Mitigating Spatial Disparity in Urban Prediction Using Residual-Aware Spatiotemporal Graph Neural Networks: A Chicago Case Study | Paper Citation Record

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This paper cites Adaptive graph convolutional recurrent network for traffic forecasting.

Mitigating Spatial Disparity in Urban Prediction Using Residual-Aware Spatiotemporal Graph Neural Networks: A Chicago Case Study Adaptive graph convolutional recurrent network for traffic forecasting

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This paper cites Model multiplicity: Opportunities, concerns, and solutions.

Mitigating Spatial Disparity in Urban Prediction Using Residual-Aware Spatiotemporal Graph Neural Networks: A Chicago Case Study Model multiplicity: Opportunities, concerns, and solutions

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This paper cites Fairness in Machine Learning: A Survey.

Mitigating Spatial Disparity in Urban Prediction Using Residual-Aware Spatiotemporal Graph Neural Networks: A Chicago Case Study Fairness in Machine Learning: A Survey

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This paper cites Battaglia, Vishal Gupta, Ang Li, Zhongwen Xu, Alvaro Sanchez-Gonzalez, Yujia Li, and Petar Velickovic.

Mitigating Spatial Disparity in Urban Prediction Using Residual-Aware Spatiotemporal Graph Neural Networks: A Chicago Case Study Battaglia, Vishal Gupta, Ang Li, Zhongwen Xu, Alvaro Sanchez-Gonzalez, Yujia Li, and Petar Velickovic

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This paper cites Edits: Modeling and mitigating data bias for graph neural networks.

Mitigating Spatial Disparity in Urban Prediction Using Residual-Aware Spatiotemporal Graph Neural Networks: A Chicago Case Study Edits: Modeling and mitigating data bias for graph neural networks

Reference 5

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This paper cites Fairness through awareness.

Mitigating Spatial Disparity in Urban Prediction Using Residual-Aware Spatiotemporal Graph Neural Networks: A Chicago Case Study Fairness through awareness

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Mitigating Spatial Disparity in Urban Prediction Using Residual-Aware Spatiotemporal Graph Neural Networks: A Chicago Case Study Unresolved cited work

Reference 7

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This paper cites Spatial-temporal graph ode networks for traffic flow forecasting.

Mitigating Spatial Disparity in Urban Prediction Using Residual-Aware Spatiotemporal Graph Neural Networks: A Chicago Case Study Spatial-temporal graph ode networks for traffic flow forecasting

Reference 9

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This paper cites A spatial–temporal graph deep learning model for urban flood nowcasting leveraging heterogeneous community features.

Mitigating Spatial Disparity in Urban Prediction Using Residual-Aware Spatiotemporal Graph Neural Networks: A Chicago Case Study A spatial–temporal graph deep learning model for urban flood nowcasting leveraging heterogeneous community features

Reference 10

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This paper cites The sociodemographic biases in machine learning algorithms: A biomedical informatics perspective.

Mitigating Spatial Disparity in Urban Prediction Using Residual-Aware Spatiotemporal Graph Neural Networks: A Chicago Case Study The sociodemographic biases in machine learning algorithms: A biomedical informatics perspective

Reference 11

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This paper cites Compacteta: A fast inference system for travel time prediction.

Mitigating Spatial Disparity in Urban Prediction Using Residual-Aware Spatiotemporal Graph Neural Networks: A Chicago Case Study Compacteta: A fast inference system for travel time prediction

Reference 12

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This paper cites Spatiotemporal Multi-Graph Convolution Network for Ride-Hailing Demand Forecasting.

Mitigating Spatial Disparity in Urban Prediction Using Residual-Aware Spatiotemporal Graph Neural Networks: A Chicago Case Study Spatiotemporal Multi-Graph Convolution Network for Ride-Hailing Demand Forecasting

Reference 13

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This paper cites The case for process fairness in learning: Feature selection for fair decision making.

Mitigating Spatial Disparity in Urban Prediction Using Residual-Aware Spatiotemporal Graph Neural Networks: A Chicago Case Study The case for process fairness in learning: Feature selection for fair decision making

Reference 14

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This paper cites Fairness-Enhancing Vehicle Rebalancing in the Ride-hailing System.

Mitigating Spatial Disparity in Urban Prediction Using Residual-Aware Spatiotemporal Graph Neural Networks: A Chicago Case Study Fairness-Enhancing Vehicle Rebalancing in the Ride-hailing System

Reference 15

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This paper cites Predicting station-level short-term passenger flow in a citywide metro network using spatiotemporal graph convolutional neural networks.

