Technical Papers
Aug 10, 2021

Optimal Bus Bridging Schedule with Transfer Passenger Demand during Disruptions of Urban Rail Transit

Publication: Journal of Transportation Engineering, Part A: Systems
Volume 147, Issue 10

Abstract

To address the transfer connection problem for urban rail transit (URT) and bus bridging in the case of unscheduled operation disruption, an integrated optimization model for bus bridging timetables and vehicle scheduling is proposed that considers the surge characteristics of transfer passenger flows. First, we analyze the transfer connections between the URT system and the bus bridging system and introduce the concept of passenger tolerance to determine whether a connection is maintained. A bilevel programming model is formulated, in which the upper level addresses timetable optimization with the aim of minimizing the passenger waiting time and the number of transfer failures, and the lower level addresses vehicle scheduling with the aim of minimizing bus operation cost. To solve the proposed model, an improved simulated annealing (SA) algorithm is developed. Finally, a case study of the Shanghai Rail Transit Line 10 is analyzed. The results show that, compared with the even headway timetable, the proposed model results in a 13.7% reduction in total cost, a 14.1% reduction in passenger waiting time, and a 54.9% reduction in the number of failed transfers.

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Data Availability Statement

All data, models, and code generated or used during the study appear in the published article.

Acknowledgments

This work is financially supported by the National Key Basic Research Program of China (2012CB725403).

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Go to Journal of Transportation Engineering, Part A: Systems
Journal of Transportation Engineering, Part A: Systems
Volume 147Issue 10October 2021

History

Received: Jun 10, 2020
Accepted: Apr 16, 2021
Published online: Aug 10, 2021
Published in print: Oct 1, 2021
Discussion open until: Jan 10, 2022

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Authors

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Jiadong Wang [email protected]
Ph.D. Candidate, Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport, Ministry of Transport, Beijing Jiaotong Univ., Beijing 100044, China (corresponding author). Email: [email protected]
Zhenzhou Yuan [email protected]
Professor, Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport, Ministry of Transport, Beijing Jiaotong Univ., Beijing 100044, China. Email: [email protected]
Zhichao Cao [email protected]
Associate Professor, School of Transportation and Civil Engineering, Nantong Univ., Nantong 226019, China. Email: [email protected]
Postgraduate, School of Transportation and Civil Engineering, Nantong Univ., Nantong 226019, China. Email: [email protected]

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