poster

FairST: Equitable Spatial and Temporal Demand Prediction for New Mobility Systems

Authors:

SIGSPATIAL '19: Proceedings of the 27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems

Pages 552 - 555

https://doi.org/10.1145/3347146.3359380

Published: 05 November 2019 Publication History

Get Access

Abstract

We present a fairness-aware model for predicting demand for new mobility systems. Our approach, called FairST, consists of 1D, 2D and 3D convolutions to learn the spatial-temporal dynamics of a mobility system, and fairness regularizers that guide the model to make equitable predictions. We propose two fairness metrics, region-based fairness gap (RFG) and individual-based fairness gap (IFG), that measure equity gaps between social groups for new mobility systems. Experimental results on two real-world datasets demonstrate the effectiveness of the proposed model: FairST not only reduces the fairness gap by more than 80%, but achieves better accuracy than state-of-the-art but fairness-oblivious methods including LSTMs, ConvLSTMs, and 3D CNN.

References

[1]

Franziska Bell and Slawek Smyl. 2018. Forecasting at Uber: An Introduction. Uber Engineering (2018). https://eng.uber.com/forecasting-introduction/

Google Scholar

[2]

Richard Berk, Hoda Heidari, Shahin Jabbari, Matthew Joseph, Michael J. Kearns, Jamie Morgenstern, Seth Neel, and Aaron Roth. 2017. A Convex Framework for Fair Regression. CoRR abs/1706.02409 (2017).

Google Scholar

[3]

Toon Calders, Asim Karim, Faisal Kamiran, Wasif Ali, and Xiangliang Zhang. 2013. Controlling attribute effect in linear regression. In ICDM. IEEE, 71--80.

Google Scholar

[4]

Alexa Delbosc and Graham Currie. 2011. Using Lorenz curves to assess public transport equity. Journal of Transport Geography 19, 6 (2011), 1252--1259.

Crossref

Google Scholar

[5]

Cynthia Dwork, Moritz Hardt, Toniann Pitassi, Omer Reingold, and Richard Zemel. 2012. Fairness Through Awareness. In Proceedings of the 3rd Innovations in Theoretical Computer Science Conference (ITCS '12). 214--226.

Digital Library

Google Scholar

[6]

Wafic El-Assi, Mohamed Salah Mahmoud, and Khandker Nurul Habib. 2017. Effects of built environment and weather on bike sharing demand: a station level analysis of commercial bike sharing in Toronto. Transportation 44, 3 (2017), 589--613.

Crossref

Google Scholar

[7]

Yanbo Ge, Christopher R Knittel, Don MacKenzie, and Stephen Zoepf. 2016. Racial and gender discrimination in transportation network companies. Technical Report. National Bureau of Economic Research.

Google Scholar

[8]

Yexin Li, Yu Zheng, and Qiang Yang. [n. d.]. Dynamic Bike Reposition: A Spatio-Temporal Reinforcement Learning Approach. In KDD '18.

Google Scholar

[9]

Stephen J Mooney, Kate Hosford, Bill Howe, An Yan, Meghan Winters, Alon Bassok, and Jana A Hirsch. 2019. Freedom from the station: Spatial equity in access to dockless bike share. Journal of Transport Geography 74 (2019), 91--96.

Crossref

Google Scholar

[10]

Du Tran, Lubomir Bourdev, Rob Fergus, Lorenzo Torresani, and Manohar Paluri. [n. d.]. Learning Spatiotemporal Features with 3D Convolutional Networks. In ICCV '15.

Google Scholar

[11]

Shi Xingjian, Zhourong Chen, Hao Wang, Dit-Yan Yeung, Wai-Kin Wong, and Wang-chun Woo. 2015. Convolutional LSTM network: A machine learning approach for precipitation nowcasting. In Advances in neural information processing systems. 802--810.

Google Scholar

Cited By

View all

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
Belal YBen Mokhtar SHaddadi HWang JMashhadi A(2024)Survey of Federated Learning Models for Spatial-Temporal Mobility ApplicationsACM Transactions on Spatial Algorithms and Systems10.1145/366608910:3(1-39)Online publication date: 1-Jun-2024
https://dl.acm.org/doi/10.1145/3666089
He EXie YChen WSkakun SBao HGhosh RRavirathinam PJia X(2024)Learning With Location-Based Fairness: A Statistically-Robust Framework and AccelerationIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2024.337146036:9(4750-4765)Online publication date: Sep-2024
https://doi.org/10.1109/TKDE.2024.3371460
Show More Cited By

Index Terms

FairST: Equitable Spatial and Temporal Demand Prediction for New Mobility Systems
1. Applied computing
  1. Operations research
    1. Transportation
2. Information systems
  1. Information systems applications
    1. Decision support systems
      1. Data analytics
    2. Spatial-temporal systems

