Reproducibility in the Control of Autonomous Mobility-on-Demand Systems
Journal:
arXiv
Published Date:
Jun 9, 2025
Abstract
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.
