基于深度强化学习的动态多式联运网络中的联合出行方式和出发时间选择模型

Ziyuan Gu , Yukai Wang , Wei Ma , Zhiyuan Liu
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引用次数: 0

摘要

动态多式联运网络中的出行选择决策并非易事。在本文中,我们提出了一种基于深度强化学习(DRL)的新型联合出行模式和出发时间选择(JTMDTC)模型,以解决这一问题。该模型的目标是最大化个人在多天内的旅行效用,具体做法是建立一个特定问题的马尔可夫决策过程来描述多天的 JTMDTC,并开发一个定制的深度 Q 网络作为解析方案。为使该方法适用于提出旅行决策要求的众多个体,我们将聚类方法与 DRL 相结合,以获得用于模型训练的代表性个体,从而形成了一种优雅且计算效率高的方法。为了证明所提方法的有效性,我们在中国苏州的实际网络中进行了基于多模式微观交通模拟的大量数值实验。实验结果表明,所提出的方法能够在复杂的交通环境中为不同的个体制定(接近)最优的 JTMDTC,与其他替代方法相比,该方法能够持续产生更高的出行效用,并且对不同的模型参数变化具有鲁棒性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A joint travel mode and departure time choice model in dynamic multimodal transportation networks based on deep reinforcement learning

Decision on travel choices in dynamic multimodal transportation networks is non-trivial. In this paper, we tackle this problem by proposing a new joint travel mode and departure time choice (JTMDTC) model based on deep reinforcement learning (DRL). The objective of the model is to maximize individuals travel utilities across multiple days, which is accomplished by establishing a problem-specific Markov decision process to characterize the multi-day JTMDTC, and developing a customized Deep Q-Network as the resolution scheme. To render the approach applicable to many individuals with travel decision-making requests, a clustering method is integrated with DRL to obtain representative individuals for model training, thus resulting in an elegant and computationally efficient approach. Extensive numerical experiments based on multimodal microscopic traffic simulation are conducted in a real-world network of Suzhou, China to demonstrate the effectiveness of the proposed approach. The results indicate that the proposed approach is able to make (near-)optimal JTMDTC for different individuals in complex traffic environments, that it consistently yields higher travel utilities compared with other alternatives, and that it is robust to different model parameter changes.

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