{"type":"ScholarlyArticle","title":"Dynamic Car Dispatching and Pricing: Revenue and Fairness for Ridesharing Platforms","authors":[{"name":"Zishuo Zhao","slug":"zishuo-zhao","given":"Zishuo","family":"Zhao","affiliation":"University of Illinois Urbana-Champaign ,"},{"name":"Xi Chen","slug":"xi-chen","given":"Xi","family":"Chen","affiliation":"New York University ,"},{"name":"Xuefeng Zhang","slug":"xuefeng-zhang","given":"Xuefeng","family":"Zhang","affiliation":"University of Illinois Urbana-Champaign ,"},{"name":"Yuan Zhou","slug":"yuan-zhou","given":"Yuan","family":"Zhou","affiliation":"Yau Mathematical Sciences Center , Tsinghua University"}],"abstract":"A major challenge for ridesharing platforms is to guarantee profit and fairness simultaneously, especially in the presence of misaligned incentives of drivers and riders. We focus on the  dispatching-pricing problem to maximize the total revenue while keeping both drivers and riders satisfied. We study the computational complexity of the problem, provide a novel two-phased pricing solution with revenue and fairness guarantees, extend it to stochastic settings and develop a dynamic (a.k.a., learning-while-doing) algorithm that actively collects data to learn the demand distribution during the scheduling process. We also conduct extensive experiments to demonstrate the effectiveness of our algorithms.","doi":"10.24963/ijcai.2022/652","url":"https://ijcai.org/proceedings/2022/652","pdf":"https://ijcai.org/proceedings/2022/0652.pdf","markdown":"https://ijcai.org/proceedings/2022/652.md","bibtex":"https://ijcai.org/proceedings/2022/bibtex/652","volume":{"slug":"2022","title":"Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2022","url":"https://ijcai.org/proceedings/2022/","year":2022,"location":"Vienna","dates":"23-29 July 2022","editors":"Luc De Raedt","isbn":"978-1-956792-00-3","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2022"},"section":"Planning and Scheduling","track":"Main Track","number":652,"pages":{"first":4701,"last":4708},"keywords":["Planning and Scheduling: Planning under Uncertainty","AI Ethics, Trust, Fairness: Fairness & Diversity","Agent-based and Multi-agent Systems: Mechanism Design","Planning and Scheduling: Planning with Incomplete Information","Planning and Scheduling: Planning Algorithms"],"videos":[{"label":"Video #1","url":"https://slideslive.com/38987005","length_sec":73,"provider":"slideslive"},{"label":"Video #2","url":"https://slideslive.com/38985148","length_sec":697,"provider":"slideslive"}],"references":[{"position":1,"raw":"[Al et al., 2005] Maiwenn J. Al, Talitha L. Feenstra, and Ben A. van Hout. Optimal allocation of resources over health care programmes: dealing with decreasing marginal utility and uncertainty. Health Economics, 14(7):655-667, 2005.","title":"Optimal allocation of resources over health care programmes: dealing with decreasing marginal utility and uncertainty","authors":"J Maiwenn, Talitha L Al, Ben A Feenstra, Van Hout","year":"2005","venue":"Health Economics","doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Bei and Zhang, 2018] Xiaohui Bei and Shengyu Zhang. Al- gorithms for trip-vehicle assignment in ride-sharing. In Thirty-second AAAI conference on artificial intelligence, 2018.","title":"Algorithms for trip-vehicle assignment in ride-sharing","authors":"Xiaohui Bei, Shengyu Zhang","year":"2018","venue":"Thirty-second AAAI conference on artificial intelligence","doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Bimpikis et al., 2019] K. Bimpikis, O. Candogan, and D. Saban. Spatial pricing in ride-sharing networks. Op- erations Research, 67, 2019.","title":"Spatial