TY - GEN
T1 - A neural network-based stochastic approximation approach to special events traffic signal timing control
AU - Yang, Jiann Shiou
PY - 2005/12/1
Y1 - 2005/12/1
N2 - A sudden traffic surge immediately after special events (e.g., conventions, hockey games, concerts, etc.) can create substantial traffic congestion in the area where the events are held. It is desired to implement a short-term traffic signal timing adjustment for the high volume traffic movements associated with special events so that progression is as efficient as possible. This paper presents a case study of special events traffic signal timing control for the City of Duluth Entertainment and Convention Center (DECC). Our optimization approach is based on neural networks (NNs) with the weight estimation via the Simultaneous Perturbation Stochastic Approximation (SPSA) algorithm. Using the traffic data collected, the NN-based SPSA optimization method is applied to make signal timing adjustments. A tolerance index is chosen as our measure-of-effectiveness (MOE). The NN weights are determined by use of the SPSA parallel estimation algorithm that minimizes the MOE criterion at the selected intersections following DECC events. The performance evaluations, based on different MOEs, using the existing signal timing and the one generated by the SPSA algorithm are investigated. The results show the potential of the proposed optimization method.
AB - A sudden traffic surge immediately after special events (e.g., conventions, hockey games, concerts, etc.) can create substantial traffic congestion in the area where the events are held. It is desired to implement a short-term traffic signal timing adjustment for the high volume traffic movements associated with special events so that progression is as efficient as possible. This paper presents a case study of special events traffic signal timing control for the City of Duluth Entertainment and Convention Center (DECC). Our optimization approach is based on neural networks (NNs) with the weight estimation via the Simultaneous Perturbation Stochastic Approximation (SPSA) algorithm. Using the traffic data collected, the NN-based SPSA optimization method is applied to make signal timing adjustments. A tolerance index is chosen as our measure-of-effectiveness (MOE). The NN weights are determined by use of the SPSA parallel estimation algorithm that minimizes the MOE criterion at the selected intersections following DECC events. The performance evaluations, based on different MOEs, using the existing signal timing and the one generated by the SPSA algorithm are investigated. The results show the potential of the proposed optimization method.
KW - Neural networks
KW - Optimization
KW - SPSA algorithm
KW - Traffic signal timing
UR - http://www.scopus.com/inward/record.url?scp=84867388487&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84867388487&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:84867388487
SN - 980656054X
SN - 9789806560543
T3 - WMSCI 2005 - The 9th World Multi-Conference on Systemics, Cybernetics and Informatics, Proceedings
SP - 199
EP - 204
BT - WMSCI 2005 - The 9th World Multi-Conference on Systemics, Cybernetics and Informatics, Proceedings
T2 - 9th World Multi-Conference on Systemics, Cybernetics and Informatics, WMSCI 2005
Y2 - 10 July 2005 through 13 July 2005
ER -