Flexible and efficient model-based congestion detection approach

This paper addresses the problem of road traffic congestion detection. We propose an effective approach to detect traffic congestion by combining the piecewise switched linear traffic (PWSL) and Shewhart control scheme. This approach uses PWSL model to describe the evolution of traffic density, and Shewhart chart to detect traffic congestions based on residuals obtained from PWSL model. The PWSL-Shewhart approach is evaluated using traffic data from the four-lane State Route 60 (SR-60) freeway in California. Results indicate that our approach accomplished reliable detection of traffic congestion.

[1]  Xin Jin,et al.  Classification of freeway traffic patterns for incident detection using constructive probabilistic neural networks , 2001, IEEE Trans. Neural Networks.

[2]  Edmond Chin-Ping Chang,et al.  A Neural Network Approach to Freeway Incident Detection , 1992, The 3rd International Conference on Vehicle Navigation and Information Systems,.

[3]  Lyudmila Mihaylova,et al.  Interval Macroscopic Models for Traffic Networks , 2011, IEEE Transactions on Intelligent Transportation Systems.

[4]  Atsuhiro Takasu,et al.  Real-time traffic incident detection using a probabilistic topic model , 2015, Inf. Syst..

[5]  Fouzi Harrou,et al.  Monitoring road traffic congestion using a macroscopic traffic model and a statistical monitoring scheme , 2017 .

[6]  Fouzi Harrou,et al.  Integrating Model-Based Observer and Kullback–Leibler Metric for Estimating and Detecting Road Traffic Congestion , 2018, IEEE Sensors Journal.

[7]  Baher Abdulhai,et al.  Development Testing And Evaluation Of Advanced Techniques For Freeway Incident Detection , 1997 .

[8]  Billy M. Williams,et al.  Traffic Management Center Use of Incident Detection Algorithms: Findings of a Nationwide Survey , 2007, IEEE Transactions on Intelligent Transportation Systems.

[9]  Fred Spiring,et al.  Introduction to Statistical Quality Control , 2007, Technometrics.

[10]  S. Kechida,et al.  Calibration and validation of a switched linear macroscopic traffic model , 2015, 2015 3rd International Conference on Control, Engineering & Information Technology (CEIT).

[11]  Dipti Srinivasan,et al.  Support vector machine models for freeway incident detection , 2003, Proceedings of the 2003 IEEE International Conference on Intelligent Transportation Systems.

[12]  Sihem Kechida,et al.  A piecewise switched linear approach for traffic flow modeling , 2017, Int. J. Autom. Comput..

[13]  Jaimyoung Kwon,et al.  Automatic Calibration of the Fundamental Diagram and Empirical Observations on Capacity , 2009 .

[14]  Javier A. Barria,et al.  Detection and Classification of Traffic Anomalies Using Microscopic Traffic Variables , 2011, IEEE Transactions on Intelligent Transportation Systems.

[15]  Yang Zhao,et al.  Research on campus traffic congestion detection using BP neural network and Markov model , 2016, J. Inf. Secur. Appl..

[16]  Farid Kadri,et al.  Seasonal ARMA-based SPC charts for anomaly detection: Application to emergency department systems , 2016, Neurocomputing.

[17]  Francesco Marcelloni,et al.  Detection of traffic congestion and incidents from GPS trace analysis , 2017, Expert Syst. Appl..

[18]  Xiang Cheng,et al.  D2D for Intelligent Transportation Systems: A Feasibility Study , 2015, IEEE Transactions on Intelligent Transportation Systems.

[19]  Alexandre M. Bayen,et al.  Traffic state estimation on highway: A comprehensive survey , 2017, Annu. Rev. Control..

[20]  Fouzi Harrou,et al.  An efficient statistical-based approach for road traffic congestion monitoring , 2017, 2017 5th International Conference on Electrical Engineering - Boumerdes (ICEE-B).

[21]  Ying Sun,et al.  Statistical fault detection in photovoltaic systems , 2017 .

[22]  Ludovica Adacher,et al.  A macroscopic model with the advantages of microscopic model: A review of Cell Transmission Model's extensions for urban traffic networks , 2018, Simul. Model. Pract. Theory.

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

[24]  Ying Sun,et al.  Traffic congestion detection based on hybrid observer and GLR test , 2018, 2018 Annual American Control Conference (ACC).