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.

翻译：路径选择建模是交通规划与需求预测中的基础任务。传统方法通常采用离散选择模型（DCM）框架，结合线性效用函数与高层次路径特征。尽管近期部分研究已开始探索深度学习在路径选择建模中的适用性，但此类方法局限于基于路径的模型，这些模型结构相对简单且依赖预定义选择集。现有基于路段的模型虽无需生成选择集即可捕捉出行过程中路段选择的动态特性，但仍假设线性关系与路段可加特征。为解决上述问题，本研究提出一种通用的深度逆强化学习（IRL）框架，用于基于路段的路径选择建模，该框架能够融合多样化特征（状态、动作及出行情境）并捕捉复杂关系。具体而言，我们针对路径选择问题调整对抗式IRL模型，以便在不进行值迭代的情况下高效估计情境依赖的奖励函数。基于中国上海市出租车GPS数据的实验结果表明，所提模型在预测性能上显著优于传统DCM及其他模仿学习基线方法，即使对于训练数据中未出现的目的地亦表现优异。进一步分析显示，该模型兼具竞争性的计算效率与合理的可解释性。本研究提出的方法为路径选择模型的未来发展提供了新方向，其通用性强，可适用于不同交通模式与网络下的其他路径选择问题。