TY - JOUR

T1 - Robust controls for traffic networks

T2 - The near-Bayes near-Minimax strategy

AU - Jones, Lee K.

AU - Deshpande, Rahul

AU - Gartner, Nathan H.

AU - Stamatiadis, Chronis

AU - Zou, Fei

N1 - Funding Information:
This research was supported (in part) by Award UM-GAID #3973 from the University Transportation Center at University of Massachusetts, Amherst. The authors would also like to thank the constructive comments of the three anonymous reviewers. Any opinions, findings and conclusions are the responsibility of the authors alone.

PY - 2013/2

Y1 - 2013/2

N2 - Traffic signals allocate scarce capacity at roadway junctions and, as such, influence the level of service both locally and in the corresponding traffic network. This paper addresses the problem of determining robust signal controls in a traffic network which (a) consider the interdependency of signal controls and traffic flow patterns and (b) account for the variability or uncertainty in the origin-destination demands. The approach taken is to model the uncertainty in terms of constraints on the possible origin-destination (OD) demands concurrently with the signal controls to produce a " best" strategy that accounts for this uncertainty. To develop this strategy solutions for two extreme cases are considered. One is the Bayes case in which we assume a probability density on the possible OD matrices. The second is the Minimax solution which seeks to minimize the worst possible costs that may occur. The strategy employed is a compromise between the Bayes and the Minimax solutions and is termed the near-Bayes near-Minimax (NBNM) strategy. It is designed to provide performance that is close to the best that can be obtained under Bayes conditions, yet does not depart too far from the most beneficial controls under the most costly origin-destination demands. As such, this is a conservative approach whose controls can provide robust or risk-averse performance. Application of the methodology is illustrated in three case studies.

AB - Traffic signals allocate scarce capacity at roadway junctions and, as such, influence the level of service both locally and in the corresponding traffic network. This paper addresses the problem of determining robust signal controls in a traffic network which (a) consider the interdependency of signal controls and traffic flow patterns and (b) account for the variability or uncertainty in the origin-destination demands. The approach taken is to model the uncertainty in terms of constraints on the possible origin-destination (OD) demands concurrently with the signal controls to produce a " best" strategy that accounts for this uncertainty. To develop this strategy solutions for two extreme cases are considered. One is the Bayes case in which we assume a probability density on the possible OD matrices. The second is the Minimax solution which seeks to minimize the worst possible costs that may occur. The strategy employed is a compromise between the Bayes and the Minimax solutions and is termed the near-Bayes near-Minimax (NBNM) strategy. It is designed to provide performance that is close to the best that can be obtained under Bayes conditions, yet does not depart too far from the most beneficial controls under the most costly origin-destination demands. As such, this is a conservative approach whose controls can provide robust or risk-averse performance. Application of the methodology is illustrated in three case studies.

KW - Equilibrium-constrained optimization

KW - Origin-destination trip tables

KW - Robust optimization

KW - System optimization

KW - Traffic signals

KW - User equilibrium

UR - http://www.scopus.com/inward/record.url?scp=84873716086&partnerID=8YFLogxK

U2 - 10.1016/j.trc.2012.06.009

DO - 10.1016/j.trc.2012.06.009

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AN - SCOPUS:84873716086

SN - 0968-090X

VL - 27

SP - 205

EP - 218

JO - Transportation Research Part C: Emerging Technologies

JF - Transportation Research Part C: Emerging Technologies

ER -