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

T0 review · 5 major / 5 minor · reviewed 2026-08-10 · deepseek-v4-flash

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Pith's one-line read This paper claims that adding a Residual-Aware Attention block and an equality-enhancing loss to spatiotemporal graph neural networks reduces spatial and demographic disparities in urban predictions, with a 48% fairness improvement at a…

desk verdict The residual-aware attention idea is worth taking seriously, but the current empirical evidence is too under-specified to support the headline fairness claims. read the letter →

arxiv 2501.11214 v1 pith:JNVOBFDC submitted 2025-01-20 cs.LG

Dingyi Zhuang , Hanyong Xu , Xiaotong Guo , Yunhan Zheng , Shenhao Wang , Jinhua Zhao This is my paper · ORCID
classification cs.LG
keywords spatiotemporalgraphneuralnetworksurbanpredictionalgorithmicfairnessspatialdisparityresidual-awareattentionequality-enhancinglossMoran'sIGeneralizedEntropyIndex
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

Urban prediction models built on spatiotemporal graph neural networks typically optimize accuracy alone, and the paper argues that this can entrench spatial and demographic inequities: residuals are not randomly scattered but cluster in particular neighborhoods. The paper claims that two add-ons—a Residual-Aware Attention (RAA) block that rewires the graph's adjacency matrix during training and an equality-enhancing loss that penalizes sign-aware residual variance and spatial clustering—reduce these disparities without ever using protected demographic attributes. In a Chicago travel-demand case study across four ST-GNN base models, the average fairness metrics improved by 48 percent while error metrics rose only about 9 percent, with average decreases of 18 percent in the Generalized Entropy Index, 47 percent in the Scaled Disparity Index, and 80 percent in Moran's I. If correct, this gives urban agencies a way to make prediction-based resource allocation more balanced while preserving most of the accuracy gains.

What carries the argument

The machinery is the Residual-Aware Attention (RAA) block coupled with a multi-term loss. The RAA block converts the residual vector into Q, K, V through tanh-activated linear layers, forms attention scores S = QK^T/$\sqrt$(|K|), softmax-normalizes them into H, and computes an adapted adjacency matrix A_adapted = A ⊙ H, which is reused in the next training epoch so that message passing downweights neighboring regions with similar residual patterns. The joint loss is L = L_MSE + λ_s D_s + λ_d D_d, where D_s is the sign-aware residual variance of spatially weighted positive and negative residuals and D_d is either Moran's I (shifted to be non-negative) or the Generalized Entropy Index; default regularization weights are 0.05 and tunable. This design is what lets the model reduce spatial disparity without demographic features.

What would settle it

Re-run the same four base models and RAA variants on Chicago ride-hail data using a strict temporal held-out split (for example, train on September through November and evaluate on December), and recompute GEI, SDI, Moran's I, MAE, and SMAPE on that test period; if the fairness gains shrink to near zero or the error increase grows well beyond 9%, the central claim is not supported.

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Extended reading notes

Core claim

The paper's central claim is that the source of demographic unfairness in ST-GNN predictions is partly spatial: message passing over a fixed geographic adjacency matrix makes residuals locally segregated, and in a city like Chicago, where racial groups are geographically separated, that spatial segregation lines up with demographic groups. The authors therefore propose to attack disparity at the message-passing level. A Residual-Aware Attention block takes the current training residuals, projects them into query, key, and value vectors, and uses softmax attention to form a mask that is multiplied element-wise into the adjacency matrix, so the graph used in the next training epoch de-emphasizes edges along which residuals are similar. An equality-enhancing loss adds to the mean squared error two regularizers: D_s, the sign-aware variance of spatially aggregated positive and negative residuals, and D_d, taken as Moran's I or the Generalized Entropy Index of residuals. On four ST-GNN architectures applied to Chicago ride-hail demand, the authors report that this combination reduces residual clustering and residual-demographic correlation, cuts GEI by an average of 18%, SDI by 47%, and Moran's I by 80%, with a 7% average increase in MAE and 12% in SMAPE.

Load-bearing premise

The load-bearing premise is that the reported fairness and accuracy numbers are measured on data the model did not train on; if they come from the training set instead, the fairness improvements could be partly the model minimizing the very quantities the loss penalizes.