pricing in ride-sharing networks","authors":"K Bimpikis, O Candogan, D Saban","year":"2019","venue":"Operations Research","doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Castillo et al., 2017] Juan Camilo Castillo, Dan Knoepfle, and Glen Weyl. Surge pricing solves the wild goose chase. In ACM Conference on Economics and Compu- tation, pages 241-242, 2017.","title":"Surge pricing solves the wild goose chase","authors":"Castillo","year":"2017","venue":"ACM Conference on Economics and Computation","doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Chawla et al., 2007] Shuchi Chawla, Jason D Hartline, and Robert Kleinberg. Algorithmic pricing via virtual valua- tions. In Proceedings of the 8th ACM Conference on Elec- tronic Commerce, pages 243-251, 2007.","title":"Algorithmic pricing via virtual valuations","authors":"Shuchi Chawla, Jason D Hartline, Robert Kleinberg","year":"2007","venue":"Proceedings of the 8th ACM Conference on Electronic Commerce","doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[Cohen et al., 2019] Michael B. Cohen, Yin Tat Lee, and Zhao Song. Solving linear programs in the current ma- trix multiplication time. In Proceedings of the 51st An- nual ACM SIGACT Symposium on Theory of Computing, page 938-942, New York, NY, USA, 2019. Association for Computing Machinery.","title":"Solving linear programs in the current matrix multiplication time","authors":"B Michael, Yin Cohen, Zhao Tat Lee, Song","year":"2019","venue":"Proceedings of the 51st Annual ACM SIGACT Symposium on Theory of Computing","doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[Didi Chuxing, 2021] Didi Chuxing. The GAIA Pub- lic Dataset. https://outreach.didichuxing.com/research/ opendata/en/, 2021. Accessed: 2021-08-31.","title":"The GAIA Public Dataset","authors":"Didi Chuxing","year":"2021","venue":null,"doi":null,"url":"https://outreach.didichuxing.com/research/opendata/en/","internal_url":null},{"position":8,"raw":"[Glazer and Rubinstein, 1996] Jacob Glazer and Ariel Ru- binstein. An extensive game as a guide for solving a nor- mal game. journal of economic theory, 70(1):32-42, 1996.","title":"Jacob Glazer and Ariel Rubinstein. An extensive game as a guide for solving a normal game","authors":"Rubinstein Glazer","year":"1996","venue":"journal of economic theory","doi":null,"url":null,"internal_url":null},{"position":9,"raw":"[Hrnčíř et al., 2015] Jan Hrnčíř, Michael Rovatsos, and Michal Jakob. Ridesharing on timetabled transport ser- vices: A multiagent planning approach. Journal of Intelli- gent Transportation Systems, 19(1):89-105, 2015.","title":"Ridesharing on timetabled transport services: A multiagent planning approach","authors":"Hrnčíř","year":"2015","venue":"Journal of Intelligent Transportation Systems","doi":null,"url":null,"internal_url":null},{"position":10,"raw":"[Jiang et al., 2020] Haotian Jiang, Tarun Kathuria, Yin Tat Lee, Swati Padmanabhan, and Zhao Song. A faster interior point method for semidefinite programming. In 2020 IEEE 61st Annual Symposium on Foundations of Computer Sci- ence (FOCS), pages 910-918. IEEE, 2020.","title":"A faster interior point method for semidefinite programming","authors":"Jiang","year":"2020","venue":"IEEE 61st Annual Symposium on Foundations of Computer Science (FOCS)","doi":null,"url":null,"internal_url":null},{"position":11,"raw":"[Li et al., 2019] Minne Li, Zhiwei Qin, Yan Jiao, Yaodong Yang, Jun Wang, Chenxi Wang, Guobin Wu, and Jieping Ye. Efficient ridesharing order dispatching with mean field multi-agent reinforcement learning. WWW '19, 2019.","title":"Efficient ridesharing order dispatching with mean field multi-agent reinforcement