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

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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
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Lionel Médini
Université Lyon 1, France

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Published: 25 April 2022

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Cited By

Yang XHe SShin KTabatabaie MDai J(2025)Cross-Modality and Equity-Aware Graph Pooling Fusion: A Bike Mobility Prediction StudyIEEE Transactions on Big Data10.1109/TBDATA.2024.341428011:1(286-302)Online publication date: Feb-2025
https://doi.org/10.1109/TBDATA.2024.3414280
Tabatabaie MHe SWang HShin K(2024)Beyond "Taming Electric Scooters": Disentangling Understandings of Micromobility Naturalistic RidingProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36785138:3(1-24)Online publication date: 9-Sep-2024
https://dl.acm.org/doi/10.1145/3678513
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: 1-Jun-2024
https://dl.acm.org/doi/10.1109/TMC.2023.3326676
Zhang XKe QZhao X(2024)Travel Demand Forecasting: A Fair AI ApproachIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2024.339506125:10(14611-14627)Online publication date: 1-Oct-2024
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