Editorial extensions

If this is right

  • Across the four ST-GNN base models, 10 of 12 RAA variants improved GEI, all 12 improved SDI, and the average Moran's I dropped by 80%, so the disparity reduction generalizes across architectures, though its size varies by model.
  • The best fairness gains came from the RAA block plus the GEI regularizer, with DCRNN and AGCRN showing the most consistent improvements across all variants.
  • Reducing spatial residual clustering also reduced demographic disparity, measured by correlation of residuals with minority and majority population shares, so spatial regularization can substitute for demographic data when protected attributes are unavailable.
  • The accuracy-fairness trade-off is mild on average (about 7% MAE and 12% SMAPE increase), and in some configurations both accuracy and fairness improve, which the authors attribute to model multiplicity or underspecification.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • Editorial inference: the headline 48% fairness gain should be read as conditional on the evaluation protocol; the paper does not state whether the metrics come from a held-out test set or the training data, and if they come from training data, part of the GEI and Moran's I reductions is the direct effect of minimizing those same terms in the loss.
  • Editorial inference: because the method only touches residuals and the adjacency matrix, it should transfer to other urban prediction targets such as crime, pollution, or temperature, but the demographic fairness payoff depends on the extent to which spatial segregation tracks the protected groups of interest.
  • Editorial inference: a testable prediction of the mechanism is that the learned attention weights should downweight edges connecting regions whose residuals have the same sign; inspecting the adapted adjacency matrix against residual sign maps would let a reader verify this directly.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

5 major / 5 minor

Summary. The paper proposes a Residual-Aware Attention (RAA) block and an equality-enhancing loss to reduce spatial and demographic disparities in ST-GNN urban prediction, and evaluates the approach on Chicago travel demand with four base ST-GNN models. It reports average fairness-metric decreases of 18% in GEI, 47% in SDI, and 80% in Moran's I at a modest accuracy cost, summarized in the abstract as a 48% significant improvement in fairness metrics with only a 9% increase in error metrics. The central empirical claim is not supported by the reported evidence because the evaluation protocol is unspecified, the fairness metrics reported in Table 1 are partly the same terms minimized in Equation (8), and the same model configurations take contradictory values across Tables 1 and 2.

Significance. If substantiated, the approach would be valuable: it targets prediction disparities without using protected attributes, it adapts the graph adjacency matrix from residuals rather than from demographic labels, and it offers a regularization framework that could transfer to other urban prediction tasks. The paper also provides spatial residual maps and attention visualizations that help interpret the mechanism. However, the significance is contingent on a clean empirical evaluation, which the manuscript does not provide. The missing held-out split, the direct optimization of the reported fairness metrics, the contradictory table entries, and the absence of uncertainty quantification mean the headline claim cannot currently be verified as a generalization result.

