The Development of the Adaptive Traffic Signal Control System

  • Authors:

  • Lawrence Y. Deng;Hsu Chun Liang;Chin-Thin Wang;Ching-Sheng Wang;Lun-Ping Hung

  • Affiliations:

  • Dept. of CSIE1, EE2, HEC3, St.Johnýs & Maryýýs Institute of Technology, Taipei, Taiwan;Dept. of CSIE1, EE2, HEC3, St.Johnýs & Maryýýs Institute of Technology, Taipei, Taiwan;Dept. of CSIE1, EE2, HEC3, St.Johnýs & Maryýýs Institute of Technology, Taipei, Taiwan;Dept. of Computer and Information Science, Altheia University, Taipei, Taiwan;Dept. of MIS, North Taiwan Institute of Science and Technology, Taipei, Taiwan

  • Venue:
  • ICPADS '05 Proceedings of the 11th International Conference on Parallel and Distributed Systems - Workshops - Volume 02
  • Year:
  • 2005

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Abstract

Agents refer to self-contained and identifiable computer programs that can migrate along the network and can act on behalf of the user or other agents. Agents often work on heterogeneous network and operating system environment. Therefore, an integrated logical process to access physical structure via agent application is become more and more urgent. The adaptive urban traffic signal control (TSC) system has become a development trend of intelligent transportation system (ITS). In this paper, we proposed a tracking and persistent agent-based mobility management system in case of adaptive urban traffic signal control (TSC) system. We investigated the vision based surveillance and to keep sight of the unpredictable and hardly measurable disturbances may perturb the traffic flow. We integrated and performed the vision based methodologies that include the object segmentation, classify and tracking methodologies to know well the real time measurements in urban road. According to the real time traffic measurement, we derived Agent Communication model and the Adaptive Traffic Signal Control algorithm to settle the red-green switching of traffic lights both in "go straight or turn right" and "turn left" situations. By comparing the experimental result obtained by original traffic signal control system which improves the traffic queuing situation, we confirmed the efficiency of our vision based adaptive TSC approach.