The Depth-First Optimal Strategy Path Generation Algorithm for Passengers in a Metro Network

Kai Lu, Tao Tang and Chunhai Gao
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Kai Lu: School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing 100404, China
Tao Tang: School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing 100404, China
Chunhai Gao: Traffic Control Technology Co., Ltd., Beijing 100070, China

Sustainability, 2020, vol. 12, issue 13, 1-16

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.

Keywords: passenger behavior; optimal strategy; strategy node; depth-first optimal strategy path generation algorithm (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2020
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