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Customized Bus Network Design Based on Individual Reservation Demands

Author

Listed:
  • Zhiling Han

    (Beijing Key Laboratory of Traffic Engineering, Beijing University of Technology, Beijing 100124, China)

  • Yanyan Chen

    (Beijing Key Laboratory of Traffic Engineering, Beijing University of Technology, Beijing 100124, China)

  • Hui Li

    (School of Civil Engineering, Henan University of Technology, Zhengzhou Henan 450001, China)

  • Kuanshuang Zhang

    (Beijing Jinghang Research Institute of Computing and Communication, Beijing 100074, China)

  • Jiyang Sun

    (Beijing Key Laboratory of Traffic Engineering, Beijing University of Technology, Beijing 100124, China
    Research Institute of Highway Ministry of Transport, Beijing 100088, China)

Abstract

With the advantages of congestion alleviation, environmental friendliness, as well as a better travel experience, the customized bus (CB) system to reduce individual motorized travel is highly popular in increasing numbers of cities in China. The line planning problem is a key aspect of the CB system. This paper presents a detailed flow chart of a CB network planning methodology, including individual reservation travel demand data processing, CB line origin–destination (OD) area division considering quantity constraints of demand in areas and distance constraints based on agglomerative hierarchical clustering (AHC), an initial set of CB lines generating quantity constraints of the demand on each line and line length constraints, and line selection model building, striking a balance between operator interests, social benefits, and passengers’ interests. Finally, the impacts of the CB vehicle type, the fixed operation cost of online car-hailing (OCH), and the weights of each itemized cost are discussed. Serval operating schemes for the Beijing CB network were created. The results show that the combination of CB vehicles with 49 seats and 18 seats is the most cost-effective and that CBs with low capacity are more cost-effective than those with larger capacity. People receive the best service when decision-makers pay more attention to environmental pollution and congestion issues. The CB network’s service acceptance rate and the spatial coverage increase with the fixed operating cost per OCH vehicle per day c 0 C . The CB vehicle use decreases as c 0 C c c increases. The results of this study can provide technical support for CB operators who design CB networks.

Suggested Citation

  • Zhiling Han & Yanyan Chen & Hui Li & Kuanshuang Zhang & Jiyang Sun, 2019. "Customized Bus Network Design Based on Individual Reservation Demands," Sustainability, MDPI, vol. 11(19), pages 1-25, October.
    • Handle: RePEc:gam:jsusta:v:11:y:2019:i:19:p:5535-:d:274179
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    References listed on IDEAS

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    Cited by:

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    2. Qing Yu & Weifeng Li & Haoran Zhang & Dongyuan Yang, 2020. "Mobile Phone Data in Urban Customized Bus: A Network-based Hierarchical Location Selection Method with an Application to System Layout Design in the Urban Agglomeration," Sustainability, MDPI, vol. 12(15), pages 1-20, July.
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    4. Lee, Enoch & Cen, Xuekai & Lo, Hong K., 2021. "Zonal-based flexible bus service under elastic stochastic demand," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 152(C).
    5. Bing Zhang & Zhishan Zhong & Xun Zhou & Yongqiang Qu & Fangwei Li, 2023. "Optimization Model and Solution Algorithm for Rural Customized Bus Route Operation under Multiple Constraints," Sustainability, MDPI, vol. 15(5), pages 1-18, February.
    6. Di Huang & Weiping Tong & Lumeng Wang & Xun Yang, 2019. "An Analytical Model for the Many-to-One Demand Responsive Transit Systems," Sustainability, MDPI, vol. 12(1), pages 1-17, December.
    7. Lee, Enoch & Cen, Xuekai & Lo, Hong K., 2022. "Scheduling zonal-based flexible bus service under dynamic stochastic demand and Time-dependent travel time," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 168(C).
    8. Liu, Jiaguo & Zhao, Huida & Li, Jian & Yue, Xiaohang, 2021. "Operational strategy of customized bus considering customers’ variety seeking behavior and service level," International Journal of Production Economics, Elsevier, vol. 231(C).

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