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Simulating two-sided mobility platforms with MaaSSim.

Rafał Kucharski1,2, Oded Cats1

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Researchers developed MaaSSim, an agent-based simulator for two-sided mobility platforms like Uber. This open-source tool models traveller and driver behavior for urban mobility research.

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Area of Science:

  • Urban planning and transportation science
  • Computational social science
  • Agent-based modeling

Background:

  • Two-sided mobility platforms (e.g., Uber, Lyft) have significantly altered urban transportation.
  • These platforms introduce complex dynamics not previously seen in urban mobility.
  • A dedicated simulation framework is needed to study these disruptive changes.

Purpose of the Study:

  • To present MaaSSim, a lightweight, agent-based simulation framework.
  • To enable researchers to model complex interactions in platform-driven urban mobility.
  • To provide a flexible, open-source tool for diverse research needs.

Main Methods:

  • Developed MaaSSim, an agent-based simulator in Python.
  • Modeled two key agent types: travellers and drivers.
  • Incorporated an intermediate platform agent for matching supply and demand.
  • Enabled user-defined modules for agent behavior (heterogeneity, learning, system control).

Main Results:

  • MaaSSim realistically reproduces complex interactions within two-sided mobility platforms.
  • The simulator allows for detailed modeling of individual agent decision-making.
  • Demonstrated functionality through illustrative examples and a comprehensive case study.

Conclusions:

  • MaaSSim offers a flexible and open-source solution for studying platform-driven urban mobility.
  • The framework supports research into agent behavior, system dynamics, and policy implications.
  • Provides a valuable tool for interdisciplinary researchers in urban mobility.