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Traffic Responsive Signal Timing Plan Generation based on Neural Network

Traffic Responsive Signal Timing Plan Generation based on Neural Network

Azzam-ul-Asar, M. Sadeeq Ullah, Mudasser F. Wyne, Jamal Ahmed, Riaz-ul-Hasnain
Copyright: © 2011 |Pages: 12
ISBN13: 9781609605957|ISBN10: 1609605950|EISBN13: 9781609605964
DOI: 10.4018/978-1-60960-595-7.ch012
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MLA

Azzam-ul-Asar, et al. "Traffic Responsive Signal Timing Plan Generation based on Neural Network." Intelligent, Adaptive and Reasoning Technologies: New Developments and Applications, edited by Vijayan Sugumaran, IGI Global, 2011, pp. 229-240. https://doi.org/10.4018/978-1-60960-595-7.ch012

APA

Azzam-ul-Asar, Ullah, M. S., Wyne, M. F., Ahmed, J., & Riaz-ul-Hasnain. (2011). Traffic Responsive Signal Timing Plan Generation based on Neural Network. In V. Sugumaran (Ed.), Intelligent, Adaptive and Reasoning Technologies: New Developments and Applications (pp. 229-240). IGI Global. https://doi.org/10.4018/978-1-60960-595-7.ch012

Chicago

Azzam-ul-Asar, et al. "Traffic Responsive Signal Timing Plan Generation based on Neural Network." In Intelligent, Adaptive and Reasoning Technologies: New Developments and Applications, edited by Vijayan Sugumaran, 229-240. Hershey, PA: IGI Global, 2011. https://doi.org/10.4018/978-1-60960-595-7.ch012

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Abstract

This paper proposes a neural network based traffic signal controller, which eliminates most of the problems associated with the Traffic Responsive Plan Selection (TRPS) mode of the closed loop system. Instead of storing timing plans for different traffic scenarios, which requires clustering and threshold calculations, the proposed approach uses an Artificial Neural Network (ANN) model that produces optimal plans based on optimized weights obtained through its learning phase. Clustering in a closed loop system is root of the problems and therefore has been eliminated in the proposed approach. The Particle Swarm Optimization (PSO) technique has been used both in the learning rule of ANN as well as generating training cases for ANN in terms of optimized timing plans, based on Highway Capacity Manual (HCM) delay for all traffic demands found in historical data. The ANN generates optimal plans online to address real time traffic demands and thus is more responsive to varying traffic conditions.

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