Learning Dynamics Feature Representation via Policy Attention for Dynamic Path Planning in Urban Road Networks

Dynamic Path Planning (DPP) in urban road networks faces fundamental challenges, as traffic conditions change rapidly over time and often render planned routes ineffective. Reinforcement Learning (RL) provides an effective way to adaptively handle such uncertainties by incorporating traffic dynamics into state, but its performance crucially depends on how these dynamics are represented. Existing approaches either rely on global traffic information, which ensures decision completeness but suffers from redundancy and high computational cost, or oversimplified local features, which are efficient but often omit critical dynamics and lead to suboptimal paths. To address this, we propose a Dynamics Feature Representation (DFR) framework that progressively refines global traffic dynamics into compact features for RL-based DPP. Specifically, we introduce a policy attention mechanism that identifies a core subset of global dynamics by extracting the top-k shortest paths, and further constructs node-related local features by intersecting with n-hop neighborhoods, enabling near-optimal policy learning. Theoretical analysis demonstrates that DFR guarantees state completeness, while empirical results confirm that, compared to classical baselines and standard RL methods, DFR significantly improves path planning performance and accelerates convergence. This work highlights the central role of feature representation in RL-based DPP and proposes a general framework that balances information sufficiency with computational efficiency, paving the way for scalable dynamic decision-making in real-world transportation systems.