research-article

Socially-Equitable Interactive Graph Information Fusion-based Prediction for Urban Dockless E-Scooter Sharing

Authors:

Kang G. ShinAuthors Info & Claims

WWW '22: Proceedings of the ACM Web Conference 2022

Pages 3269 - 3279

https://doi.org/10.1145/3485447.3512145

Published: 25 April 2022 Publication History

Abstract

Urban dockless e-scooter sharing (DES) has become a popular Web-of-Things (WoT) service and widely adopted globally. Despite its early commercial success, conventional mobility demand and supply prediction based on machine learning and subsequent redistribution may favor advantaged socio-economic communities and tourist regions, at the expense of reducing mobility accessibility and resource allocation for historically disadvantaged communities. To address this unfairness, we propose a socially-Equitable Interactive Graph information fusion-based mobility flow prediction system for Dockless E-scooter Sharing (EIGDES). By considering city regions as nodes connected by trips, EIGDES learns and captures the complex interactions across spatial and temporal graph features through a novel interactive graph information dissemination and fusion structure. We further design a novel model learning objective with metrics that capture both the mobility distributions and the socio-economic factors, ensuring spatial fairness in the communities’ resource accessibility and their experienced DES prediction accuracy. Through its integration with the optimization regularizer, EIGDES jointly learns the DES flow patterns and socio-economic factors, and returns socially-equitable flow predictions. Our in-depth experimental study upon more than 2,122,270 DES trips from three metropolitan cities in North America has demonstrated EIGDES’s effectiveness in accurate prediction of DES flow patterns with substantial reduction of mobility unfairness.

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Cited By

Wang GHe SJiang LWang SMiao FZhang FDong ZZhang D(2024)FairMove: A Data-Driven Vehicle Displacement System for Jointly Optimizing Profit Efficiency and Fairness of Electric For-Hire VehiclesIEEE Transactions on Mobile Computing10.1109/TMC.2023.332667623:6(6785-6802)Online publication date: Jun-2024
https://doi.org/10.1109/TMC.2023.3326676
Tan HYuan YYan HZhong SYang Y(2024)Human Preference-aware Rebalancing and Charging for Shared Electric Micromobility Vehicles2024 IEEE International Conference on Robotics and Automation (ICRA)10.1109/ICRA57147.2024.10610713(9608-9615)Online publication date: 13-May-2024
https://doi.org/10.1109/ICRA57147.2024.10610713
Zhan YHaddadi HMashhadi A(2023)Analysing Fairness of Privacy-Utility Mobility ModelsAdjunct Proceedings of the 2023 ACM International Joint Conference on Pervasive and Ubiquitous Computing & the 2023 ACM International Symposium on Wearable Computing10.1145/3594739.3610676(359-365)Online publication date: 8-Oct-2023
https://dl.acm.org/doi/10.1145/3594739.3610676

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Socially-Equitable Interactive Graph Information Fusion-based Prediction for Urban Dockless E-Scooter Sharing

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cover image ACM Conferences

WWW '22: Proceedings of the ACM Web Conference 2022

April 2022

3764 pages

ISBN:9781450390965

DOI:10.1145/3485447

Editors:
Frédérique Laforest
INSA Lyon, France
,
Raphaël Troncy
EURECOM, France
,
Elena Simperl
King’s College London, UK
,
Deepak Agarwal
Pinterest, USA
,
Aristides Gionis
KTH Royal Institute of Technology, Sweden
,
Ivan Herman
W3C / retired
,
Lionel Médini
Université Lyon 1, France

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Published: 25 April 2022

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Cited By

Wang GHe SJiang LWang SMiao FZhang FDong ZZhang D(2024)FairMove: A Data-Driven Vehicle Displacement System for Jointly Optimizing Profit Efficiency and Fairness of Electric For-Hire VehiclesIEEE Transactions on Mobile Computing10.1109/TMC.2023.332667623:6(6785-6802)Online publication date: Jun-2024
https://doi.org/10.1109/TMC.2023.3326676
Tan HYuan YYan HZhong SYang Y(2024)Human Preference-aware Rebalancing and Charging for Shared Electric Micromobility Vehicles2024 IEEE International Conference on Robotics and Automation (ICRA)10.1109/ICRA57147.2024.10610713(9608-9615)Online publication date: 13-May-2024
https://doi.org/10.1109/ICRA57147.2024.10610713
Zhan YHaddadi HMashhadi A(2023)Analysing Fairness of Privacy-Utility Mobility ModelsAdjunct Proceedings of the 2023 ACM International Joint Conference on Pervasive and Ubiquitous Computing & the 2023 ACM International Symposium on Wearable Computing10.1145/3594739.3610676(359-365)Online publication date: 8-Oct-2023
https://dl.acm.org/doi/10.1145/3594739.3610676
Tan HYuan YZhong SYang YFrommholz IHopfgartner FLee MOakes MLalmas MZhang MSantos R(2023)Joint Rebalancing and Charging for Shared Electric Micromobility Vehicles with Energy-informed DemandProceedings of the 32nd ACM International Conference on Information and Knowledge Management10.1145/3583780.3614942(2392-2401)Online publication date: 21-Oct-2023
https://dl.acm.org/doi/10.1145/3583780.3614942

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