{"type":"ScholarlyArticle","title":"Ridesharing with Driver Location Preferences","authors":[{"name":"Duncan Rheingans-Yoo","slug":"duncan-rheingans-yoo","given":"Duncan","family":"Rheingans-Yoo","affiliation":"Harvard College"},{"name":"Scott Duke Kominers","slug":"scott-duke-kominers","given":"Scott Duke","family":"Kominers","affiliation":"Harvard Business School and Department of Economics"},{"name":"Hongyao Ma","slug":"hongyao-ma","given":"Hongyao","family":"Ma","affiliation":"Harvard School of Engineering and Applied Sciences"},{"name":"David C. Parkes","slug":"david-c-parkes","given":"David C.","family":"Parkes","affiliation":"Harvard School of Engineering and Applied Sciences"}],"abstract":"We study revenue-optimal pricing and driver compensation in ridesharing platforms when drivers have heterogeneous preferences over locations. If a platform ignores drivers' location preferences, it may make inefficient trip dispatches; moreover, drivers may strategize so as to route towards their preferred locations. In a model with stationary and continuous demand and supply, we present a mechanism that incentivizes drivers to both (i) report their location preferences truthfully and (ii) always provide service. In settings with unconstrained driver supply or symmetric demand patterns, our mechanism achieves (full-information) first-best revenue. Under supply constraints and unbalanced demand, we show via simulation that our mechanism improves over existing mechanisms and has performance close to the first-best.","doi":"10.24963/ijcai.2019/79","url":"https://ijcai.org/proceedings/2019/79","pdf":"https://ijcai.org/proceedings/2019/0079.pdf","markdown":"https://ijcai.org/proceedings/2019/79.md","bibtex":"https://ijcai.org/proceedings/2019/bibtex/79","volume":{"slug":"2019","title":"Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2019","url":"https://ijcai.org/proceedings/2019/","year":2019,"location":"Macao","dates":"10-16 August 2019","editors":"Sarit Kraus","isbn":"978-0-9992411-4-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2019"},"section":"Agent-based and Multi-agent Systems","track":"Main track","number":79,"pages":{"first":557,"last":564},"keywords":["Agent-based and Multi-agent Systems: Economic Paradigms, Auctions and Market-Based Systems","Agent-based and Multi-agent Systems: Algorithmic Game Theory","Multidisciplinary Topics and Applications: Transportation"],"videos":[],"references":[{"position":1,"raw":"[Afèche et al., 2018] Philipp Afèche, Zhe Liu, and Costis Maglaras. 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