Abstract
Passenger behavior analysis is a key issue in passenger assignment research, in which the path choice is a fundamental component. A highly complex transit network offers multiple paths for each origin–destination (OD) pair and thus resulting in more flexible choices for each passenger. To reflect a passenger’s flexible choice for the transit network, the optimal strategy was proposed by other researchers to determine passenger choice behavior. However, only strategy links have been searched in the optimal strategy algorithm and these links cannot complete the whole path. To determine the paths for each OD pair, this study proposes the depth-first path generation algorithm, in which a strategy node concept is newly defined. The proposed algorithm was applied to the Beijing metro network. The results show that, in comparison to the shortest path and the K-shortest path analysis, the proposed depth-first optimal strategy path generation algorithm better represents the passenger behavior more reliably and flexibly.
Highlights
Passenger behavior analysis is a fundamental issue for urban rail transit research, where the path choice or route choice is the dominant aspect
The complex skip–stop and through service operation plan are seldom applied in Beijing metro, which leads to the results that most of the OD pairs only contain one path
This study focused on the passenger’s path choice behavior in a complex transit network
Summary
Passenger behavior analysis is a fundamental issue for urban rail transit research, where the path choice or route choice is the dominant aspect. The ring route path searching problem for the Dial algorithm was solved Note that these algorithms are designed based on the link travel time. Some new searching algorithms such as the Fibonacci heap method from computer science [26] and space-time prisms [27] are proposed to find the K-shortest path in SBN. It is assumed that in the K-shortest path assignment, the passengers would not change their paths until they reach their destinations This is not suitable to describe the passenger’s flexible path choice behavior. Based on the strategy node, a depth-first optimal strategy path generation algorithm is proposed, which was applied to the Beijing metro network to identify passenger behavior. The discussion, conclusions and directions for our future work are presented in the last two section
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