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

TL;DR

MaaSSim introduces an open-source, agent-based simulator tailored to two-sided mobility platforms, modeling travellers, drivers, and an intermediate platform within urban road networks. The framework emphasizes modular, user-definable decision modules, microscopic representation of supply and demand, and day-to-day evolution, enabling exploration of platform matching, pricing, and driver learning under varied scenarios. Through illustrative experiments, it demonstrates the emergence of equilibria, interactive effects between supply and demand, and the impact of ride-pooling, providing a versatile tool for policy analysis and research across transportation, economics, and complex systems. The work offers a practical, extensible platform with tutorials and reproducible workflows, designed to accelerate cross-disciplinary investigations into the disruption caused by ride-halling and similar mobility-on-demand services.

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.

Figures (7)

• Figure 1: Input and output of MaaSSim workflow
• Figure 2: Routines of the three kinds of agents in MaaSSim. Boxes in violet denote an interaction with the platform, orange displays matching between drivers and travellers, blue refers to joint part where a traveller is transported by the driver. Places where agents make a decision are marked with red rounded boxes and their decision protocols can be replaced by used-defined python functions.
• Figure 3: Average waiting time for travellers until the driver arrives (left) and for driver, until they get requested (right). Dark denotes longer waiting times. Results from 20 replications of a four hour period simulation with 200 travellers and 10 vehicles in Delft, the Netherlands.
• Figure 4: Service performance for various demand and supply levels. Average waiting times for travellers (left) and drivers (right), which follow mirroring diagonal trends.
• Figure 5: Searching for optimal platform competition strategy: a platform enters a market with competitor operating a fleet of 20 vehicles and offering a trip fare of 1.0 unit/km. We report average vehicle kilometers per driver (left) and total platform revenues (right) resulting from varying fleet size (x-axis) and fare (per-kilometer) combinations for 20 replications.