MaaSSim -- agent-based two-sided mobility platform simulator

TL;DR

MaaSSim is an open-source agent-based simulator for two-sided urban mobility platforms like Uber, modeling traveler and driver behaviors, platform interactions, and system dynamics for research and development.

Contribution

The paper introduces MaaSSim, a flexible, open-source simulation framework for modeling two-sided mobility platforms with customizable agent behaviors and decision-making processes.

Findings

Supports modeling of heterogeneous agent behaviors

Enables simulation of platform demand-supply matching dynamics

Provides reproducible use-case scenarios

Abstract

Two-sided mobility platforms, such as Uber and Lyft, widely emerged in the urban mobility landscape, bringing disruptive changes to transportation systems worldwide. This calls for a simulation framework where researchers from various and across disciplines may introduce models aimed at representing the dynamics of platform-driven urban mobility systems. In this work, we present MaaSSim, an agent-based simulator reproducing the transport system used by two kind 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, allows demand to be matched with supply. Agents are decision makers, specifically, travellers may decide which mode they use or reject an incoming offer. Similarly, drivers may opt-out from the system or reject incoming requests. All of the above behaviours are modelled through user-defined modules, representing agents' taste variations (heterogeneity), their previous experiences (learning) and available information (system control). MaaSSim is an open-source library available at a public repository github.com/RafalKucharskiPK/MaaSSim, along with a set of tutorials and reproducible use-case scenarios.