A Traffic Monitoring Based on Vehicle Density Estimation and Analysis for a Mixed Traffic Flow in a Transport Cross-road
Main Article Content
Abstract
The Traffic Monitoring System (TMS) is an important element in an Intelligent Transportation Sysstem (ITS) for a real-time traffict flow control, especially in a developing country like Vietnam with a mixed traffic flow of vehicles including motorcycles and cars. In our previous research work, the density and length of the vehicles occupied on the road have been determined in the area viewed by a mounted camera on the road. However in this paper, a proper method for traffict flow control in the actual transport scenario in Vietnam is proposed, which monitors vehicle density in the area beyond and in front of traffict lights, as well as in the cross-road area from the mounted camera to estimate the traffic density. The system implementation and experimental results showed the efficiency of the proposed method compared to other convenient ones, where the traffic light signals are fully controlled by the estimated vehicle density captured by the system camera.
Keywords
Traffic monitoring, Traffic light control, Cross-road, TMS, ITS
Article Details
References
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[2] Mianfang Liu, Shengwu Xiong, Xiaohan Yu, Pengfeng Duan and Jun Wang." Behavior characteristics of mixed traffic flow on campus", Computational Intelligence in Vehicles and Transportation Systems (CIVTS), pp. 140-147. 2014.
[3] Mohamed A. Khamis, Walid Gomaa; "Enhanced Multiagent Multi-Objective Reinforcement Learning for Urban Traffic Light Control", International Conference on Machine Learning and Applications 11th, pp. 586-591, 2012.
[4] Maram Bani Younes and Azzedine Boukerche; "Intelligent Traffic Light Controlling Algorithms Using Vehicular Networks", IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, pp. 1-13, 2015.
[5] Massimo Magrini, Davide Moroni, Giovanni Palazzese, Gabriele Pieri, Giuseppe Riccardo Leone, and Ovidio Salvetti, "Computer Vision on Embedded Sensors for Traffic Flow Monitoring", IEEE 18th International Conference on Intelligent Transportation Systems, Spain, pp. 161-166, 2015.
[6] Chris Stauffer and W.E.L Grimson, "Adaptive background mixture models for real-time tracking", Computer Vision and Pattern Recognition, Volume 2, pp. 246-252, 1999.
[7] Nobuyuki Otsu, "A threshold selection method from gray-level histograms", IEEE Transactions on Systems, Man, and Cybernetics, pp. 62-66, 1979.
[8] Transportation Research Board; "Highway Capacity Manual", National Research Council, 2000.
[9] Pallavi Choudekar, Sayanti Banerjee and M.K.Muju, "Implementation of Image Processing in Real Time Traffic Light Control", Electronics Computer Technology (ICECT), 2011 3rd International Conference on, pp. 94-98, 2011.
[10] Nguyen Viet Hung, Le Chung Tran, Nguyen Hoang Dung, Thang Manh Hoang, Nguyen Tien Dzung; "A Traffic Monitoring System For A Mixed Traffic Flow Via Road Estimation And Analysis", International Conference on Communications and Electronics (ICCE) 6th, pp. 375-378, 2016.
[11] Nguyen Viet Hung, Nguyen Hoang Dung, Le Chung Tran, Thang Manh Hoang, Nguyen Tien Dzung; "Vehicle Classification By Estimation Of The Direction Angle In A Mixed Traffic Flow", International Conference on Communications and Electronics (ICCE) 6th, pp. 365-368, 2016.
[12] Girija H. Kulkarni, Poorva G. Waingankar; "Fuzzy Logic Based Traffic Light Controller"; 2007 International Conference on Industrial and Information Systems; pp. 107-110; 2007.
[13] Suhail M. Odeh: "Hybrid algorithm: fuzzy logic-genetic algorithm on traffic light intelligent system"; 2015 ΙΕΕΕ International Conference on Fuzzy Systems (FUZZ-IEEE); pp. 1-7; 2015.