通过匹配与定价内化巡游感知出租车车队的感知外部性
Internalizing sensing externality via matching and pricing for drive-by sensing taxi fleets
AI总结：
本文针对网约车出租车车队的巡游感知问题，提出基于双向拍卖的联合匹配定价方案，该方案具备多种优良经济特性，可有效内化感知外部性并实现与服务水平、预算平衡的协同。
摘要：
巡游感知（drive-by sensing）是一种极具前景的数据采集范式，它利用车辆的移动性以低成本监测城市环境，为城市交通活动带来正外部性。本文聚焦网约车服务，通过设计一种基于双向拍卖流程的联合匹配与定价方案，挖掘出租车车队的感知潜力。匹配模块通过优先安排高感知潜力的行程来最大化感知效用，定价模块则根据参与者对感知效用的贡献分配相应的社会福利。研究表明，所提方案具备配置效率、个体理性、预算平衡、无嫉妒性以及群体激励相容性。其中最后一项特性保证了参与者作为一个群体，无论其成员是否存在谎报行为，最终获得的总效用都保持一致。基于真实场景的大量数值测试显示，感知外部性能够与服务水平及预算平衡实现良好协同。通过基于场景的敏感性分析，本文还得出了关于所提方案适用性与有效性的多项管理启示。
英文摘要：
Drive-by sensing is a promising data collection paradigm that leverages the mobilities of vehicles to survey urban environments at low costs, contributing to the positive externality of urban transport activities. Focusing on e-hailing services, this paper explores the sensing potential of taxi fleets, by designing a joint matching and pricing scheme based on a double auction process. The matching module maximizes the sensing utility by prioritizing trips with high sensing potentials, and the pricing module allocates the corresponding social welfare according to the participants' contributions to the sensing utility. We show that the proposed scheme is allocative efficient, individually rational, budget balancing, envy-free, and group incentive compatible. The last notion guarantees that the participants, as a cohort, will end up with the same total utility regardless of mis-reporting on part of its members. Extensive numerical tests based on a real-world scenario reveal that the sensing externality can be well aligned with the level of service and budget balance. Various managerial insights regarding the applicability and efficacy of the proposed scheme are generated through scenario-based sensitivity analyses.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