major comments (5)
  1. [§5.5, §5.6.1, Eq. (8)] The paper never states whether the metrics in Table 1 are computed on a held-out test set, a validation set, or the training set. This is load-bearing because Equation (8) includes D_s and D_d as loss terms, and Section 4.2 says D_d is instantiated as Moran's I or GEI, exactly the metrics reported as outcomes in Table 1. If Table 1 is computed on the training residuals used to fit the RAA block and the loss, then the reported GEI and Moran's I decreases are in part a direct consequence of minimizing those same objectives, making the '48% significant improvement' claim circular. The authors must specify the data split and should report metrics on both training and held-out test sets to demonstrate generalization.
  2. [Tables 1 and 2] The same STGCN configurations have contradictory values across the overall-performance table and the ablation table. For 'RAA block + Ds', Table 1 gives MAE 7.329, SMAPE 0.538, and GEI 1.075, whereas Table 2 (Model 3) gives MAE 7.257, SMAPE 0.474, and GEI 1.854. For 'RAA block + Moran's I', Table 1 gives MAE 7.885 and GEI 1.692, while Table 2 (Model 4) gives MAE 10.465 and GEI 0.842. These discrepancies suggest different evaluation sets, runs, or seeds, and they undermine the reliability of the empirical claims in Sections 5.6.1 and 5.6.3. The authors need to reconcile these numbers and report the exact evaluation setup.
  3. [§5.6.1, abstract] The claims of 'significant improvement' and the aggregated 48% figure are not supported by any confidence intervals, standard deviations, or significance tests. Additionally, percentage changes are not meaningful for Moran's I when the baseline is near zero or the value changes sign; for example, DCRNN Moran's I from 0.182 to -0.135 is reported as a -174% change. Fairness-metric improvements should be accompanied by error bars and hypothesis tests over multiple seeds.
  4. [§4.2, Eq. (8) vs §3.2 Eq. (3)] The equality-enhancing loss is ambiguously specified: Equation (3) defines D_d as |Corr(r, Pop_minor)| + |Corr(r, Pop_major)|, which requires demographic data, but Section 4.2 states that D_d 'utilizes fairness metrics like Moran's I or GEI.' Since the paper emphasizes fairness through unawareness, the actual form of D_d used in the experiments must be clarified, and the demographic-correlation version should be reconciled with the Moran's I/GEI version.
  5. [§5.6.1, Table 1] The description of the results is inaccurate: the text says 'among all 12 RAA experiments, 10 of them have reduced metrics for each fairness metric,' but Table 1 shows DSTAGNN with the Moran's I loss has GEI increase from 1.383 to 2.862 and Moran's I increase from 0.542 to 0.558, and STGCN with Moran's I loss has GEI increase from 1.506 to 1.692. The sentence about error metrics, 'all of them have at least one fairness metric improving while having error metrics decreasing,' also appears to be a typo, since many variants show increased MAE and SMAPE. These statements need correction.
minor comments (5)
  1. [§5.6.3 heading] The heading 'Abaltion Study' should be corrected to 'Ablation Study.'
  2. [§5.5] The experiment setup gives no hyperparameters, number of runs, random seeds, or early-stopping criteria; these details are necessary for reproducibility.
  3. [Table 1] Table 1 lists an AGCRN variant 'RAA block only,' although Section 5.6.1 says only three variants are compared (RAA + Ds, RAA + Moran's I, RAA + GEI); this discrepancy should be explained.
  4. [§4.1, Eq. (7)] The adapted adjacency matrix A_adapted = A ⊙ H lacks a normalization step, which is important for stable GCN training; the authors should clarify or reference a standard normalization.
  5. [§5.6.1] The statement that 'the average percentage increase for MAE and SMAPE are 7% and 12%' does not match the abstract's '9% increase' unless the two are averaged; the relationship should be stated explicitly.

Circularity Check

2 steps flagged · score 6.0 of 10

The headline fairness gains may be training-set artifacts: Eq. 8 directly optimizes Moran's I and GEI, and Table 1 reports the same metrics as improvements, without a stated held-out split.

  1. fitted input called prediction [Section 4.2, Eq. 8; Section 5.3; Section 5.6.1, Table 1]
    "The overall loss function is: Ljoint = Lprediction + λsDs + λdDd, (8) ... The Dd term utilizes fairness metrics like Moran's I or GEI to measure spatial clustering or information redundancy ... We selected GEI, SDI, and Moran's I to evaluate three aspects of the residual distribution: entropy, correlation to the demographics features, and spatial clusteringness."

    Equation 8 trains the model to minimize λdDd, where Dd is defined as Moran's I or GEI. Section 5.3 then defines the evaluation metrics GEI and Moran's I, and Table 1 reports improvements in exactly those metrics (average decreases of 18% in GEI and 80% in Moran's I). If Table 1 is computed on the training residuals used to fit Eq. 8, then these reported fairness improvements are partly the direct effect of optimizing the same quantities; the 'prediction' of lower GEI and Moran's I is the training objective itself, not independent evidence of fairness. The paper never states in Section 5.5 whether the reported numbers come from a held-out test set, so this reduction cannot be excluded.

  2. self definitional [Section 5.6.3, Ablation Study, Table 2]
    "Lastly, adding Ds and Dd terms improves the corresponding fairness metrics. Comparing between Model 2 and Model 5, as well as Model 3 and Model 7, we see that introducing GEI in the loss function as a regularization term significantly enhances the GEI performance of model predictions."

