Abstract
Ride-sharing has played an important role in reducing travel costs, traffic congestion and air pollution. However, existing works of order matching in the ride-sharing usually aim to minimize the total travel distances or maximize the profit of the platform running the ride-sharing service. Inefficient matching may result in loss for drivers, and they may not want to participate in the ride-sharing business. In this paper, we intend to solve the order matching issue by maximizing the social welfare of the platform and vehicle drivers. Specifically, we propose two truthful auction based order matching mechanisms, SWMOM-VCG and SWMOM-SASP, where vehicle drivers bid for the orders published by the platform to accomplish the orders and make profits. Compared with SWMOM-VCG, SWMOM-SASP can match a vehicle with multiple orders at each time slot and can do the order matching quicker with only a slight sacrifice of social welfare. We theoretically prove that both mechanisms satisfy the properties such as truthfulness, individual rationality, profitability and so on. We then evaluate the performance of both mechanisms in the real taxi order data in New York city and demonstrate that our mechanisms can achieve higher social welfare than the state-of-the-art approaches.
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Acknowledgement
This paper was funded by the Humanity and Social Science Youth Research Foundation of Ministry of Education (Grant No. 19YJC790111), the Philosophy and Social Science Post-Foundation of Ministry of Education (Grant No. 18JHQ060), Shenzhen Basic Research Foundation (General Program, Grant No. JCYJ20190809175613332) and the Innovation Foundation for Industry, Education and Research in Universities of Science and Technology Development Center of Ministry of Education (Grant No. 2018A02030).
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Shi, B., Luo, Y., Zhu, L. (2020). Auction-Based Order-Matching Mechanisms to Maximize Social Welfare in Real-Time Ride-Sharing. In: Nah, Y., Cui, B., Lee, SW., Yu, J.X., Moon, YS., Whang, S.E. (eds) Database Systems for Advanced Applications. DASFAA 2020. Lecture Notes in Computer Science(), vol 12112. Springer, Cham. https://doi.org/10.1007/978-3-030-59410-7_16
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DOI: https://doi.org/10.1007/978-3-030-59410-7_16
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