Traffic Signal Timing with Neural Dynamic Optimization

  • Authors:

  • Jing Xu;Wen-Sheng Yu;Jian-Qiang Yi;Zhi-Shou Tu

  • Affiliations:

  • The Key Laboratory of Complex Systems and Intelligence Science, Institute of Automation, Chinese Academy of Sciences, Beijing 100080, China;The Key Laboratory of Complex Systems and Intelligence Science, Institute of Automation, Chinese Academy of Sciences, Beijing 100080, China;The Key Laboratory of Complex Systems and Intelligence Science, Institute of Automation, Chinese Academy of Sciences, Beijing 100080, China;Experiment and Practice Center, Chongqing Technology and Business University, Chongqing 400067, China

  • Venue:
  • ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks
  • Year:
  • 2007

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Abstract

With the discrete traffic model of an oversaturated intersection, the technique of neural dynamic optimization is used to approximate the optimal signal timing strategy which can lead the minimal delay time while considering the whole congestion period. Our approach can provide an approximation of the optimal timing split in each cycle, as well as the most reasonable number of cycles for specific oversaturated traffic inflows. Specifically, for the two-phase case, we are interested to find that the optimal timing strategy is a bang-bang like control instead of a strict bang-bang one as proposed in relative literature. Moreover, our approach is evaluated with a general four-phase case, and its optimal strategy appears also to be a bang-bang like control, which may illuminate the traffic signal timing in practice.