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<p>Mobility-on-demand (MoD) services have been extensively investigated across multiple research disciplines. For example, transport planners are interested in the impact on mode choice and traffic, while researchers in operations research focus on operational sub-problems such as assignment, pooling, and routing. These differing perspectives lead to varying setups for simulations and dynamic vehicle routing problems. Many developed approaches and algorithms are not open-source, and results are tied to specific problem settings, making fair comparisons difficult. To address this, we developed FleetPy, an agent-based simulator that provides a consistent and modular platform for studying and comparing MoD services. Its modular structure enables exploration of diverse aspects at varying levels of detail. These modules cover a wide range of fleet control tasks—including implementations of vehicle assignment, repositioning, pricing, and charging methods—alongside customer behavior models, network and routing components, and agent interactions. Well-designed interfaces facilitate coupling with established transport simulation frameworks and have been successfully tested in real-world experiments. FleetPy advances reproducible research in three ways: (1) enabling comparison of new algorithms with existing methods under consistent scenarios, (2) providing benchmark input data for Manhattan, Munich, and Chicago, and (3) supporting transport planners in developing MoD case studies with reduced technical effort.</p>

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FleetPy: an open source simulator for reproducible research on mobility-on-demand services

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年7月15日

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  • Roman Engelhardt,
  • Florian Dandl,
  • Arslan Ali Syed,
  • Chenhao Ding,
  • Santiago Alvarez-Ossorio Martinez,
  • Yunfei Zhang,
  • Hoda Hamdy,
  • Joel Brodersen,
  • Zhipu Chen,
  • Klaus Bogenberger

摘要

Mobility-on-demand (MoD) services have been extensively investigated across multiple research disciplines. For example, transport planners are interested in the impact on mode choice and traffic, while researchers in operations research focus on operational sub-problems such as assignment, pooling, and routing. These differing perspectives lead to varying setups for simulations and dynamic vehicle routing problems. Many developed approaches and algorithms are not open-source, and results are tied to specific problem settings, making fair comparisons difficult. To address this, we developed FleetPy, an agent-based simulator that provides a consistent and modular platform for studying and comparing MoD services. Its modular structure enables exploration of diverse aspects at varying levels of detail. These modules cover a wide range of fleet control tasks—including implementations of vehicle assignment, repositioning, pricing, and charging methods—alongside customer behavior models, network and routing components, and agent interactions. Well-designed interfaces facilitate coupling with established transport simulation frameworks and have been successfully tested in real-world experiments. FleetPy advances reproducible research in three ways: (1) enabling comparison of new algorithms with existing methods under consistent scenarios, (2) providing benchmark input data for Manhattan, Munich, and Chicago, and (3) supporting transport planners in developing MoD case studies with reduced technical effort.