摘要
In order to ensure on-time arrival when travelersmake their trips, the stochastic network assignment modelunder uncertainty of travel time is investigated. First, basedon travelers＇ route choice behavior, the reliable travel timeconfidence level （RTTCL）, which is the probability that a triparrives within the shortest average travel time plus theacceptable travel time difference, is defined. Then, areliability-based user equilibrium （RUE） model, whichhypothesizes that for each OD pair no traveler can improvehis/her RTTCL by unilaterally changing routes, is built.Since the traditional traffic assignment algorithms are notfeasible to solve the RUE model, a quasi method of successiveaverage （QMSA） is developed. Using Nguyen-Dupuis andSioux Falls networks, the model and the algorithm are tested.The results show that the QMSA algorithm can rapidlyconverge to a high accuracy for solving the proposed RUEmodel, and the RUE model can provide a good response totravelers＇ behavior in the stochastic network.
In order to ensure on-time arrival when travelers make their trips, the stochastic network assignment model under uncertainty of travel time is investigated. First, based on travelers ' route choice behavior, the reliable travel time confidence level( RTTCL), which is the probability that a trip arrives within the shortest average travel time plus the acceptable travel time difference, is defined. Then, a reliability-based user equilibrium( RUE) model, which hypothesizes that for each OD pair no traveler can improve his/her RTTCL by unilaterally changing routes, is built.Since the traditional traffic assignment algorithms are not feasible to solve the RUE model, a quasi method of successive average( QMSA) is developed. Using Nguyen-Dupuis and Sioux Falls networks, the model and the algorithm are tested.The results showthat the QMSA algorithm can rapidly converge to a high accuracy for solving the proposed RUE model, and the RUE model can provide a good response to travelers' behavior in the stochastic network.
基金
The National Natural Science Foundation of China(No.51608115,51578150,51378119)
the Natural Science Foundation of Jiangsu Province(No.BK20150613)
the Scientific Research Foundation of Graduate School of Southeast University(No.YBJJ1679)
the Scientific Innovation Research of College Graduates in Jiangsu Province(No.KYLX15_0150)
the China Scholarship Council(CSC)Program
