorgiou, J. Blosseviller, and H. Hadj-Salem, “Modelling and real-time control of traffic flow on the southern part of boulevard peripherique in paris: Parti: modelling”, Transportation Research, vol. 24A, no. 5, pp. 345–359, 1990. [14] Doucet, A. and Tadic, V.B., “Parameter estimation in general state-space models using particle methods”, Ann. Inst. Stat. Math., 55, 409- 422, 2003. [15] N. J. Gordon, D. J. Salmond, and A. F. M. Smith., “Novel approach to nonlinear/non-Gaussian Bayesian state estimation”, In IEE Proceedings on Radar and Signal Processing, volume 140, pages 107– 113, 1993. [16] B. Coifman, D. Lyddy, and A. Sabardonis, “The berkeley highway laboratory-building on the i-80 field experiment”, in Proc. IEEE ITS Council Annual Meeting, pp. 5– 10, 2000. | ||
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