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

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arxiv 2207.14246 v1 pith:3D4CTP74 submitted 2022-07-28 cs.MA cs.SYeess.SY

classification cs.MAcs.SYeess.SY
keywords fleetpysimulationservicesleveladditionallydatadetaildeveloped
verification ladder T0 review T1 audit T2 compute T3 formal
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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 open-source 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.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. RideGym: A Standardized Interface for Real-World Large-Scale Ride-Sharing System

    cs.MA 2026-07 accept novelty 5.5 of 10

    RideGym provides the first open, algorithm-agnostic Gym interface for large-scale ride-sharing order dispatch and shows exploration noise can reverse MARL performance rankings.

  2. Semi-on-Demand Transit Feeders with Shared Autonomous Vehicles and Reinforcement-Learning-Based Zonal Dispatching Control

    cs.LG 2025-09 conditional novelty 5.0 of 10

    A reinforcement-learning dispatch controller for semi-on-demand transit feeders serves 16% more passengers than a fixed route in simulation, with the RL layer adding a small 2.4% gain over a nominal zonal rule.

  3. HRSim: An agent-based simulation platform for high-capacity ride-sharing services

    eess.SY 2025-05 conditional novelty 5.0 of 10

    The authors present HRSim, an open-source agent-based platform for simulating high-capacity ride-sharing operations at city scale, with modules for pricing, routing, matching, repositioning, and visualization.

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