Autonomous Mobility-on-Demand (AMoD) systems, powered by advances in robotics, control, and Machine Learning (ML), offer a promising paradigm for future urban transportation. AMoD offers fast and personalized travel services by leveraging centralized control of autonomous vehicle fleets to optimize operations and enhance service performance. However, the rapid growth of this field has outpaced the development of standardized practices for evaluating and reporting results, leading to significant challenges in reproducibility. As AMoD control algorithms become increasingly complex and data-driven, a lack of transparency in modeling assumptions, experimental setups, and algorithmic implementation hinders scientific progress and undermines confidence in the results. This paper presents a systematic study of reproducibility in AMoD research. We identify key components across the research pipeline, spanning system modeling, control problems, simulation design, algorithm specification, and evaluation, and analyze common sources of irreproducibility. We survey prevalent practices in the literature, highlight gaps, and propose a structured framework to assess and improve reproducibility. Specifically, concrete guidelines are offered, along with a "reproducibility checklist", to support future work in achieving replicable, comparable, and extensible results. While focused on AMoD, the principles and practices we advocate generalize to a broader class of cyber-physical systems that rely on networked autonomy and data-driven control. This work aims to lay the foundation for a more transparent and reproducible research culture in the design and deployment of intelligent mobility systems.

翻译：自动驾驶按需出行系统凭借机器人学、控制理论和机器学习领域的技术进步，为未来城市交通提供了前景广阔的新型范式。该系统通过集中调度自动驾驶车队来优化运营并提升服务性能，从而提供快速个性化的出行服务。然而，该领域的快速发展已超越评估与结果报告标准化体系的建设进程，导致研究可复现性面临严峻挑战。随着AMoD控制算法日益复杂和数据驱动化，建模假设、实验设置及算法实现中透明度的缺失阻碍了科学进展，并削弱了研究结果的可信度。本文对AMoD研究的可复现性问题展开系统性研究。我们识别了贯穿研究流程的关键环节——涵盖系统建模、控制问题、仿真设计、算法规范与评估体系，并分析了不可复现性的常见成因。通过梳理文献中的通行实践，揭示现存缺陷，进而提出结构化评估框架以提升可复现性。具体而言，本文提供了明确的操作指南及"可复现性核查清单"，以支持未来研究获得可复制、可比较、可扩展的成果。虽然聚焦于AMoD领域，但我们倡导的原则与实践可推广至更广泛的依赖网络化自主性与数据驱动控制的网络物理系统。本研究旨在为智能交通系统设计与部署领域建立更透明、可复现的科研文化奠定基础。