Fairness-Enhancing Deep Learning for Ride-Hailing Demand Prediction
Name
Fairness-Enhancing_Deep_Learning_for_Ride-Hailing_Demand_Prediction.pdf
Size
9.59 MB
Format
Adobe PDF
Checksum (MD5)
ba771c7e8423ab322e0d9b59b119be91
Author(s)
Zheng, Yunhan
Wang, Qingyi
Zhuang, Dingyi
Wang, Shenhao
Zhao, Jinhua
Date Issued
2023
Publisher
Institute of Electrical and Electronics Engineers
Citation
Y. Zheng, Q. Wang, D. Zhuang, S. Wang and J. Zhao, "Fairness-Enhancing Deep Learning for Ride-Hailing Demand Prediction," in IEEE Open Journal of Intelligent Transportation Systems, vol. 4, pp. 551-569, 2023.
Version
Final published version
Abstract
Short-term demand forecasting for on-demand ride-hailing services is a fundamental issue in intelligent transportation systems. However, previous research predominantly focused on improving prediction accuracy, ignoring fairness issues such as systematic underestimations of travel demand in disadvantaged neighborhoods. This study investigates how to measure, evaluate, and enhance prediction fairness between disadvantaged and privileged communities in spatial-temporal demand forecasting of ride-hailing services. We developed a socially-aware neural network (SA-Net) that integrates socio-demographics and ridership information for fair demand prediction, and introduced a bias-mitigation regularization to reduce the prediction error gap between black and non-black, and low-income and high-income communities. The experimental results, using Chicago Transportation Network Company (TNC) data, demonstrate that our de-biasing SA-Net model outperforms other models in both prediction accuracy and fairness. Notably, the SA-Net exhibits a significant improvement in prediction accuracy, reducing 2.3% in Mean Absolute Error (MAE) compared to state-of-the-art models. When coupled with the bias-mitigation regularization, the de-biasing SA-Net effectively bridges the mean percentage prediction error (MPE) gap between the disadvantaged and privileged groups, and protects the disadvantaged regions against systematic underestimation of TNC demand. Specifically, our approach reduces the MPE gap between black and non-black communities by 67% without compromising overall prediction accuracy.
Subjects
Computer Science Applications
Mechanical Engineering
Automotive Engineering
MIT Department
Massachusetts Institute of Technology. Department of Civil and Environmental Engineering
Massachusetts Institute of Technology. Department of Urban Studies and Planning
Terms of Use
Creative Commons Attribution
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1109/ojits.2023.3297517
