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Route choice modeling is a fundamental task in transportation planning anddemand forecasting. Classical methods generally adopt the discrete choice model(DCM) framework with linear utility functions and high-level routecharacteristics. While several recent studies have started to explore theapplicability of deep learning for route choice modeling, they are limited topath-based models with relatively simple model architectures and relying onpredefined choice sets. Existing link-based models can capture the dynamicnature of link choices within the trip without the need for choice setgeneration, but still assume linear relationships and link-additive features.To address these issues, this study proposes a general deep inversereinforcement learning (IRL) framework for link-based route choice modeling,which is capable of incorporating diverse features (of the state, action andtrip context) and capturing complex relationships. Specifically, we adapt anadversarial IRL model to the route choice problem for efficient estimation ofcontext-dependent reward functions without value iteration. Experiment resultsbased on taxi GPS data from Shanghai, China validate the superior predictionperformance of the proposed model over conventional DCMs and other imitationlearning baselines, even for destinations unseen in the training data. Furtheranalysis show that the model exhibits competitive computational efficiency andreasonable interpretability. The proposed methodology provides a new directionfor future development of route choice models. It is general and can beadaptable to other route choice problems across different modes and networks.

A deep inverse reinforcement learning approach to route choice modeling with context-dependent rewards