Mitigating Spatial Disparity in Urban Prediction Using Residual-Aware Spatiotemporal Graph Neural Networks: A Chicago Case Study Predicting station-level short-term passenger flow in a citywide metro network using spatiotemporal graph convolutional neural networks

Reference 16

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This paper cites Dueta: Traffic congestion propagation pattern modeling via efficient graph learning for eta prediction at baidu maps.

Mitigating Spatial Disparity in Urban Prediction Using Residual-Aware Spatiotemporal Graph Neural Networks: A Chicago Case Study Dueta: Traffic congestion propagation pattern modeling via efficient graph learning for eta prediction at baidu maps

Reference 17

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This paper cites Urban ride-hailing demand prediction with multiple spatio-temporal information fusion network.

Mitigating Spatial Disparity in Urban Prediction Using Residual-Aware Spatiotemporal Graph Neural Networks: A Chicago Case Study Urban ride-hailing demand prediction with multiple spatio-temporal information fusion network

Reference 18

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This paper cites Deep multi-view graph-based network for citywide ride-hailing demand prediction.

Mitigating Spatial Disparity in Urban Prediction Using Residual-Aware Spatiotemporal Graph Neural Networks: A Chicago Case Study Deep multi-view graph-based network for citywide ride-hailing demand prediction

Reference 19

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This paper cites Spatio-temporal graph neural networks for predictive learning in urban computing: A survey.

Mitigating Spatial Disparity in Urban Prediction Using Residual-Aware Spatiotemporal Graph Neural Networks: A Chicago Case Study Spatio-temporal graph neural networks for predictive learning in urban computing: A survey

Reference 20

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This paper cites Spatio-temporal graph neural point process for traffic congestion event prediction.

Mitigating Spatial Disparity in Urban Prediction Using Residual-Aware Spatiotemporal Graph Neural Networks: A Chicago Case Study Spatio-temporal graph neural point process for traffic congestion event prediction

Reference 21

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This paper cites Residual unfairness in fair machine learning from prejudiced data.

Mitigating Spatial Disparity in Urban Prediction Using Residual-Aware Spatiotemporal Graph Neural Networks: A Chicago Case Study Residual unfairness in fair machine learning from prejudiced data

Reference 22

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This paper cites Short-term forecasting of passenger demand under on-demand ride services: A spatio-temporal deep learning approach.

Mitigating Spatial Disparity in Urban Prediction Using Residual-Aware Spatiotemporal Graph Neural Networks: A Chicago Case Study Short-term forecasting of passenger demand under on-demand ride services: A spatio-temporal deep learning approach

Reference 23

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This paper cites STGAT: Spatial-Temporal Graph Attention Networks for Traffic Flow Forecasting.

Mitigating Spatial Disparity in Urban Prediction Using Residual-Aware Spatiotemporal Graph Neural Networks: A Chicago Case Study STGAT: Spatial-Temporal Graph Attention Networks for Traffic Flow Forecasting

Reference 24

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This paper cites Counterfactual fairness.

Mitigating Spatial Disparity in Urban Prediction Using Residual-Aware Spatiotemporal Graph Neural Networks: A Chicago Case Study Counterfactual fairness

Reference 25

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This paper cites Dstagnn: Dynamic spatial-temporal aware graph neural network for traffic flow forecasting.

Mitigating Spatial Disparity in Urban Prediction Using Residual-Aware Spatiotemporal Graph Neural Networks: A Chicago Case Study Dstagnn: Dynamic spatial-temporal aware graph neural network for traffic flow forecasting

Reference 26

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Observation 07e488b9-29a7-4f8e-80bf-71415381fdb7 · outbound

This paper cites Diffusion convolutional recurrent neural network: Data-driven traffic forecasting.

Mitigating Spatial Disparity in Urban Prediction Using Residual-Aware Spatiotemporal Graph Neural Networks: A Chicago Case Study Diffusion convolutional recurrent neural network: Data-driven traffic forecasting

Reference 27

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This paper cites Geoman: Multi-level attention networks for geo-sensory time series prediction.

Mitigating Spatial Disparity in Urban Prediction Using Residual-Aware Spatiotemporal Graph Neural Networks: A Chicago Case Study Geoman: Multi-level attention networks for geo-sensory time series prediction

Reference 28

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This paper cites Evaluating Transportation Equity.

