Simulation Evaluation for Traffic Signal Control Based on Expected Traffic Congestion by AVENUE
In these years, the traffic jam becomes a serious problem according to the increasing of vehicle holders in Japan. One of the key issues to ease the traffic jam is a traffic signal control, i.e., optimization of traffic signal parameters (cycle, split, and offset). The information technologies such as probe car and road-to-vehicle communication enable to progress to the next stage for the traffic signal control. In this paper, we focus on “expected traffic congestion (ETC)” which is a simple indicator for traffic jam. The value of ETC is based on the shared probe data (i.e., path information) among vehicles by road-to-vehicle communication. We apply the ETC to the optimization of traffic signal parameters. Moreover, in order to evaluate the effectiveness of the optimization, we introduce “AVENUE” which is a popular traffic stream simulator. We developed an outer module to calculate the ETC and update the traffic signal parameters for AVENUE. The experimental results using the outer module and AVENUE indicate that our traffic signal control can reduce the traveling time of vehicles.
KeywordsSignal Control Probe Data Intelligent Transportation System Current Cycle Simulation Evaluation
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- 1.Miyanishi, Y., Miyamoto, E., Maekawa, S.: A proposal of traffic signal control using its technology. IPSJ SIG Technical Reports 2001(83), 53–60 (2001)Google Scholar
- 2.Sun, X., Kusakabe, T.: A study on traffic signal control for prevention of traffic congestion. IPSJ SIG Technical Reports 2005(89), 31–34 (2005)Google Scholar
- 3.Dresner, K., Stone, P.: Multiagent traffic management: A reservation-based intersection control mechanism. In: The Third International Joint Conference on Autonomous Agents and Multiagent Systems, pp. 530–537 (2004)Google Scholar
- 5.Jyunichi, K., Yasuo, K., Souichi, T., Yoshitaka, K., Taisuke, S.: Applying genetic programming with substructure discovery to a traffic signal control problem. Transactions of the Japanese Society for Artificial Intelligence 22(2), 127–139 (2007)Google Scholar
- 6.Ando, Y., Masutani, O., Honiden, S.: Performance of pheromone model for predicting traffic congestion. In: Proceeding of Autonomus Agents & Multi Agent Systems (2006)Google Scholar
- 7.Ando, Y., Masutani, O., Honiden, S.: Pheromone model: Appplication to traffic congestion prediction. In: Proceeding of Autonomus Agents & Multi Agent Systems, pp. 1287–1298 (2005)Google Scholar
- 8.Ando, Y., Masutani, O., Sasaki, H., Motoida, S.: Pheromone model: Application to traffic congestion prediction. The Institute of Electronics, Information and Communication Engeneers j88-D-1(9), 1287–1298 (2005)Google Scholar
- 9.Kato, Y., Hasegawa, T.: Traffic signals controlled by vehicles: Paradigm shift of traffic signals. IEICE Technical Reports ITS 100(284), 67–71 (2000)Google Scholar
- 10.Aso, T., Hasegawa, T.: Full automated advanvced demand signals ii scheme. In: Proceedings of the 12th International IEEE Conference on Intelligent Transportation Systems, pp. 522–527 (2009)Google Scholar
- 11.Aso, T., Hasegawa, T.: Traffic signal control schemes for the ubiquitous sensor network period. In: Proceedings of ITS Symposium 2009, pp. 67–72 (2009)Google Scholar
- 12.Hanabusa, H., Iijima, M., Horiguchi, R.: Development of delay estimation method using real time probe data for adaptive signal control algorithm. In: Proceedings of ITS Symposium 2009, pp. 207–212 (2009)Google Scholar
- 13.Yamashita, T., Kurumatani, K., Nakashita, H.: Approach to smooth traffic flow by a cooperative car navigation system. Transactions of Information Processing Society of Japan 49(1), 177–188 (2008)Google Scholar