    This ablation conclusion evaluates the very metric that was added to the loss: Models 5 and 7 include GEI as the Dd regularizer, and the reported GEI is then used as evidence of improvement. On the training residuals, this is true by construction, because the model is trained to minimize GEI. The claim as stated is also internally inconsistent—Model 2 has GEI 0.402 while Model 5 has GEI 0.525, which contradicts the assertion—so it does not provide independent support for the method's effectiveness.

full rationale

The central empirical claim is that the RAA architecture and equality-enhancing loss improve fairness by 48% with only a 9% error increase. The paper's own equations make GEI and Moran's I part of the training objective: Eq. 8 adds λdDd to the MSE loss, and Section 4.2 explicitly says Dd uses Moran's I or GEI. Section 5.3 then defines the evaluation metrics GEI and Moran's I with the same conceptual content, and Table 1 reports decreases in those exact metrics. Optimizing a quantity on training residuals and then reporting a decrease in that quantity is not independent evidence of fairness unless the evaluation is on a held-out set; the paper does not state that split in Section 5.5. The SDI metric is not in the loss, and four base models are compared, which are independent elements, so the circularity is partial rather than total. There is no load-bearing self-citation chain or imported uniqueness theorem. The ambiguity is reinforced by the fact that the same STGCN variant appears with different values in Table 1 and Table 2 (e.g., RAA block + Ds: MAE 7.329 vs 7.257), suggesting the tables may mix different evaluation protocols. Overall, the headline fairness improvement partly reduces to the training objective, giving a partial-circularity score of 6.

Assumptions & free parameters 4 free parameters · 3 assumptions · 1 invented entities

The method as described depends on several hand-chosen weights and on the assumption that spatial error clustering tracks demographic disadvantage. The loss function optimizes the same spatial metrics (GEI, Moran's I) that are later used to report success, so the empirical contribution is partly determined by the chosen objective.

free parameters (4)
  • lambda_s = 0.05 (default, tunable)
    Regularization weight for the sign-aware residual variance term D_s in Equation 8. Chosen by hand, not reported as tuned for each dataset.
  • lambda_d = 0.05 (default, tunable)
    Regularization weight for D_d (Moran's I or GEI) in Equation 8. Chosen by hand, not reported.
  • GEI alpha = 2
    Parameter controlling emphasis on larger residuals in Equation 9. Set to 2 without sensitivity analysis.
  • GEI residual shift m = not specified (chosen such that residuals non-negative)
    Requires a data-dependent shift; the paper does not give the value or how it varies across runs.
assumptions (3)
  • domain assumption Prediction residuals are a valid indicator of algorithmic unfairness in urban prediction.
    Motivated by Kallus and Zhou (2018) and the correlation in Figure 1; no formal justification or external validation is provided.
  • domain assumption Spatial clustering of residuals is a reliable proxy for demographic disparity.
    The method aims to reduce demographic disparity by reducing spatial disparity; the paper relies on Chicago's residential segregation (Figure 1b) but does not validate this proxy over time, tasks, or cities.
  • ad hoc to paper The residual-driven attention weights from training generalize to the target data.
    The RAA block adapts the adjacency matrix from training residuals, and the paper assumes this adaptation remains valid at inference and for unseen data. No analysis of convergence or generalization is provided.
invented entities (1)
  • Scaled Disparity Index (SDI)
    purpose: A new metric to quantify demographic disparity from the Pearson correlations between residuals and minority/majority population shares.
    The SDI is introduced in this paper and used as an evaluation metric, but its properties, interpretation, and validity relative to established fairness metrics are not examined.

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Pith. "Pith review of Mitigating Spatial Disparity in Urban Prediction Using Residual-Aware Spatiotemporal Graph Neural Networks: A Chicago Case Study." pith.science (2026). https://pith.science/paper/JNVOBFDC

@misc{pith2026250111214,
  author       = {Pith},
  title        = {Pith review of: Mitigating Spatial Disparity in Urban Prediction Using Residual-Aware Spatiotemporal Graph Neural Networks: A Chicago Case Study},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/JNVOBFDC}},
  note         = {Machine review of arXiv:2501.11214}
}
read the original 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 a travel demand dataset 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. Attention maps demonstrated the model's ability to dynamically adjust focus, leading to more balanced predictions. Case studies of various community areas in Chicago further illustrated the effectiveness of our approach in addressing spatial and demographic disparities, supporting more balanced and equitable urban planning and policy-making.

Figures

Figures reproduced from arXiv: 2501.11214 by the authors.

