Traffic Density Estimation and Congestion Identification Based on Switched Decentralized State Observer for Large-Scale Urban Freeway Network

In order to estimate traffic flow densities and detect the traffic congestion for the large-scale freeway network, by using the limited known data, a switched decentralized state observer design strategy is proposed. First of all, the road network can be modeled by means of the distributed method, the whole network is divided into several subsystem, and thus a distributed piecewise affine system (DPWAS) model is deduced. Secondly, based on the established model, a switching type of decentralized observer is considered, the road densities of each subsystem is allowed estimated by the local observer, and by comparing the error between the estimated densities and the known congested ones, the congestion case can be further detected. There is no interference between adjacent observers. Finally, the experiment results of Jingtong freeway shown that the proposed method is feasible.

[1]  Huajian Chang,et al.  A novel algorithm for urban traffic congestion detection based on GPS data compression , 2016, 2016 IEEE International Conference on Service Operations and Logistics, and Informatics (SOLI).

[2]  W. Y. Szeto,et al.  Stochastic cell transmission model (SCTM): A stochastic dynamic traffic model for traffic state surveillance and assignment , 2011 .

[3]  Carlos Canudas-de-Wit,et al.  Graph constrained-CTM observer design for the Grenoble south ring , 2012 .

[4]  R. Horowitz,et al.  Globally optimal solutions to the on-ramp metering problem - Part 1 , 2004, Proceedings. The 7th International IEEE Conference on Intelligent Transportation Systems (IEEE Cat. No.04TH8749).

[5]  Ying Wang,et al.  Modeling and Density Estimation of an Urban Freeway Network Based on Dynamic Graph Hybrid Automata , 2017, Sensors.

[6]  Saul Rodriguez,et al.  Real-Time Urban Traffic State Estimation with A-GPS Mobile Phones as Probes , 2012 .

[7]  R. Horowitz,et al.  Methodological calibration of the cell transmission model , 2004, Proceedings of the 2004 American Control Conference.

[8]  Yuqi Guo,et al.  Modeling freeway network by using dynamic graph hybrid automata and estimating its states by designing state observer , 2015, 2015 Chinese Automation Congress (CAC).

[9]  N. Braiek,et al.  Decentralized Observer based Guaranteed Cost Control for Nonlinear Interconnected Systems , 2009 .

[10]  Arun Agrawal,et al.  Traffic congestion detection in vehicular adhoc networks using GPS , 2014 .

[11]  R. Horowitz,et al.  Traffic density estimation with the cell transmission model , 2003, Proceedings of the 2003 American Control Conference, 2003..

[12]  Prabhakar R. Pagilla,et al.  Decentralized output feedback control of a class of large-scale interconnected systems , 2007, IMA J. Math. Control. Inf..

[13]  H Jianming,et al.  Traffic congestion identification based on image processing , 2012 .

[14]  Florian Dörfler,et al.  Continuous-Time Distributed Observers With Discrete Communication , 2013, IEEE Journal of Selected Topics in Signal Processing.

[15]  C. Daganzo.  THE CELL TRANSMISSION MODEL.. , 1994 .

[16]  Roberto Horowitz,et al.  Optimal freeway ramp metering using the asymmetric cell transmission model , 2006 .

[17]  Carlos Canudas de Wit,et al.  A new robust approach for highway traffic density estimation , 2014, 2014 European Control Conference (ECC).

[18]  Carlos F. Daganzo,et al.  THE CELL TRANSMISSION MODEL, PART II: NETWORK TRAFFIC , 1995 .

[19]  E. Yaz.  Linear Matrix Inequalities In System And Control Theory , 1998, Proceedings of the IEEE.

[20]  Ande Chang,et al.  Traffic Congestion Identification Method Based on GPS Equipped Floating Car , 2010, 2010 International Conference on Intelligent Computation Technology and Automation.

[21]  Yangzhou Chen,et al.  Decentralized State-Observer-Based Traffic Density Estimation of Large-Scale Urban Freeway Network by Dynamic Model , 2017, Inf..

[22]  Wei-Hua Lin,et al.  An enhanced 0-1 mixed-integer LP formulation for traffic signal control , 2004, IEEE Trans. Intell. Transp. Syst..

[23]  Chaogui Kang,et al.  Exploring time-dependent traffic congestion patterns from taxi trajectory data , 2015, 2015 2nd IEEE International Conference on Spatial Data Mining and Geographical Knowledge Services (ICSDM).

[24]  L. Alvarez-Icaza,et al.  Adaptive observer for traffic density estimation , 2004, Proceedings of the 2004 American Control Conference.

[25]  Hong K. Lo,et al.  A Cell-Based Traffic Control Formulation: Strategies and Benefits of Dynamic Timing Plans , 2001, Transp. Sci..