Traffic signal control based on predicted distribution of traffic jam

Cui Cheng-You, Shin Ji-Sun, Shoji Fumihiro, Lee Hee-Hyol

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In this paper, we propose a new method of a traffic signal control based on the predicted distribution of the traffic jam. First, we built a forecasting model to predict the probability distribution of vehicles for traffic jam during the each period of traffic signal. As the forecasting model, the Dynamic Bayesian Network is used and predicted the probability distribution of the amount of the standing vehicle in traffic jam. According to calculation by the Dynamic Bayesian network, the prediction of probability distribution of the amount of standing vehicles in each time will be obtained, and a control rule to adjust the split and the cycle of the signal to maintain the probability of a lower limit and a ceiling of the standing vehicles is deduced. Through the simulation using the actual traffic data of a city, the effectiveness of our method is shown.

Original languageEnglish
Title of host publicationProceedings of the 14th International Symposium on Artificial Life and Robotics, AROB 14th'09
Pages55-58
Number of pages4
Publication statusPublished - 2009 Dec 1
Event14th International Symposium on Artificial Life and Robotics, AROB 14th'09 - Oita, Japan
Duration: 2008 Feb 52009 Feb 7

Publication series

NameProceedings of the 14th International Symposium on Artificial Life and Robotics, AROB 14th'09

Conference

Conference14th International Symposium on Artificial Life and Robotics, AROB 14th'09
CountryJapan
CityOita
Period08/2/509/2/7

Keywords

  • Dynamic Bayesian network
  • Forecasting model
  • Probabilistic distribution
  • Traffic jam
  • Traffic signal control

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Vision and Pattern Recognition
  • Human-Computer Interaction

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