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:路由选择模型是运输规划和需求预测的一项基本任务。古典方法通常采用具有线性通用功能和高水平路线特点的离散选择模型框架(DCM),尽管最近一些研究已开始探索选择路由选择模型的深层次学习适用性,但限于具有相对简单的模型架构和依赖预先界定的选择数据集的路径模型。现有的基于链接的模型可以在旅行中捕捉链接选择的动态性质,而不需要作出选择,但仍然承担线性关系和连接补充特征。为解决这些问题,本研究提出了基于链接的路线选择模型通用深层强化学习(IRL)框架,该框架能够纳入多种特征(州、行动和旅行背景)并捕捉复杂的关系。具体地说,我们将一个基于竞争的IRL模型适用于选择路径问题,以便有效估计基于背景的奖励功能,而无需再加码。中国上海的出租车GPS数据实验结果验证了拟议模型优于常规DCM和其他模拟学习基线的预测性业绩,即使是在培训数据中看不见的目的地。进一步分析方法可以解释其他选择模式的成本效益。</s>