Deep reinforcement learning of passenger behavior in multimodal journey planning with proportional fairness

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dc.contributor.authorChu, Kai-Fung
dc.contributor.authorGuo, Weisi
dc.date.accessioned2023-08-07T10:43:50Z
dc.date.available2023-08-07T10:43:50Z
dc.date.issued2023-07-20
dc.description.abstractMultimodal 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.en_UK
dc.identifier.citationChu K-F, Guo W. (2023) Deep reinforcement learning of passenger behavior in multimodal journey planning with proportional fairness. Neural Computing and Applications, Volume 35, September 2023, pp. 20221-20240en_UK
dc.identifier.issn0941-0643
dc.identifier.urihttps://doi.org/10.1007/s00521-023-08733-4
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/20055
dc.language.isoenen_UK
dc.publisherSpringeren_UK
dc.rightsAttribution 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectintelligent transportation systemsen_UK
dc.subjectdeep reinforcement learningen_UK
dc.subjectpassenger behavioren_UK
dc.subjectoptimizationen_UK
dc.subjectproportional fairnessen_UK
dc.titleDeep reinforcement learning of passenger behavior in multimodal journey planning with proportional fairnessen_UK
dc.typeArticleen_UK

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