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Designing the master schedule for demand-adaptive transit systems

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Listed:
  • Teodor Crainic
  • Fausto Errico
  • Federico Malucelli
  • Maddalena Nonato

Abstract

Demand-Adaptive Systems (DASs) display features of both traditional fixed-line bus services and purely on-demand systems such as dial-a-ride, that is, they offer demand-responsive services within the framework of traditional scheduled bus transportation. A DAS bus line serves a given set of compulsory stops according to a predefined schedule, which specifies the time windows associated with each stop, and thus provides the traditional use of a transit line without reservation. On the other hand, passengers may issue requests for transportation between two optional stops, inducing detours in the vehicle routes. The design of a DAS line is a complex planning process that requires to select the compulsory stops and to determine its master schedule in terms of the time windows associated with the compulsory stops. In this paper, we focus on determining the master schedule for a single DAS line. We propose a mathematical description and a solution method based on probabilistic approximations of several DAS line core characteristics. Results of numerical experiments are also given and analyzed. Copyright Springer Science+Business Media, LLC 2012

Suggested Citation

  • 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.
    • Handle: RePEc:spr:annopr:v:194:y:2012:i:1:p:151-166:10.1007/s10479-010-0710-5
    DOI: 10.1007/s10479-010-0710-5
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    References listed on IDEAS

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    1. Irina Ioachim & Jacques Desrosiers & Yvan Dumas & Marius M. Solomon & Daniel Villeneuve, 1995. "A Request Clustering Algorithm for Door-to-Door Handicapped Transportation," Transportation Science, INFORMS, vol. 29(1), pages 63-78, February.
    2. Teodor Gabriel Crainic & Federico Malucelli & Maddalena Nonato & François Guertin, 2005. "Meta-Heuristics for a Class of Demand-Responsive Transit Systems," INFORMS Journal on Computing, INFORMS, vol. 17(1), pages 10-24, February.
    3. Horn, M. E. T., 2002. "Multi-modal and demand-responsive passenger transport systems: a modelling framework with embedded control systems," Transportation Research Part A: Policy and Practice, Elsevier, vol. 36(2), pages 167-188, February.
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    Cited by:

    1. Bruni, M.E. & Guerriero, F. & Beraldi, P., 2014. "Designing robust routes for demand-responsive transport systems," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 70(C), pages 1-16.
    2. 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.
    3. Xuekai Cen & Kanghui Ren & Yiying Cai & Qun Chen, 2023. "Designing Flexible-Bus System with Ad-Hoc Service Using Travel-Demand Clustering," Mathematics, MDPI, vol. 11(4), pages 1-27, February.
    4. GALARZA MONTENEGRO, Bryan David & SÖRENSEN, Kenneth & VANSTEENWEGEN, Pieter, 2023. "A demand-responsive feeder service with a maximum headway at mandatory stops," Working Papers 2023001, University of Antwerp, Faculty of Business and Economics.
    5. Kuo, Yong-Hong & Leung, Janny M.Y. & Yan, Yimo, 2023. "Public transport for smart cities: Recent innovations and future challenges," European Journal of Operational Research, Elsevier, vol. 306(3), pages 1001-1026.

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