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

Cheng You Cui; Ji Sun Shin; Michio Miyazaki; Hee Hyol Lee

doi:10.1541/ieejeiss.132.21

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

Cheng You Cui^*, Ji Sun Shin, Michio Miyazaki, Hee Hyol Lee

^*Corresponding author for this work

Graduate School of Information, Production and Systems

Research output: Contribution to journal › Article › peer-review

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 language	English
Pages (from-to)	21-31+2
Journal	IEEJ Transactions on Electronics, Information and Systems
Volume	132
Issue number	1
DOIs	https://doi.org/10.1541/ieejeiss.132.21
Publication status	Published - 2012

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

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10.1541/ieejeiss.132.21

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title = "Real-time traffic signal control for optimization of traffic jam probability",

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.",

keywords = "Bayesian Network, Cellular Automaton traffic model, Particle Swarm Optimization, Predicted Probabilistic Distribution, Traffic jam, Traffic signal control",

author = "Cui, {Cheng You} and Shin, {Ji Sun} and Michio Miyazaki and Lee, {Hee Hyol}",

year = "2012",

doi = "10.1541/ieejeiss.132.21",

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T1 - Real-time traffic signal control for optimization of traffic jam probability

AU - Cui, Cheng You

AU - Shin, Ji Sun

AU - Miyazaki, Michio

AU - Lee, Hee Hyol

PY - 2012

Y1 - 2012

N2 - 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.

AB - 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.

KW - Bayesian Network

KW - Cellular Automaton traffic model

KW - Particle Swarm Optimization

KW - Predicted Probabilistic Distribution

KW - Traffic jam

KW - Traffic signal control

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

Abstract

Keywords

ASJC Scopus subject areas

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