Deep reinforcement learning of passenger behavior in multimodal journey planning with proportional fairness
Deep reinforcement learning of passenger behavior in multimodal journey planning with proportional fairness
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DOI：
10.1007/s00521-023-08733-4
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发表时间：
2023-07
影响因子：
6
通讯作者：
Kai-Fung Chu;Weisi Guo
中科院分区：
文献类型：
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作者：
Kai-Fung Chu;Weisi Guo
多式联运系统需要有效的旅程规划者将多名乘客分配给运输运营商。一个例子是移动即服务，这是一种通过单一平台集成各种运输模式的新型移动服务。在这样的多式联运和多样化的旅程规划问题中，适应具有不同和动态偏好的异质乘客可能具有挑战性。此外，乘客的行为可能基于经验和期望，从某种意义上说，交通经验会影响他们的状态和下一次交通服务的决定。当前将每次旅程规划优化视为非时变单一体验问题的方法无法充分模拟一段时间内许多旅程的乘客体验和记忆。在本文中，我们将乘客体验建模为马尔可夫模型，其中先前的体验对未来的长期满意度和保留率有短暂的影响。因此，我们制定了一个多目标旅程规划问题，考虑了乘客的个人偏好、经历和记忆。所提出的方法动态确定效用权重，以便根据个人乘客的状态获得最佳旅程计划。为了平衡每个运输运营商收到的利润，我们提出了基于变体的比例公平。我们使用真实世界和合成数据集进行的实验表明，与基线方法相比，我们的方法提高了乘客满意度。我们证明，总体利润增加了 2.3 倍，满意度水平越高，留存率越高。我们提出的方法可以促进交通运营商的参与并促进乘客对 MaaS 的接受。
Multimodal transportation systems require an effective journey planner to allocate multiple passengers to transport operators. One example is mobility-as-a-service, a new mobility service that integrates various transport modes through a single platform. In such a multimodal and diverse journey planning problem, accommodating heterogeneous passengers with different and dynamic preferences can be challenging. Furthermore, passengers may behave based on experiences and expectations, in the sense that the transport experience affects their state and decision of the next transport service. Current methods of treating each journey planning optimization as a non-time varying single experience problem cannot adequately model passenger experience and memories over many journeys over time. In this paper, we model passenger experience as a Markov model where prior experiences have a transient effect on future long-term satisfaction and retention rate. As such, we formulate a multi-objective journey planning problem that considers individual passenger preferences, experiences, and memories. The proposed approach dynamically determines utility weights to obtain an optimal journey plan for individual passengers based on their status. To balance the profit received by each transport operator, we present a variant-based proportional fairness. Our experiments using real-world and synthetic datasets show that our approach enhances passenger satisfaction, compared to baseline methods. We demonstrate that the overall profit is increased by 2.3 times, resulting in a higher retention rate caused by higher satisfaction levels. Our proposed approach can facilitate the participation of transport operators and promote passenger acceptance of MaaS.