Technical Papers
Nov 26, 2022

Coordinated Operation of Fixed-Route and Demand-Responsive Feeder Transit Services in a Travel Corridor

Publication: Journal of Transportation Engineering, Part A: Systems
Volume 149, Issue 2

Abstract

With the wide use of smartphone travel apps, a new hybrid transit system (HTS) that integrates fixed-route transit (FRT) and app-based demand-responsive feeder transit (DRFT) services have recently emerged in many cities over the world. App-based DRFT services are coordinated with FRT to offer fast, comfortable, and well-connected mobility services to travelers. The study investigates a new variant of HTS, termed flexible HTS (FHTS), allowing FRT to have flexible vehicle departure times and capacitated vehicle loads and DRFT to have flexible feeder stations, so as to further enhance the coordination between FRT and DRFT. The coordinated operation optimization problem of such an FHTS in a travel corridor is formulated as a mixed-integer linear programming model. A novel hybrid metaheuristic algorithm is developed to solve it. In the hybrid algorithm, a constrained compass search method is used to optimize FRT vehicle departure times, and an adaptive large neighborhood search algorithm, coupled with a tailored forward time slack algorithm, is applied to solve the DRFT routing and scheduling problems. A case study in Shucheng County, China is conducted to examine the performance of the model and solution algorithms. Sensitivity analyses on feeder station layout and vehicle size are also conducted. The results show that flexible FRT vehicle departure times and flexible DRFT feeder stations have a great potential of enhancing the coordination of FRT and DRFT services by reducing operators’ costs and passengers’ total travel time. Managerial implications are to set feeder transit stations downstream of an FRT line and to keep a reasonable station spacing.

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Data Availability Statement

The data applied to support the findings of this study are available from the corresponding author upon reasonable request.

Acknowledgments

This work was supported by the Fundamental Research Funds for the Central Universities of China (Grant No. 2022JBMC045) and the National Natural Science Foundation of China (Grant Nos. 72271018 and 61903311).

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Go to Journal of Transportation Engineering, Part A: Systems
Journal of Transportation Engineering, Part A: Systems
Volume 149Issue 2February 2023

History

Received: Apr 28, 2022
Accepted: Sep 28, 2022
Published online: Nov 26, 2022
Published in print: Feb 1, 2023
Discussion open until: Apr 26, 2023

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Dongdong He [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. Email: [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]
Associate Professor, National Engineering Laboratory of Integrated Transportation Big Data Application Technology, School of Transportation and Logistics, Southwest Jiaotong Univ., Chengdu 611756, China; Associate Professor, Institute of System Science and Engineering, School of Transportation and Logistics, Southwest Jiaotong Univ., Chengdu 611756, China. ORCID: https://orcid.org/0000-0001-6101-1924. Email: [email protected]
Associate Professor, Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport, Ministry of Transport, Beijing Jiaotong Univ., Beijing 100044, China (corresponding author). ORCID: https://orcid.org/0000-0002-4752-9994. Email: [email protected]

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