learning","authors":"Minne Li, Zhiwei Qin, Yan Jiao, Yaodong Yang, Jun Wang, Chenxi Wang, Guobin Wu, Jieping Ye","year":"2019","venue":"WWW '19","doi":null,"url":null,"internal_url":null},{"position":12,"raw":"[Ma et al., 2020] Hongyao Ma, Fei Fang, and David C. Parkes. Spatio-temporal pricing for ridesharing platforms. SIGecom Exch., 18(2):53-57, December 2020.","title":"Spatio-temporal pricing for ridesharing platforms","authors":"Hongyao Ma, Fei Fang, David C Parkes","year":"2020","venue":"SIGecom Exch","doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[Qin et al., 2020] T. Qin, X. Tang, Y. Jiao, F. Zhang, and J. Ye. Ride-hailing order dispatching at didi via reinforce- ment learning. Interface, 50(5):272-286, 2020.","title":"Ride-hailing order dispatching at didi via reinforcement learning","authors":"T Qin, X Tang, Y Jiao, F Zhang, J Ye","year":"2020","venue":"Interface","doi":null,"url":null,"internal_url":null},{"position":14,"raw":"[Riquelme et al., 2015] Carlos Riquelme, Siddhartha Baner- jee, and Ramesh Johari. Pricing in ride-sharing platforms: A queueing-theoretic approach. In ACM Conference on Economics and Computation, page 639, 2015.","title":"Pricing in ride-sharing platforms: A queueing-theoretic approach","authors":"Carlos Riquelme, Siddhartha Banerjee, Ramesh Johari","year":"2015","venue":"ACM Conference on Economics and Computation","doi":null,"url":null,"internal_url":null},{"position":15,"raw":"[Tardos, 1985] Éva Tardos. A strongly polynomial minimum cost circulation algorithm. Combinatorica, 5(3):247-255, 1985.","title":"A strongly polynomial minimum cost circulation algorithm","authors":"Éva Tardos","year":"1985","venue":"Combinatorica","doi":null,"url":null,"internal_url":null},{"position":16,"raw":"[Wang and Zhang, 2011] Jian Wang and Yi Zhang. Utilizing marginal net utility for recommendation in e-commerce. In Proceedings of the 34th international ACM SIGIR con- ference on Research and development in Information Re- trieval, pages 1003-1012, 2011.","title":"Utilizing marginal net utility for recommendation in e-commerce","authors":"Wang, Zhang ; ] Jian Wang, Yi Zhang","year":"2011","venue":"Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval","doi":null,"url":null,"internal_url":null},{"position":17,"raw":"[Wang et al., 2018] Zhaodong Wang, Zhiwei Qin, Xi- aocheng Tang, Jieping Ye, and Hongtu Zhu. Deep reinforcement learning with knowledge transfer for online rides order dispatching. In 2018 IEEE International Conference on Data Mining (ICDM), pages 617-626. IEEE, 2018.","title":"Deep reinforcement learning with knowledge transfer for online rides order dispatching","authors":"Zhaodong Wang, Zhiwei Qin, Xiaocheng Tang, Jieping Ye, Hongtu Zhu","year":"2018","venue":"IEEE International Conference on Data Mining (ICDM)","doi":null,"url":null,"internal_url":null},{"position":18,"raw":"[Yan et al., 2018] Chiwei Yan, Helin Zhu, Nikita Korolko, and Dawn Woodard. Dynamic pricing and matching in ride-hailing platforms. Naval Research Logistics, Forth- coming, 2018.","title":"Dynamic pricing and matching in ride-hailing platforms","authors":"Yan","year":"2018","venue":"Forthcoming","doi":null,"url":null,"internal_url":null},{"position":19,"raw":"[Zhao et al., 2015] Tongtiegang Zhao, Jianshi Zhao, Pan Liu, and Xiaohui Lei. Evaluating the marginal utility principle for long-term hydropower scheduling. Energy Conversion and Management, 106:213-223, 2015.","title":"Evaluating the marginal utility principle for long-term hydropower scheduling","authors":"Zhao","year":"2015","venue":"Energy Conversion and Management","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}