Mitigating Spatial Disparity in Urban Prediction Using Residual-Aware Spatiotemporal Graph Neural Networks: A Chicago Case Study
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3701716.3717368.pdf
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5.41 MB
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Checksum (MD5)
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Author(s)
Zhuang, Dingyi
Xu, Hanyong
Guo, Xiaotong
Zheng, Yunhan
Wang, Shenhao
Zhao, Jinhua
Date Issued
May 23, 2025
Publisher
ACM|Companion Proceedings of the ACM Web Conference 2025
Citation
Dingyi Zhuang, Hanyong Xu, Xiaotong Guo, Yunhan Zheng, Shenhao Wang, and Jinhua Zhao. 2025. Mitigating Spatial Disparity in Urban Prediction Using Residual-Aware Spatiotemporal Graph Neural Networks: A Chicago Case Study. In Companion Proceedings of the ACM on Web Conference 2025 (WWW '25). Association for Computing Machinery, New York, NY, USA, 2351–2360.
Version
Final published version
Abstract
Urban prediction tasks, such as forecasting traffic flow, temperature, and crime rates, are crucial for efficient urban planning and management. However, existing Spatiotemporal Graph Neural Networks (ST-GNNs) often rely solely on accuracy, overlooking spatial and demographic disparities in their predictions. This oversight can lead to imbalanced resource allocation and exacerbate existing inequities in urban areas. This study introduces a Residual-Aware Attention (RAA) Block and an equality-enhancing loss function to address these disparities. By adapting the adjacency matrix during training and incorporating spatial disparity metrics, our approach aims to reduce local segregation of residuals and errors. We applied our methodology to urban prediction tasks in Chicago, utilizing travel demand datasets as an example. Our model achieved a 48% significant improvement in fairness metrics with only a 9% increase in error metrics. Spatial analysis of residual distributions revealed that models with RAA Blocks produced more equitable prediction results, particularly by reducing errors clustered in central regions, supporting more balanced and equitable urban planning and policy-making.
Description
WWW Companion ’25, Sydney, NSW, Australia
MIT Department
Massachusetts Institute of Technology. Department of Civil and Environmental Engineering
Massachusetts Institute of Technology. Department of Urban Studies and Planning
Singapore-MIT Alliance in Research and Technology (SMART)
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Creative Commons Attribution
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DOI of Published Version
https://doi.org/10.1145/3701716.3717368