Figure 1
Figure 1. Comparison between prediction residual and demographic distributions. (a): the red colors represent under [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. Prediction framework with residual aware attention block. [PITH_FULL_IMAGE:figures/full_fig_p006_2.png] view at source ↗
Figure 3
Figure 3. Residual spatial distribution in Chicago. [PITH_FULL_IMAGE:figures/full_fig_p011_3.png] view at source ↗
Figures from the paper (1 more)
Figure 4
Figure 4. Figure 4: Attention Maps and Residuals in Chicago. [PITH_FULL_IMAGE:figures/full_fig_p013_4.png]

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Reference graph

Works this paper leans on

63 extracted references · 36 canonical work pages

  1. [1]

    Adaptive graph convolutional recurrent network for traffic forecasting

    Lei Bai, Lina Yao, Can Li, Xianzhi Wang, and Can Wang. Adaptive graph convolutional recurrent network for traffic forecasting. Advances in neural information processing systems, 33: 0 17804--17815, 2020

    work page 2020

  2. [2]

    Model multiplicity: Opportunities, concerns, and solutions

    Emily Black, Manish Raghavan, and Solon Barocas. Model multiplicity: Opportunities, concerns, and solutions. In Proceedings of the 2022 ACM Conference on Fairness, Accountability, and Transparency, FAccT ’22, page 850–863, New York, NY, USA, June 2022. Association for Computing Machinery. ISBN 978-1-4503-9352-2. doi:10.1145/3531146.3533149. URL https://dl...

    arXiv 2022

  3. [3]

    Fairness in Machine Learning : A Survey , October 2020

    Simon Caton and Christian Haas. Fairness in Machine Learning : A Survey , October 2020. URL http://arxiv.org/abs/2010.04053. arXiv:2010.04053 [cs, stat]

    arXiv 2020

  4. [4]

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

    Austin Derrow-Pinion, Jennifer She, David Wong, Oliver Lange, Todd Hester, Luis Perez, Marc Nunkesser, Seongjae Lee, Xueying Guo, Brett Wiltshire, Peter W. Battaglia, Vishal Gupta, Ang Li, Zhongwen Xu, Alvaro Sanchez-Gonzalez, Yujia Li, and Petar Velickovic. Eta prediction with graph neural networks in google maps. In Proceedings of the 30th ACM Internati...

    arXiv 2021

  5. [5]

    Edits: Modeling and mitigating data bias for graph neural networks

    Yushun Dong, Ninghao Liu, Brian Jalaian, and Jundong Li. Edits: Modeling and mitigating data bias for graph neural networks. In Proceedings of the ACM Web Conference 2022, page 1259–1269, April 2022. doi:10.1145/3485447.3512173. URL http://arxiv.org/abs/2108.05233. arXiv:2108.05233 [cs]

    arXiv 2022

  6. [6]

    Fairness through awareness

    Cynthia Dwork, Moritz Hardt, Toniann Pitassi, Omer Reingold, and Richard Zemel. Fairness through awareness. In Proceedings of the 3rd Innovations in Theoretical Computer Science Conference, ITCS ’12, page 214–226, New York, NY, USA, January 2012. Association for Computing Machinery. ISBN 978-1-4503-1115-1. doi:10.1145/2090236.2090255. URL https://doi.org/...

    arXiv 2012

  7. [7]

    Alexander D’Amour, Katherine Heller, Dan Moldovan, Ben Adlam, Babak Alipanahi, Alex Beutel, Christina Chen, Jonathan Deaton, Jacob Eisenstein, Matthew D. Hoffman, Farhad Hormozdiari, Neil Houlsby, Shaobo Hou, Ghassen Jerfel, Alan Karthikesalingam, Mario Lucic, Yian Ma, Cory McLean, Diana Mincu, Akinori Mitani, Andrea Montanari, Zachary Nado, Vivek Nataraj...

    work page 2022

  8. [9]

    Spatial-temporal graph ode networks for traffic flow forecasting

    Zheng Fang, Qingqing Long, Guojie Song, and Kunqing Xie. Spatial-temporal graph ode networks for traffic flow forecasting. In Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining, pages 364--373, 2021

    2021

Show all 63 references
  1. [10]