Mitigating Spatial Disparity in Urban Prediction Using Residual-Aware Spatiotemporal Graph Neural Networks: A Chicago Case Study Evaluating Transportation Equity

Reference 29

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This paper cites Largest: A benchmark dataset for large-scale traffic forecasting.

Mitigating Spatial Disparity in Urban Prediction Using Residual-Aware Spatiotemporal Graph Neural Networks: A Chicago Case Study Largest: A benchmark dataset for large-scale traffic forecasting

Reference 30

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This paper cites Social graph transformer networks for pedestrian trajectory prediction in complex social scenarios.

Mitigating Spatial Disparity in Urban Prediction Using Residual-Aware Spatiotemporal Graph Neural Networks: A Chicago Case Study Social graph transformer networks for pedestrian trajectory prediction in complex social scenarios

Reference 31

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This paper cites Make more connections: Urban traffic flow forecasting with spatiotemporal adaptive gated graph convolution network.

Mitigating Spatial Disparity in Urban Prediction Using Residual-Aware Spatiotemporal Graph Neural Networks: A Chicago Case Study Make more connections: Urban traffic flow forecasting with spatiotemporal adaptive gated graph convolution network

Reference 32

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This paper cites A Survey on Bias and Fairness in Machine Learning.

Mitigating Spatial Disparity in Urban Prediction Using Residual-Aware Spatiotemporal Graph Neural Networks: A Chicago Case Study A Survey on Bias and Fairness in Machine Learning

Reference 33

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This paper cites Social-STGCNN: A Social Spatio-Temporal Graph Convolutional Neural Network for Human Trajectory Prediction.

Mitigating Spatial Disparity in Urban Prediction Using Residual-Aware Spatiotemporal Graph Neural Networks: A Chicago Case Study Social-STGCNN: A Social Spatio-Temporal Graph Convolutional Neural Network for Human Trajectory Prediction

Reference 34

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This paper cites Stirnet: A spatial-temporal interaction-aware recursive network for human trajectory prediction.

Mitigating Spatial Disparity in Urban Prediction Using Residual-Aware Spatiotemporal Graph Neural Networks: A Chicago Case Study Stirnet: A spatial-temporal interaction-aware recursive network for human trajectory prediction

Reference 35

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This paper cites A Review on Fairness in Machine Learning.

Mitigating Spatial Disparity in Urban Prediction Using Residual-Aware Spatiotemporal Graph Neural Networks: A Chicago Case Study A Review on Fairness in Machine Learning

Reference 36

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This paper cites D-stgcn: Dynamic pedestrian trajectory prediction using spatio-temporal graph convolutional networks.

Mitigating Spatial Disparity in Urban Prediction Using Residual-Aware Spatiotemporal Graph Neural Networks: A Chicago Case Study D-stgcn: Dynamic pedestrian trajectory prediction using spatio-temporal graph convolutional networks

Reference 37

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This paper cites Gummadi, Adish Singla, Adrian Weller, and Muhammad Bilal Zafar.

Mitigating Spatial Disparity in Urban Prediction Using Residual-Aware Spatiotemporal Graph Neural Networks: A Chicago Case Study Gummadi, Adish Singla, Adrian Weller, and Muhammad Bilal Zafar

Reference 38

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This paper cites Spatial-temporal attention network for crime prediction with adaptive graph learning.

Mitigating Spatial Disparity in Urban Prediction Using Residual-Aware Spatiotemporal Graph Neural Networks: A Chicago Case Study Spatial-temporal attention network for crime prediction with adaptive graph learning

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This paper cites Mfstgn: a multi-scale spatial-temporal fusion graph network for traffic prediction.

Mitigating Spatial Disparity in Urban Prediction Using Residual-Aware Spatiotemporal Graph Neural Networks: A Chicago Case Study Mfstgn: a multi-scale spatial-temporal fusion graph network for traffic prediction

Reference 40

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This paper cites Attention is all you need.

Mitigating Spatial Disparity in Urban Prediction Using Residual-Aware Spatiotemporal Graph Neural Networks: A Chicago Case Study Attention is all you need

Reference 41

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This paper cites Gsnet: Learning spatial-temporal correlations from geographical and semantic aspects for traffic accident risk forecasting.

Mitigating Spatial Disparity in Urban Prediction Using Residual-Aware Spatiotemporal Graph Neural Networks: A Chicago Case Study Gsnet: Learning spatial-temporal correlations from geographical and semantic aspects for traffic accident risk forecasting

Reference 42

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This paper cites Hagen: Homophily-aware graph convolutional recurrent network for crime forecasting.

