{"type":"ScholarlyArticle","title":"Real-Time Dispatching of Large-Scale Ride-Sharing Systems: Integrating Optimization, Machine Learning, and Model Predictive Control","authors":[{"name":"Connor Riley","slug":"connor-riley","given":"Connor","family":"Riley","affiliation":"Georgia Institute of Technology , Atlanta , USA"},{"name":"Pascal van Hentenryck","slug":"pascal-van-hentenryck","given":"Pascal","family":"Van Hentenryck","affiliation":"Georgia Institute of Technology , Atlanta , USA"},{"name":"Enpeng Yuan","slug":"enpeng-yuan","given":"Enpeng","family":"Yuan","affiliation":"Georgia Institute of Technology , Atlanta , USA"}],"abstract":"This paper considers the dispatching of large-scale real-time ride-sharing systems to address congestion issues faced by many cities. The goal is to serve all customers (service guarantees) with a small number of vehicles while minimizing waiting times under constraints on ride duration. This paper proposes an end-to-end approach that tightly integrates a state-of-the-art dispatching algorithm, a machine-learning model to predict zone-to-zone demand over time, and a model predictive control optimization to relocate idle vehicles. Experiments using historic taxi trips in New York City indicate that this integration decreases average waiting times by about 30% over all test cases and reaches close to 55% on the largest instances for high-demand zones.","doi":"10.24963/ijcai.2020/609","url":"https://ijcai.org/proceedings/2020/609","pdf":"https://ijcai.org/proceedings/2020/0609.pdf","markdown":"https://ijcai.org/proceedings/2020/609.md","bibtex":"https://ijcai.org/proceedings/2020/bibtex/609","volume":{"slug":"2020","title":"Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2020","url":"https://ijcai.org/proceedings/2020/","year":2020,"location":"Yokohama","dates":"January 2021 (postponed from 11-17 July 2020)","editors":"Christian Bessiere","isbn":"978-0-9992411-6-5","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2020"},"section":"Special track on AI for CompSust and Human well-being","track":"Special track on AI for CompSust and Human well-being","number":609,"pages":{"first":4417,"last":4423},"keywords":["Uncertainty in AI: Sequential Decision Making","Machine Learning Applications: Other","Planning and Scheduling: Planning and Scheduling","Constraints and SAT: Constraint Optimization"],"videos":[{"label":"Video","url":"https://videos.ijcai20.org/24577/file_24577.m3u8","length_sec":259,"provider":"hls"}],"references":[{"position":1,"raw":"[Alonso-Mora et al., 2017] Javier Alonso-Mora, Samitha Samaranayake, Alex Wallar, Emilio Frazzoli, and Daniela Rus. 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