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A modeling approach for matching ridesharing trips within macroscopic travel demand models

Author

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  • Markus Friedrich

    (University of Stuttgart)

  • Maximilian Hartl

    (University of Stuttgart)

  • Christoph Magg

    (University of Stuttgart)

Abstract

State of the art travel demand models for urban areas typically distinguish four or five main modes: walking, cycling, public transport and car. The mode car can be further split into car-driver and car-passenger. As the importance of ridesharing may increase in the coming years, ridesharing should be addressed as an additional sub or main mode in travel demand modeling. This requires an algorithm for matching the trips of suppliers (typically car drivers) and demanders (travelers of non-car modes). The paper presents a matching algorithm, which can be integrated in existing travel demand models. The algorithm works likewise with integer demand, which is typical for agent-based microscopic models, and with non-integer demand occurring in travel demand matrices of a macroscopic model. The algorithm compares two path sets of suppliers and demanders. The representation of a path in the road network is reduced from a sequence of links to a sequence of zones. The zones act as a buffer along the path, where demanders can be picked up. The travel demand model of the Stuttgart Region serves as an application example. The study estimates that the entire travel demand of all motorized modes in the Stuttgart Region could be transported by 7% of the current car fleet with 65% of the current vehicle distance traveled, if all travelers were willing to either use ridesharing vehicles with 6 seats or traditional rail.

Suggested Citation

  • Markus Friedrich & Maximilian Hartl & Christoph Magg, 2018. "A modeling approach for matching ridesharing trips within macroscopic travel demand models," Transportation, Springer, vol. 45(6), pages 1639-1653, November.
    • Handle: RePEc:kap:transp:v:45:y:2018:i:6:d:10.1007_s11116-018-9957-5
    DOI: 10.1007/s11116-018-9957-5
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    References listed on IDEAS

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    Cited by:

    1. André Palma & Lucas Javaudin & Patrick Stokkink & Léandre Tarpin-Pitre, 2024. "Ride-sharing with inflexible drivers in the Paris metropolitan area," Transportation, Springer, vol. 51(3), pages 963-986, June.
    2. Nadine Kostorz & Eva Fraedrich & Martin Kagerbauer, 2021. "Usage and User Characteristics—Insights from MOIA, Europe’s Largest Ridepooling Service," Sustainability, MDPI, vol. 13(2), pages 1-18, January.
    3. Johann Hartleb & Markus Friedrich & Emely Richter, 2022. "Vehicle scheduling for on-demand vehicle fleets in macroscopic travel demand models," Transportation, Springer, vol. 49(4), pages 1133-1155, August.
    4. Wang, Jiangbo & Yamamoto, Toshiyuki & Liu, Kai, 2021. "Spatial dependence and spillover effects in customized bus demand: Empirical evidence using spatial dynamic panel models," Transport Policy, Elsevier, vol. 105(C), pages 166-180.
    5. André de Palma & Lucas Javaudin & Patrick Stokkink & Léandre Tarpin-Pitre, 2021. "Modelling Ridesharing in a Large Network with Dynamic Congestion," THEMA Working Papers 2021-16, THEMA (THéorie Economique, Modélisation et Applications), Université de Cergy-Pontoise.
    6. Kucharski, Rafał & Cats, Oded, 2020. "Exact matching of attractive shared rides (ExMAS) for system-wide strategic evaluations," Transportation Research Part B: Methodological, Elsevier, vol. 139(C), pages 285-310.
    7. Mark Muller & Seri Park & Ross Lee & Brett Fusco & Gonçalo Homem de Almeida Correia, 2021. "Review of Whole System Simulation Methodologies for Assessing Mobility as a Service (MaaS) as an Enabler for Sustainable Urban Mobility," Sustainability, MDPI, vol. 13(10), pages 1-15, May.
    8. Rajendran, Suchithra & Srinivas, Sharan, 2020. "Air taxi service for urban mobility: A critical review of recent developments, future challenges, and opportunities," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 143(C).

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