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Ramezani, Amin, Moshiri, Behzad, Rahimi Kian, Ashkan. (1391). The Development of Maximum Likelihood Estimation Approaches for Adaptive Estimation of Free Speed and Critical Density in Vehicle Freeways. نشریه مهندسی عمران امیرکبیر, 44(2), 47-58. doi: 10.22060/eej.2012.362
Amin Ramezani; Behzad Moshiri; Ashkan Rahimi Kian. "The Development of Maximum Likelihood Estimation Approaches for Adaptive Estimation of Free Speed and Critical Density in Vehicle Freeways". نشریه مهندسی عمران امیرکبیر, 44, 2, 1391, 47-58. doi: 10.22060/eej.2012.362
Ramezani, Amin, Moshiri, Behzad, Rahimi Kian, Ashkan. (1391). 'The Development of Maximum Likelihood Estimation Approaches for Adaptive Estimation of Free Speed and Critical Density in Vehicle Freeways', نشریه مهندسی عمران امیرکبیر, 44(2), pp. 47-58. doi: 10.22060/eej.2012.362
Ramezani, Amin, Moshiri, Behzad, Rahimi Kian, Ashkan. The Development of Maximum Likelihood Estimation Approaches for Adaptive Estimation of Free Speed and Critical Density in Vehicle Freeways. نشریه مهندسی عمران امیرکبیر, 1391; 44(2): 47-58. doi: 10.22060/eej.2012.362

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، زمستان 2012، صفحه 47-58  XML اصل مقاله (946 K)
نوع مقاله: Research Paper
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.
کلیدواژه ها
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.
کلیدواژه ها [English]
Maximum Likelihood Estimation, Free Speed, Critical Density, Expectation Maximization Algorithm
مراجع
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[8] Benveniste, A., Metivier, M. and Priouret, P..Adaptive Algorithms and Stochastic Approximation. New York: Springer- Verlag, 1990.
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[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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