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Demand Response Transit Scheduling Research Based on Urban and Rural Transportation Station Optimization

Author

Listed:
  • Peiqing Li

    (School of Mechanical and Energy Engineering, Zhejiang University of Science and Technology, Hangzhou 310023, China)

  • Longlong Jiang

    (School of Mechanical and Energy Engineering, Zhejiang University of Science and Technology, Hangzhou 310023, China)

  • Shunfeng Zhang

    (School of Transportation, Southeast University, Nanjing 310058, China)

  • Xi Jiang

    (School of Transportation, Southeast University, Nanjing 310058, China)

Abstract

To reduce the operating cost and running time of demand responsive transit between urban and rural areas, a DBSCAN K-means (DK-means) clustering algorithm, which is based on the density-based spatial clustering of applications with noise (DBSCAN) and K-means clustering algorithm, was proposed to cluster pre-processing and station optimization for passenger reservation demand and to design a new variable-route demand responsive transit service system that can promote urban–rural integration. Firstly, after preprocessing the reservation demand through DBSCAN clustering algorithm, K-means clustering algorithm was used to divide fixed sites and alternative sites. Then, a bus scheduling model was established, and a genetic simulated annealing algorithm was proposed to solve the model. Finally, the feasibility of the model was validated in the northern area of Yongcheng City, Henan Province, China. The results show that the optimized bus scheduling reduced the operating cost and running time by 9.5% and 9.0%, respectively, compared with those of the regional flexible bus, and 4.5% and 5.1%, respectively, compared with those of the variable-route demand response transit after K-means clustering for passenger preprocessing.

Suggested Citation

  • Peiqing Li & Longlong Jiang & Shunfeng Zhang & Xi Jiang, 2022. "Demand Response Transit Scheduling Research Based on Urban and Rural Transportation Station Optimization," Sustainability, MDPI, vol. 14(20), pages 1-17, October.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:20:p:13328-:d:944337
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    References listed on IDEAS

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    1. Teodor Crainic & Fausto Errico & Federico Malucelli & Maddalena Nonato, 2012. "Designing the master schedule for demand-adaptive transit systems," Annals of Operations Research, Springer, vol. 194(1), pages 151-166, April.
    2. Chunqin Zhang & Yuting Hu & Anning Ni & Hongwei Li, 2019. "Compensation Scheme for Self-Employed Bus Service Requisitions in Urban–Rural Passenger Transport," Sustainability, MDPI, vol. 11(18), pages 1-20, September.
    3. Marco Diana & Luca Quadrifoglio & Cristina Pronello, 2009. "A methodology for comparing distances traveled by performance-equivalent fixed-route and demand responsive transit services," Transportation Planning and Technology, Taylor & Francis Journals, vol. 32(4), pages 377-399, June.
    4. Fu Wang & Manqing Ye & Hongbin Zhu & Dengjun Gu, 2022. "Optimization Method for Conventional Bus Stop Placement and the Bus Line Network Based on the Voronoi Diagram," Sustainability, MDPI, vol. 14(13), pages 1-20, June.
    5. Sergei Dytckov & Jan A. Persson & Fabian Lorig & Paul Davidsson, 2022. "Potential Benefits of Demand Responsive Transport in Rural Areas: A Simulation Study in Lolland, Denmark," Sustainability, MDPI, vol. 14(6), pages 1-21, March.
    6. Yu, Yao & Machemehl, Randy B. & Xie, Chi, 2015. "Demand-responsive transit circulator service network design," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 76(C), pages 160-175.
    7. 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.
    8. Liu, Xiaohan & Qu, Xiaobo & Ma, Xiaolei, 2021. "Improving flex-route transit services with modular autonomous vehicles," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 149(C).
    9. A Pratelli & F Schoen, 2001. "A mathematical programming model for the bus deviation route problem," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 52(5), pages 494-502, May.
    10. Mohsen Momenitabar & Jeremy Mattson, 2021. "A Multi-Objective Meta-Heuristic Approach to Improve the Bus Transit Network: A Case Study of Fargo-Moorhead Area," Sustainability, MDPI, vol. 13(19), pages 1-25, September.
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    Cited by:

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    3. Jun Zhao & Wenyu Rong & Di Liu, 2023. "Urban Agglomeration High-Speed Railway Backbone Network Planning: A Case Study of Beijing-Tianjin-Hebei Region, China," Sustainability, MDPI, vol. 15(8), pages 1-22, April.

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