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 - 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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U2 - 10.1541/ieejeiss.132.21
DO - 10.1541/ieejeiss.132.21
M3 - Article
AN - SCOPUS:84863033964
SN - 0385-4221
VL - 132
SP - 21-31+2
JO - IEEJ Transactions on Electronics, Information and Systems
JF - IEEJ Transactions on Electronics, Information and Systems
IS - 1
ER -