Unveiling the Influential Factors for Customized Bus Service Reopening from Naturalistic Observations in Shanghai

This work attempts to understand how a customized bus (CB) operator decides to open or close a CB line. The authors look into the changes in the operation status of CB lines (i.e. reopen and closure) from one of the largest CB operators in Shanghai, China, with a 22-month consecutive observation ranging from January 2019 to October 2020. As all CB services were totally suspended at the beginning of 2020 due to the COVID-19 travel restriction and then gradually recovered in March 2020, the authors utilize this study period as a naturalistic observation experiment to investigate the changes in the operation status of each CB line before and after the travel restriction. Using the operation status at each month as the binary alternatives, the mixed logit models and the tree-based models with explainable machine learning techniques are respectively adopted to explore the factors that influence the decision-making process. The findings from both types of models are in general consistent. The results show that the characteristics of each CB line including the ridership, the length of the line, the closeness to charging stations, and the overlap of CB lines significantly impact the decisions. In addition, the land-use types around the CB stops and the market competition from alternative travel modes also play a key role in making the decisions.

• Record URL:
  • https://doi.org/10.1016/j.ijtst.2023.12.002
• Record URL:
  • http://www.sciencedirect.com/science/article/pii/S2046043023001077
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/20460430
• Supplemental Notes:
  • © 2023 Tongji University and Tongji University Press. Publishing Services by Elsevier B.V. Abstract reprinted with permission of Elsevier.
• Authors:
  • Shen, Yu
  • Xu, Chenlong
  • Jiang, Shengchuan
  • Zhai, Zhikang
  • Ji, Yuxiong
  • Du, Yuchuan
• Publication Date: 2024-3

Language

• English

Media Info

• Media Type: Web
• Features: Figures; Maps; References; Tables;
• Pagination: pp 106-121
• Serial:
  • International Journal of Transportation Science and Technology
  • Volume: 13
  • Issue Number: 0
  • Publisher: Elsevier
  • ISSN: 2046-0430
  • Serial URL: http://www.sciencedirect.com/science/journal/20460430

Subject/Index Terms

• TRT Terms: Automatic data collection systems; Bus lines; Choice models; Decision making; Machine learning
• Geographic Terms: Shanghai (China)
• Subject Areas: Data and Information Technology; Operations and Traffic Management; Planning and Forecasting; Public Transportation;

Filing Info

• Accession Number: 01905086
• Record Type: Publication
• Files: TRIS
• Created Date: Jan 19 2024 4:35PM