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Incentive-compatible mechanisms for continuous resource allocation in mobility-as-a-service: Pay-as-You-Go and Pay-as-a-Package
arXiv - CS - Computer Science and Game Theory Pub Date : 2020-09-15 , DOI: arxiv-2009.06806
Haoning Xi, Wei Liu, David Rey, S. Travis Waller, Philip Kilby

Mobility as a Service (MaaS) has recently received a significant attention from researchers, industry stakeholders, and the public sector. The vast majority of existing MaaS paradigms are articulated based on the traditional segmentation of travel modes, e.g. private vehicle, public transportation (bus, metro, light rail) and shared mobility (car/bike/ride-sharing, ride-sourcing). In the context of `Everything-as-a-Service' (XaaS), service providers have evolved from product-based models towards less segmented representations in which resources are priced in a continuous fashion. Yet, such continuous resource allocation formulations are inexistent for MaaS systems. This study attempts to address this gap by introducing innovative MaaS mechanisms that allocate mobility resources to users without any form of travel mode segmentation. We introduce an online auction framework where travelers have the possibility to bid for continuous mobility resources based on their requirements and willingness to pay. We propose two MaaS mechanisms, Pay-as-You-Go (PAYG) and Pay-as-a-Package (PAAP), which allow travelers to either pay for the immediate use of mobility services or to subscribe to mobility service packages for a more protracted usage. Both MaaS mechanisms are based on mixed-integer or linear programming formulations designed to maximize social welfare. We show that the proposed PAYG and PAAP mechanisms are incentive-compatible, develop efficient online primal-dual algorithms to implement the proposed MaaS mechanisms and derive theoretical bounds on the worst-case performance of these algorithms. Moreover, we design a rolling horizon framework to incorporate booking flexibility. Numerical results on extensive problem instances generated from realistic mobility data highlight the benefits of the proposed MaaS mechanisms, and quantify the trade-offs among the proposed approaches.

中文翻译:

移动即服务中持续资源分配的激励兼容机制:即用即付和套餐即付

移动即服务 (MaaS) 最近受到了研究人员、行业利益相关者和公共部门的极大关注。绝大多数现有的 MaaS 范式都基于出行方式的传统细分,例如私家车、公共交通(公共汽车、地铁、轻轨)和共享出行(汽车/自行车/拼车、代驾)。在“一切即服务”(XaaS) 的背景下,服务提供商已经从基于产品的模型演变为资源以连续方式定价的不那么细分的表示。然而,对于 MaaS 系统来说,这种连续的资源分配公式是不存在的。本研究试图通过引入创新的 MaaS 机制来解决这一差距,该机制将移动资源分配给用户,而无需任何形式的出行模式细分。我们引入了一个在线拍卖框架,旅行者可以根据他们的要求和支付意愿对连续移动资源进行竞标。我们提出了两种 MaaS 机制,即现收现付 (PAYG) 和即付即用 (PAAP),允许旅行者为立即使用移动服务付费或订阅移动服务套餐以获得更持久的使用。这两种 MaaS 机制都基于旨在最大化社会福利的混合整数或线性规划公式。我们表明所提出的 PAYG 和 PAAP 机制是激励兼容的,开发有效的在线原始对偶算法来实现所提出的 MaaS 机制,并推导出这些算法在最坏情况下性能的理论界限。此外,我们设计了一个滚动框架来整合预订灵活性。
更新日期:2020-09-16
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