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Journal of Information Science and Engineering, Vol. 30 No. 4, pp. 1245-1260 (July 2014)

Traffic Congestion Evaluation and Signal Timing Optimization Based on Wireless Sensor Networks: Issues, Approaches and Simulation*

School of Computer Science and Technology
Dalian University of Technology
Dalian, 116023 P.R. China

This paper proposed the model and algorithms for traffic data monitoring and signal timing optimization based on continuum traffic model and wireless sensor networks. Given the scenario that sensor nodes are sparsely installed along the segment between signalized intersections, an analytical model is built based on continuum traffic equations, and an adaptive interpolation method is proposed to estimate traffic parameters with scattered sensor data. Based on the principle of traffic congestion formation, a congestion factor is introduced which can be used to evaluate the real-time status of traffic congestion along the segment, and to predict the subcritical state of traffic jams. The result is expected to support the signal timing optimization of traffic light control for the purpose to avoid traffic jams before its formation. We simulated the traffic monitoring based on Mobile Century dataset, and analyzed the performance of signal control on VISSIM platform when congestion factor is introduced into the phase optimization model. The simulation result shows that this method can improve the spatial-temporal resolution of traffic data monitoring, and its helpful to alleviate urban traffic congestion that remarkably decreases the average delays and maximum queue length.

Keywords: intelligent transportation system, traffic surveillance, wireless sensor networks, traffic congestion evaluation, congestion factor, cost function, traffic flow theory, scattered data fitting, timing phase optimization, multi-objective optimization

Full Text () Retrieve PDF document (201407_18.pdf)

Received July 16, 2012; revised November 5, 2012; accepted January 14, 2013.
Communicated by Chung-Ta King.
* This work was supported in part by the National High Technology Research and Development 863 Program of China under Grant No. 2012AA111902, the National Key Technology R&D Program of China under Grant No. 2011BAK02B02, the National Natural Science Foundation of China under Grant No. 60873256, and the Fundamental Research Funds for the Central Universities under Grant No. DUT12JS01.