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Simulating two-sided mobility platforms with MaaSSim

Author

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  • Rafał Kucharski
  • Oded Cats

Abstract

Two-sided mobility platforms, such as Uber and Lyft, widely emerged in the urban mobility landscape. Distributed supply of individual drivers, matched with travellers via intermediate platform yields a new class of phenomena not present in urban mobility before. Such disruptive changes to transportation systems call for a simulation framework where researchers from various and across disciplines may introduce models aimed at representing the complex dynamics of platform-driven urban mobility. In this work, we present MaaSSim, a lightweight agent-based simulator reproducing the transport system used by two kinds of agents: (i) travellers, requesting to travel from their origin to destination at a given time, and (ii) drivers supplying their travel needs by offering them rides. An intermediate agent, the platform, matches demand with supply. Agents are individual decision-makers. Specifically, travellers may decide which mode they use or reject an incoming offer; drivers may opt-out from the system or reject incoming requests. All of the above behaviours are modelled through user-defined modules, allowing to represent agents’ taste variations (heterogeneity), their previous experiences (learning) and available information (system control). MaaSSim is a flexible open-source python library capable of realistically reproducing complex interactions between agents of a two-sided mobility platform. MaaSSim is available from a public repository, along with a set of tutorials and reproducible use-case scenarios, as demonstrated with a series of illustrative examples and a comprehensive case study.

Suggested Citation

  • Rafał Kucharski & Oded Cats, 2022. "Simulating two-sided mobility platforms with MaaSSim," PLOS ONE, Public Library of Science, vol. 17(6), pages 1-20, June.
    • Handle: RePEc:plo:pone00:0269682
    DOI: 10.1371/journal.pone.0269682
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    References listed on IDEAS

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    1. Xu, Zhengtian & Yin, Yafeng & Ye, Jieping, 2020. "On the supply curve of ride-hailing systems," Transportation Research Part B: Methodological, Elsevier, vol. 132(C), pages 29-43.
    2. Boeing, Geoff, 2017. "OSMnx: New Methods for Acquiring, Constructing, Analyzing, and Visualizing Complex Street Networks," SocArXiv q86sd, Center for Open Science.
    3. repec:osf:socarx:q86sd_v1 is not listed on IDEAS
    4. Berger, Thor & Chen, Chinchih & Frey, Carl Benedikt, 2018. "Drivers of disruption? Estimating the Uber effect," European Economic Review, Elsevier, vol. 110(C), pages 197-210.
    5. Oded Cats & Rafal Kucharski & Santosh Rao Danda & Menno Yap, 2022. "Beyond the dichotomy: How ride-hailing competes with and complements public transport," PLOS ONE, Public Library of Science, vol. 17(1), pages 1-17, January.
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    1. Ashkrof, Peyman & Ghasemi, Farnoud & Kucharski, Rafał & Homem de Almeida Correia, Gonçalo & Cats, Oded & van Arem, Bart, 2025. "The implications of drivers’ ride acceptance decisions on the operations of ride-sourcing platforms," Transportation Research Part A: Policy and Practice, Elsevier, vol. 192(C).
    2. Agriesti, Serio & Roncoli, Claudio & Nahmias-Biran, Bat-hen, 2025. "A simulation-based framework for quantifying potential demand loss due to operational constraints in automated mobility services," Transportation Research Part A: Policy and Practice, Elsevier, vol. 192(C).

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