Highly efficient and scalable multi-hop ride-sharing

On-demand ride-sharing services such as Uber and Lyft have gained tremendous popularity over the past decade, largely driven by the omnipresence of mobile devices. Ride-sharing services can provide economic and environmental benefits such as reducing traffic congestion and vehicle emissions. Multi-h...

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Bibliographic Details
Main Authors: XU, Yixin, KULIK, Lars, BOROVICA‐GAJIC, Renata, ALDWYISH, Abdullah, QI, Jianzhong
Format: text
Language:English
Published: Institutional Knowledge at Singapore Management University 2020
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Online Access:https://ink.library.smu.edu.sg/sis_research/8305
https://ink.library.smu.edu.sg/context/sis_research/article/9308/viewcontent/SIGSPATIAL2020_MultiHopRideSharing.pdf
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Institution: Singapore Management University
Language: English
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Summary:On-demand ride-sharing services such as Uber and Lyft have gained tremendous popularity over the past decade, largely driven by the omnipresence of mobile devices. Ride-sharing services can provide economic and environmental benefits such as reducing traffic congestion and vehicle emissions. Multi-hop ride-sharing enables passengers to transfer between vehicles within a single trip, which significantly extends the benefits of ride-sharing and provides ride opportunities that are not possible otherwise. Despite its advantages, offering real-time multi-hop ride-sharing services at large scale is a challenging computational task due to the large combination of vehicles and passenger transfer points. To address these challenges, we propose exact and approximation algorithms that are scalable and achieve real-time responses for highly dynamic ride-sharing scenarios in large metropolitan areas. Our experiments on real-world datasets show the benefits of multi-hop ride-sharing services and demonstrate that our proposed algorithms are more than two orders of magnitude faster than the state-of-the-art. Our approximation algorithms offer a comparable trip quality to our exact algorithm, while improving the ride-sharing request matching time by another order of magnitude.