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Comparison between ad-hoc demand responsive and conventional transit: a simulation study

Author

Listed:
  • Zahra Navidi

    (The University of Melbourne)

  • Nicole Ronald

    (Swinburne University of Technology)

  • Stephan Winter

    (The University of Melbourne)

Abstract

Considering the sprawl of cities, conventional public transport with fixed route and fixed schedule becomes less efficient and desirable every day. However, emerging technologies in computation and communication are facilitating more adaptive types of public transport systems, such as demand responsive transport that operates according to real-time demand. It is crucial to study the feasibility and advantages of these novel systems before implementation to prevent failure and financial loss. In this work, an extensive comparison of demand responsive transport and conventional public transport is provided by incorporating a dynamic routing algorithm into an agent-based traffic simulation. The results show that replacing conventional public transport with demand responsive transport will improve the mobility by decreasing the perceived travel time by passengers without any extra cost under certain circumstances. The simulation results are confirmed for different forms of networks, including a real-world network proving the potential of demand responsive transport to solve the challenge of underutilised conventional public transport in suburban areas with low transport demand.

Suggested Citation

  • Zahra Navidi & Nicole Ronald & Stephan Winter, 2018. "Comparison between ad-hoc demand responsive and conventional transit: a simulation study," Public Transport, Springer, vol. 10(1), pages 147-167, May.
    • Handle: RePEc:spr:pubtra:v:10:y:2018:i:1:d:10.1007_s12469-017-0173-z
    DOI: 10.1007/s12469-017-0173-z
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    3. Hörcher, Daniel & Tirachini, Alejandro, 2021. "A review of public transport economics," Economics of Transportation, Elsevier, vol. 25(C).
    4. Franco, Patrizia & Johnston, Ryan & McCormick, Ecaterina, 2020. "Demand responsive transport: Generation of activity patterns from mobile phone network data to support the operation of new mobility services," Transportation Research Part A: Policy and Practice, Elsevier, vol. 131(C), pages 244-266.
    5. Mittelman, Gur & Kariv, Yaron & Cohen, Yuval & Avineri, Erel, 2022. "Techno-economic analysis of energy supply to personal rapid transit (PRT) systems," Applied Energy, Elsevier, vol. 306(PB).
    6. Babak Mehran & Yongzhe Yang & Sushreeta Mishra, 2020. "Analytical models for comparing operational costs of regular bus and semi-flexible transit services," Public Transport, Springer, vol. 12(1), pages 147-169, March.
    7. Queiroz, Michell & Lucas, Flavien & Sörensen, Kenneth, 2024. "Instance generation tool for on-demand transportation problems," European Journal of Operational Research, Elsevier, vol. 317(3), pages 696-717.
    8. Hyunmyung Kim & Jaeheon Choi & Sungjin Cho & Feng Liu & Hyungmin Jin & Suhwan Lim & Dongjun Kim & Jun Lee & Chang-Hyeon Joh, 2022. "Identifying Different Sources of the Benefit: Simulation of DRT Operation in the Heartland and Hinterland Regions," Sustainability, MDPI, vol. 14(24), pages 1-20, December.
    9. Jaâfar Berrada & Alexis Poulhès, 2021. "Economic and socioeconomic assessment of replacing conventional public transit with demand responsive transit services in low-to-medium density areas," Post-Print hal-03325200, HAL.
    10. Cavallaro, Federico & Nocera, Silvio, 2023. "Flexible-route integrated passenger–freight transport in rural areas," Transportation Research Part A: Policy and Practice, Elsevier, vol. 169(C).
    11. Kersting, Moritz & Kallbach, Felina & Schlüter, Jan Christian, 2021. "For the young and old alike – An analysis of the determinants of seniors’ satisfaction with the true door-to-door DRT system EcoBus in rural Germany," Journal of Transport Geography, Elsevier, vol. 96(C).
    12. Berrada, Jaâfar & Poulhès, Alexis, 2021. "Economic and socioeconomic assessment of replacing conventional public transit with demand responsive transit services in low-to-medium density areas," Transportation Research Part A: Policy and Practice, Elsevier, vol. 150(C), pages 317-334.
    13. Alsaleh, Nael & Farooq, Bilal, 2021. "Interpretable data-driven demand modelling for on-demand transit services," Transportation Research Part A: Policy and Practice, Elsevier, vol. 154(C), pages 1-22.
    14. MELIS, Lissa & QUEIROZ, Michell & SÖRENSEN, kenneth, 2021. "The integrated on-demand bus routing problem," Working Papers 2021004, University of Antwerp, Faculty of Business and Economics.

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