Mitigating Spatial Disparity in Urban Prediction Using Residual-Aware Spatiotemporal Graph Neural Networks: A Chicago Case Study Hagen: Homophily-aware graph convolutional recurrent network for crime forecasting

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This paper cites Boosting urban prediction tasks with domain-sharing knowledge via meta-learning.

Mitigating Spatial Disparity in Urban Prediction Using Residual-Aware Spatiotemporal Graph Neural Networks: A Chicago Case Study Boosting urban prediction tasks with domain-sharing knowledge via meta-learning

Reference 44

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Mitigating Spatial Disparity in Urban Prediction Using Residual-Aware Spatiotemporal Graph Neural Networks: A Chicago Case Study Unresolved cited work

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Observation 32f538be-2fc0-48e4-a87e-913fbc078908 · outbound

This paper cites Origin-destination matrix prediction via graph convolution: a new perspective of passenger demand modeling.

Mitigating Spatial Disparity in Urban Prediction Using Residual-Aware Spatiotemporal Graph Neural Networks: A Chicago Case Study Origin-destination matrix prediction via graph convolution: a new perspective of passenger demand modeling

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This paper cites Gallat: A spatiotemporal graph attention network for passenger demand prediction.

Mitigating Spatial Disparity in Urban Prediction Using Residual-Aware Spatiotemporal Graph Neural Networks: A Chicago Case Study Gallat: A spatiotemporal graph attention network for passenger demand prediction

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This paper cites Event-Aware Multimodal Mobility Nowcasting.

Mitigating Spatial Disparity in Urban Prediction Using Residual-Aware Spatiotemporal Graph Neural Networks: A Chicago Case Study Event-Aware Multimodal Mobility Nowcasting

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This paper cites Inductive graph neural networks for spatiotemporal kriging.

Mitigating Spatial Disparity in Urban Prediction Using Residual-Aware Spatiotemporal Graph Neural Networks: A Chicago Case Study Inductive graph neural networks for spatiotemporal kriging

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Observation d3c8fa0a-a83f-4788-9f19-98793a67832f · outbound

This paper cites Spatial-temporal sequential hypergraph network for crime prediction with dynamic multiplex relation learning.

Mitigating Spatial Disparity in Urban Prediction Using Residual-Aware Spatiotemporal Graph Neural Networks: A Chicago Case Study Spatial-temporal sequential hypergraph network for crime prediction with dynamic multiplex relation learning

Reference 50

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Observation f01584bb-4bbc-40dc-b322-13b924cea0f9 · outbound

This paper cites Fairness- Aware Demand Prediction for New Mobility.

Mitigating Spatial Disparity in Urban Prediction Using Residual-Aware Spatiotemporal Graph Neural Networks: A Chicago Case Study Fairness- Aware Demand Prediction for New Mobility

Reference 51

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This paper cites Predicting traffic propagation flow in urban road network with multi-graph convolutional network.

Mitigating Spatial Disparity in Urban Prediction Using Residual-Aware Spatiotemporal Graph Neural Networks: A Chicago Case Study Predicting traffic propagation flow in urban road network with multi-graph convolutional network

Reference 52

Resolution: verified exact; doi, observed 2026-08-10T18:37:03.629406Z

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No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation be413ca6-40b4-4e36-bbf0-0ef4e1a345f8 · outbound

This paper cites How to build a graph-based deep learning architecture in traffic domain: A survey.

Mitigating Spatial Disparity in Urban Prediction Using Residual-Aware Spatiotemporal Graph Neural Networks: A Chicago Case Study How to build a graph-based deep learning architecture in traffic domain: A survey

Reference 53

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This paper cites Coupled layer-wise graph convolution for transportation demand prediction.

Mitigating Spatial Disparity in Urban Prediction Using Residual-Aware Spatiotemporal Graph Neural Networks: A Chicago Case Study Coupled layer-wise graph convolution for transportation demand prediction

Reference 54

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This paper cites Spatio-Temporal Graph Convolutional Networks : A Deep Learning Framework For Traffic Forecasting.

Mitigating Spatial Disparity in Urban Prediction Using Residual-Aware Spatiotemporal Graph Neural Networks: A Chicago Case Study Spatio-Temporal Graph Convolutional Networks : A Deep Learning Framework For Traffic Forecasting

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:37:05.495269Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation aa2d8e03-1a6f-4645-b74f-e26d66427a79 · outbound

This paper cites Deep spatio-temporal graph convolutional network for traffic accident prediction.

