Safety-aware smart parking recommendations for shared micro-mobility using deep reinforcement learning

The rapid expansion of shared micro-mobility services has intensified safety concerns in dense urban environments in recent years. Traffic complexity and infrastructure limitations increase accident risks, yet safety aspects are often overlooked by operators and existing decision-support systems. Current safety-oriented approaches mainly rely on static analyzes, historical accident data, or infrastructure-based interventions,
which often lack adaptability to real-time urban dynamics and offer limited practical deployment at the user level. This paper addresses these limitations through DRL-SMSafe, a deep reinforcement learning–based framework for improving
real-time micro-mobility safety via smart parking recommendations, focusing on
the initial and final segments of trips. The proposed approach dynamically identifies
alternative safer drop-off zones at the moment of a user’s parking request by leveraging both static and dynamic, easily accessible safety-related factors. To preserve user convenience, recommendations are restricted to neighboring zones, minimizing the distance between the user’s initially selected location and the suggested alternative. Comparative experimental results, supported by a detailed analysis and examination of the outcomes against baseline methods, demonstrate the effectiveness of the proposed framework and its suitability for real-time decision-making.