An iterative learning approach for network contraction: Path finding problem in stochastic time‐varying networks

Abstract

AbstractPath finding problem has a broad application in different fields of engineering. Travel time uncertainty is a critical factor affecting this problem and the route choice of transportation users. The major downside of the existing algorithms for the reliable path finding problem is their inefficiency in computational time. This study aims to develop a network contraction approach to reduce the network size of each specific origin and destination (OD) pair in stochastic time‐dependent networks. The network contraction is based on the comparison of optimistic and pessimistic solutions resulting from minimum and maximum travel time realizations of a Monte‐Carlo simulation (MCS)‐based approach. In this respect, the researchers propose a learning approach to utilize the information of the realizations in the initial iterations of the MCS approach. Implementation of this approach is in place for several OD pairs of two real‐world large‐scale applications. First, it is calibrated for the Chicago downtown network; the performance and accuracy of the proposed approach are investigated by comparing the results against that of the approach without any network contraction. In addition, the Salt Lake City network illustrates the transferability of the approach to other networks. The results demonstrate significant computational improvements, with an acceptable accuracy level relative to the approach without network contraction.