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

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

doi:10.1002/ecj.11436

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

9 Citations (Scopus)

Abstract

Real-time traffic signal control is an integral part of an urban traffic control system. It can control traffic signals online according to variations of traffic flow. In this paper we propose a new method for a 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 particle swarm optimization to calculate the optimal traffic signals. A simulation based on real traffic data was carried out to show the effectiveness of the proposed CAPSOBN real-time traffic signal control system using a micro traffic simulator.

Original language	English
Pages (from-to)	1-13
Number of pages	13
Journal	Electronics and Communications in Japan
Volume	96
Issue number	1
DOIs	https://doi.org/10.1002/ecj.11436
Publication status	Published - 2013 Jan 1

Keywords

Bayesian network
cellular automaton traffic model
particle swarm optimization
predicted probabilistic distribution
traffic jam
traffic signal control

ASJC Scopus subject areas

Signal Processing
Physics and Astronomy(all)
Computer Networks and Communications
Electrical and Electronic Engineering
Applied Mathematics

Access to Document

10.1002/ecj.11436

Cite this

@article{c2bfbf1b1452484892d2b9a0c7c4b467,

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

abstract = "Real-time traffic signal control is an integral part of an urban traffic control system. It can control traffic signals online according to variations of traffic flow. In this paper we propose a new method for a 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 particle swarm optimization to calculate the optimal traffic signals. A simulation based on real traffic data was carried out to show the effectiveness of the proposed CAPSOBN real-time traffic signal control system 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 = "2013",

month = jan,

day = "1",

doi = "10.1002/ecj.11436",

language = "English",

volume = "96",

pages = "1--13",

journal = "Electronics and Communications in Japan",

issn = "1942-9533",

publisher = "Scripta Technica",

number = "1",

}

TY - JOUR

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 - 2013/1/1

Y1 - 2013/1/1

N2 - Real-time traffic signal control is an integral part of an urban traffic control system. It can control traffic signals online according to variations of traffic flow. In this paper we propose a new method for a 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 particle swarm optimization to calculate the optimal traffic signals. A simulation based on real traffic data was carried out to show the effectiveness of the proposed CAPSOBN real-time traffic signal control system using a micro traffic simulator.

AB - Real-time traffic signal control is an integral part of an urban traffic control system. It can control traffic signals online according to variations of traffic flow. In this paper we propose a new method for a 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 particle swarm optimization to calculate the optimal traffic signals. A simulation based on real traffic data was carried out to show the effectiveness of the proposed CAPSOBN real-time traffic signal control system 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

UR - http://www.scopus.com/inward/record.url?scp=84872913087&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84872913087&partnerID=8YFLogxK

U2 - 10.1002/ecj.11436

DO - 10.1002/ecj.11436

M3 - Article

AN - SCOPUS:84872913087

SN - 1942-9533

VL - 96

SP - 1

EP - 13

JO - Electronics and Communications in Japan

JF - Electronics and Communications in Japan

IS - 1

ER -

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

Abstract

Keywords

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this