Real-time traffic signal control for optimization of traffic jam probability

Cheng You Cui, Ji Sun Shin, Michio Miyazaki, HeeHyol Lee

Research output: Contribution to journalArticle

3 Citations (Scopus)

Abstract

Real-time traffic signal control is an integral part of urban traffic control system. It can control traffic signals online according to variation of traffic flow. In this paper, we propose a new method for the real-time traffic signal control system. The system uses a Cellular Automaton model and a Bayesian Network model to predict probabilistic distributions of standing vehicles, and uses a Particle Swarm Optimization method to calculate optimal traffic signals. A simulation based on real traffic data was carried out to show the effectiveness of the proposed real-time traffic signal control system CAPSOBN using a micro traffic simulator.

Original languageEnglish
JournalIEEJ Transactions on Electronics, Information and Systems
Volume132
Issue number1
DOIs
Publication statusPublished - 2012

Fingerprint

Traffic signals
Control systems
Traffic control
Cellular automata
Bayesian networks
Particle swarm optimization (PSO)
Simulators

Keywords

  • Bayesian Network
  • Cellular Automaton traffic model
  • Particle Swarm Optimization
  • Predicted Probabilistic Distribution
  • Traffic jam
  • Traffic signal control

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

Cite this

Real-time traffic signal control for optimization of traffic jam probability. / Cui, Cheng You; Shin, Ji Sun; Miyazaki, Michio; Lee, HeeHyol.

In: IEEJ Transactions on Electronics, Information and Systems, Vol. 132, No. 1, 2012.

Research output: Contribution to journalArticle

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