FleetPy: A Modular Open-Source Simulation Tool for Mobility On-Demand Services

Abstract

The market share of mobility on-demand (MoD) services strongly increased in recent years and is expected to rise even higher once vehicle automation is fully available. These services might reduce space consumption in cities as fewer parking spaces are required if private vehicle trips are replaced. If rides are shared additionally, occupancy related traffic efficiency is increased. Simulations help to identify the actual impact of MoD on a traffic system, evaluate new control algorithms for improved service efficiency and develop guidelines for regulatory measures. This paper presents the opensource agent-based simulation framework FleetPy. FleetPy (written in the programming language "Python") is explicitly developed to model MoD services in a high level of detail. It specially focuses on the modeling of interactions of users with operators while its flexibility allows the integration and embedding of multiple operators in the overall transportation system. Its modular structure ensures the transferabillity of previously developed elements and the selection of an appropriate level of modeling detail. This paper compares existing simulation frameworks for MoD services and highlights exclusive features of FleetPy. The upper level simulation flows are presented, followed by required input data for the simulation and the output data FleetPy produces. Additionally, the modules within FleetPy and high-level descriptions of current implementations are provided. Finally, an example showcase for Manhattan, NYC provides insights into the impacts of different modules for simulation flow, fleet optimization, traveler behavior and network representation.

RidePy: A fast and modular framework for simulating ridepooling systems

“…FleetPy (Engelhardt et al, 2022), a recently released on-demand mobility simulation, is primarily aimed at realistic modeling of the interactions between operators and users, specifically incorporating multiple operators. While its technical approach is similar to ours, integrating Python with fast Cython and C++ extensions, the project is predominantly focused on applied simulations, although its framework architecture promises to allow for adjustment of the model detail level.…”