  Route choice modeling is a fundamental task in transportation planning and
demand forecasting. Classical methods generally adopt the discrete choice model
(DCM) framework with linear utility functions and high-level route
characteristics. While several recent studies have started to explore the
applicability of deep learning for route choice modeling, they are limited to
path-based models with relatively simple model architectures and relying on
predefined choice sets. Existing link-based models can capture the dynamic
nature of link choices within the trip without the need for choice set
generation, but still assume linear relationships and link-additive features.
To address these issues, this study proposes a general deep inverse
reinforcement learning (IRL) framework for link-based route choice modeling,
which is capable of incorporating diverse features (of the state, action and
trip context) and capturing complex relationships. Specifically, we adapt an
adversarial IRL model to the route choice problem for efficient estimation of
context-dependent reward functions without value iteration. Experiment results
based on taxi GPS data from Shanghai, China validate the superior prediction
performance of the proposed model over conventional DCMs and other imitation
learning baselines, even for destinations unseen in the training data. Further
analysis show that the model exhibits competitive computational efficiency and
reasonable interpretability. The proposed methodology provides a new direction
for future development of route choice models. It is general and can be
adaptable to other route choice problems across different modes and networks.
