Urban Vehicle Route Choice Modeling Based on Multi-Source Trajectory Data and Adversarial Inverse Reinforcement Learning

Abstract

To address the limitations of traditional route choice models, including their dependence on manually constructed path sets, their difficulty in capturing nonlinear decision preferences, and their insufficient consideration of dynamic congestion factors, this paper proposes an urban vehicle route choice modeling method that integrates trajectory map matching with adversarial inverse reinforcement learning. First, based on taxi GPS (global positioning system) trajectories, the OSM (Open Street Map) road network, Amap real-time traffic conditions, and POI (point of interest) data, a multi-source feature set including road-segment attributes, dynamic congestion, built environment, and destination context is constructed. Second, STGM-Net is used to convert raw GPS point trajectories into road-segment sequences. Furthermore, the route choice process is modeled as a Markov decision process at the road-segment scale, and an AIRL model is constructed to learn the latent reward function from real trajectories. Finally, the SHAP (Shapley additive explanations) method is introduced to analyze the influence of different features on route choice behavior. Experimental results show that the proposed model outperforms traditional discrete choice models and imitation learning models in terms of path similarity, distribution fitting, and traffic-flow restoration. Dynamic congestion level and shortest-path distance are key factors influencing vehicle route choice.