Recommendations

Fairer Machine Learning Through Multi-objective Evolutionary Learning
Artificial Neural Networks and Machine Learning – ICANN 2021
Abstract
Dilemma between model accuracy and fairness in machine learning models has been shown theoretically and empirically. So far, dozens of fairness measures have been proposed, among which incompatibility and complementarity exist. However, no ...
Spatial-Temporal Semantic Generative Adversarial Networks for Flexible Multi-step Urban Flow Prediction
Artificial Neural Networks and Machine Learning – ICANN 2022
Abstract
Accurate multi-step citywide urban flow prediction plays a critical role in traffic management and future smart city. However, it is very challenging since urban flow is affected by complex semantic factors and has multi-scale dependencies on both ...
Towards dropout training for convolutional neural networks

Recently, dropout has seen increasing use in deep learning. For deep convolutional neural networks, dropout is known to work well in fully-connected layers. However, its effect in convolutional and pooling layers is still not clear. This paper ...

Comments

Information & Contributors

Information

Published In

SIGSPATIAL '19: Proceedings of the 27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems

November 2019

648 pages

ISBN:9781450369091

DOI:10.1145/3347146

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 05 November 2019

Check for updates

Author Tags

Qualifiers

Poster
Research
Refereed limited

Conference

SIGSPATIAL '19

Sponsor:

SIGSPATIAL

SIGSPATIAL '19: 27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems

November 5 - 8, 2019

IL, Chicago, USA

Acceptance Rates

SIGSPATIAL '19 Paper Acceptance Rate 34 of 161 submissions, 21%;

Overall Acceptance Rate 257 of 1,238 submissions, 21%

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

15
Total Citations
View Citations
367
Total Downloads

Downloads (Last 12 months)44
Downloads (Last 6 weeks)4

Reflects downloads up to 25 Feb 2025

Other Metrics

View Author Metrics

Citations

Cited By

View all

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
Belal YBen Mokhtar SHaddadi HWang JMashhadi A(2024)Survey of Federated Learning Models for Spatial-Temporal Mobility ApplicationsACM Transactions on Spatial Algorithms and Systems10.1145/366608910:3(1-39)Online publication date: 1-Jun-2024
https://dl.acm.org/doi/10.1145/3666089
He EXie YChen WSkakun SBao HGhosh RRavirathinam PJia X(2024)Learning With Location-Based Fairness: A Statistically-Robust Framework and AccelerationIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2024.337146036:9(4750-4765)Online publication date: Sep-2024
https://doi.org/10.1109/TKDE.2024.3371460
Villa-Zapata LRodriguez-Roman DFlórez-Coronel JGonzález-López JFigueroa-Medina A(2024)Incorporating equity in the vehicle rebalancing operations of dockless micromobility servicesLatin American Transport Studies10.1016/j.latran.2024.1000092(100009)Online publication date: Dec-2024
https://doi.org/10.1016/j.latran.2024.100009
He EWan YLetcher BFair JXie YJia XElkind E(2023)CGSProceedings of the Thirty-Second International Joint Conference on Artificial Intelligence10.24963/ijcai.2023/664(5986-5994)Online publication date: 19-Aug-2023
https://dl.acm.org/doi/10.24963/ijcai.2023/664
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
Jagadish HStoyanovich JHowe B(2023)The Many Facets of Data EquityJournal of Data and Information Quality10.1145/353342514:4(1-21)Online publication date: 7-Feb-2023
https://dl.acm.org/doi/10.1145/3533425
Huang HHe STabatabaie M(2023)Extreme-Aware Local-Global Attention for Spatio-Temporal Urban Mobility Learning2023 IEEE 39th International Conference on Data Engineering (ICDE)10.1109/ICDE55515.2023.00086(1059-1070)Online publication date: Apr-2023
https://doi.org/10.1109/ICDE55515.2023.00086
Pappalardo LManley ESekara VAlessandretti L(2023)Future directions in human mobility scienceNature Computational Science10.1038/s43588-023-00469-43:7(588-600)Online publication date: 3-Jul-2023
https://doi.org/10.1038/s43588-023-00469-4
He EXie YJia XChen WBao HZhou XJiang ZGhosh RRavirathinam PRenz MSarwat M(2022)Sailing in the location-based fairness-bias sphereProceedings of the 30th International Conference on Advances in Geographic Information Systems10.1145/3557915.3560976(1-10)Online publication date: 1-Nov-2022
https://dl.acm.org/doi/10.1145/3557915.3560976
Show More Cited By

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Abstract

References

Cited By

Index Terms

Recommendations

Fairer Machine Learning Through Multi-objective Evolutionary Learning

Spatial-Temporal Semantic Generative Adversarial Networks for Flexible Multi-step Urban Flow Prediction

Towards dropout training for convolutional neural networks

Comments

Information

Published In

Sponsors

Publisher

Publication History

Check for updates

Author Tags

Qualifiers

Conference

Acceptance Rates

Contributors

Other Metrics

Bibliometrics

Article Metrics

Other Metrics

Citations

Cited By

Login options

Full Access

View options

PDF

eReader

Share

Share this Publication link

Share on social media

Affiliations