Mitigating Spatial Disparity in Urban Prediction Using Residual-Aware Spatiotemporal Graph Neural Networks: A Chicago Case Study Deep spatio-temporal graph convolutional network for traffic accident prediction

Reference 56

Resolution: verified exact; doi, observed 2026-08-10T18:37:03.602486Z

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No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 36d53cab-0dfc-410b-8092-1c387cb8cc5f · outbound

This paper cites Travel Demand Forecasting: A Fair AI Approach.

Mitigating Spatial Disparity in Urban Prediction Using Residual-Aware Spatiotemporal Graph Neural Networks: A Chicago Case Study Travel Demand Forecasting: A Fair AI Approach

Reference 58

Resolution: verified exact; local_arxiv, observed 2026-08-10T18:37:04.236470Z

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Observation 10076c10-c5b5-4ba9-93ba-8a9d7f211c60 · outbound

This paper cites Graph deep learning model for network-based predictive hotspot mapping of sparse spatio-temporal events.

Mitigating Spatial Disparity in Urban Prediction Using Residual-Aware Spatiotemporal Graph Neural Networks: A Chicago Case Study Graph deep learning model for network-based predictive hotspot mapping of sparse spatio-temporal events

Reference 59

Resolution: metadata mismatch; raw_fallback, observed 2026-08-10T18:37:04.215845Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 8f8b3467-4876-4048-b976-3f8f959db679 · outbound

This paper cites Equality of opportunity in travel behavior prediction with deep neural networks and discrete choice models.

Mitigating Spatial Disparity in Urban Prediction Using Residual-Aware Spatiotemporal Graph Neural Networks: A Chicago Case Study Equality of opportunity in travel behavior prediction with deep neural networks and discrete choice models

Reference 60

Resolution: unresolved; no resolver link, observed 2026-08-10T18:37:03.520307Z

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Observation 14d199e9-daaa-48c2-b412-a4a6f4aa9378 · outbound

This paper cites Fairness-enhancing deep learning for ride-hailing demand prediction.

Mitigating Spatial Disparity in Urban Prediction Using Residual-Aware Spatiotemporal Graph Neural Networks: A Chicago Case Study Fairness-enhancing deep learning for ride-hailing demand prediction

Reference 62

Resolution: verified exact; local_arxiv, observed 2026-08-10T18:37:04.048848Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation bb78b666-b1e1-411b-8068-c7b683bfd2d9 · outbound

This paper cites Forecasting pm2.5 using hybrid graph convolution-based model considering dynamic wind-field to offer the benefit of spatial interpretability.

Mitigating Spatial Disparity in Urban Prediction Using Residual-Aware Spatiotemporal Graph Neural Networks: A Chicago Case Study Forecasting pm2.5 using hybrid graph convolution-based model considering dynamic wind-field to offer the benefit of spatial interpretability

Reference 63

Resolution: metadata mismatch; raw_fallback, observed 2026-08-10T18:37:04.027211Z

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Observation 4c6d009f-631a-4671-96ca-28c22521dd41 · outbound

This paper cites Signet: A siamese graph convolutional network for multi-class urban change detection.

Mitigating Spatial Disparity in Urban Prediction Using Residual-Aware Spatiotemporal Graph Neural Networks: A Chicago Case Study Signet: A siamese graph convolutional network for multi-class urban change detection

Reference 64

Resolution: verified exact; doi, observed 2026-08-10T18:37:03.586375Z

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No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation a49e66d3-1343-4bd3-bc45-ea5311cb5a2a · outbound

This paper cites Uncertainty quantification of sparse travel demand prediction with spatial-temporal graph neural networks.

Mitigating Spatial Disparity in Urban Prediction Using Residual-Aware Spatiotemporal Graph Neural Networks: A Chicago Case Study Uncertainty quantification of sparse travel demand prediction with spatial-temporal graph neural networks

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:37:05.478653Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation d6c8e930-d0d8-4182-be7a-4144df8d8b91 · outbound

This paper cites Advancing Transportation Mode Share Analysis with Built Environment: Deep Hybrid Models with Urban Road Network.

Mitigating Spatial Disparity in Urban Prediction Using Residual-Aware Spatiotemporal Graph Neural Networks: A Chicago Case Study Advancing Transportation Mode Share Analysis with Built Environment: Deep Hybrid Models with Urban Road Network

Reference 66

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