A real-time dynamic vehicle path optimization framework for urban logistics based on deep reinforcement learning

Abstract

This paper proposes a real-time dynamic vehicle path optimization framework for urban logistics, driven by advanced deep reinforcement learning techniques. The study describes the urban vehicle routing problem as a Markov decision process, integrating fleet operations, dynamic traffic conditions, and constantly arriving customer orders from heterogeneous realtime data streams. A graph-based neural network architecture for capturing complex spatio-temporal dependencies. This enables the system to learn and adapt to rapidly changing urban routing strategies. Both synthetic and real-world datasets are extensively tested. The proposed methods significantly reduce the operational cost and delivery latency. These methods are very effective compared to adaptive heuristic algorithms and traditional machine learning baselines. Maintaining robust performance under high traffic fluctuation and demand uncertainty is crucial for spatio-temporal feature extraction and network architecture optimization. The study demonstrates the feasibility and effectiveness of deep reinforcement learning technology in large-scale real-time logistics optimization in cities, and provides an important reference for the application of intelligent data-driven scheduling and path planning systems in complex urban networks.