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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: © 2009 |Volume: 5 |Issue: 3 |Pages: 18
ISSN: 1548-3657|EISSN: 1548-3665|ISSN: 1548-3657|EISBN13: 9781616920371|EISSN: 1548-3665|DOI: 10.4018/jiit.2009070104
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MLA

ul-Asar, Azzam, et al. "Traffic Responsive Signal Timing Plan Generation Based on Neural Network." IJIIT vol.5, no.3 2009: pp.84-101. http://doi.org/10.4018/jiit.2009070104

APA

ul-Asar, A., Ullah, M. S., Wyne, M. F., Ahmed, J., & ul-Hasnain, R. (2009). Traffic Responsive Signal Timing Plan Generation Based on Neural Network. International Journal of Intelligent Information Technologies (IJIIT), 5(3), 84-101. http://doi.org/10.4018/jiit.2009070104

Chicago

ul-Asar, Azzam, et al. "Traffic Responsive Signal Timing Plan Generation Based on Neural Network," International Journal of Intelligent Information Technologies (IJIIT) 5, no.3: 84-101. http://doi.org/10.4018/jiit.2009070104

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Abstract

This article 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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