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

    Hamed Farahmand, Yuanchang Xu, and Ali Mostafavi. A spatial–temporal graph deep learning model for urban flood nowcasting leveraging heterogeneous community features. Scientific Reports, 13 0 (1): 0 6768, April 2023. ISSN 2045-2322. doi:10.1038/s41598-023-32548-x

    2023 doi

  2. [11]

    The sociodemographic biases in machine learning algorithms: A biomedical informatics perspective

    Gillian Franklin, Rachel Stephens, Muhammad Piracha, Shmuel Tiosano, Frank Lehouillier, Ross Koppel, and Peter L Elkin. The sociodemographic biases in machine learning algorithms: A biomedical informatics perspective. Life, 14 0 (6): 0 652, 2024

    2024

  3. [12]

    Compacteta: A fast inference system for travel time prediction

    Kun Fu, Fanlin Meng, Jieping Ye, and Zheng Wang. Compacteta: A fast inference system for travel time prediction. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, KDD ’20, page 3337–3345, New York, NY, USA, August 2020. Associ...

    2020

  4. [13]

    Spatiotemporal Multi-Graph Convolution Network for Ride-Hailing Demand Forecasting

    Xu Geng, Yaguang Li, Leye Wang, Lingyu Zhang, Qiang Yang, Jieping Ye, and Yan Liu. Spatiotemporal Multi-Graph Convolution Network for Ride-Hailing Demand Forecasting . Proceedings of the AAAI Conference on Artificial Intelligence, 33: 0 3656--3663, 2019. ISSN 2159-5399. doi:10...

    2019 doi

  5. [14]

    The case for process fairness in learning: Feature selection for fair decision making

    Nina Grgic-Hlacˇa, Muhammad Bilal Zafar, Krishna P Gummadi, and Adrian Weller. The case for process fairness in learning: Feature selection for fair decision making

  6. [15]

    Fairness-enhancing vehicle rebalancing in the ride-hailing system

    Xiaotong Guo, Hanyong Xu, Dingyi Zhuang, Yunhan Zheng, and Jinhua Zhao. Fairness-enhancing vehicle rebalancing in the ride-hailing system. 0 (arXiv:2401.00093), December 2023. doi:10.48550/arXiv.2401.00093. URL http://arxiv.org/abs/2401.00093. arXiv:2401.00093 [cs]

    2023 doi arXiv

  7. [16]

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

    Yong Han, Shukang Wang, Yibin Ren, Cheng Wang, Peng Gao, and Ge Chen. Predicting station-level short-term passenger flow in a citywide metro network using spatiotemporal graph convolutional neural networks . ISPRS International Journal of Geo-Information, 8 0 (6), 2019. ISSN 2...

    2019 doi

  8. [17]

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

    Jizhou Huang, Zhengjie Huang, Xiaomin Fang, Shikun Feng, Xuyi Chen, Jiaxiang Liu, Haitao Yuan, and Haifeng Wang. Dueta: Traffic congestion propagation pattern modeling via efficient graph learning for eta prediction at baidu maps. In Proceedings of the 31st ACM International C...

    2022

  9. [18]

    Urban ride-hailing demand prediction with multiple spatio-temporal information fusion network

    Guangyin Jin, Yan Cui, Liang Zeng, Hanbo Tang, Yanghe Feng, and Jincai Huang. Urban ride-hailing demand prediction with multiple spatio-temporal information fusion network. Transportation Research Part C: Emerging Technologies, 117: 0 102665, August 2020. ISSN 0968-090X. doi:1...

    2020

  10. [19]

    Deep multi-view graph-based network for citywide ride-hailing demand prediction

    Guangyin Jin, Zhexu Xi, Hengyu Sha, Yanghe Feng, and Jincai Huang. Deep multi-view graph-based network for citywide ride-hailing demand prediction. Neurocomputing, 510: 0 79–94, October 2022. ISSN 0925-2312. doi:10.1016/j.neucom.2022.09.010

    2022 doi

  11. [20]

    Spatio-temporal graph neural networks for predictive learning in urban computing: A survey

    Guangyin Jin, Yuxuan Liang, Yuchen Fang, Zezhi Shao, Jincai Huang, Junbo Zhang, and Yu Zheng. Spatio-temporal graph neural networks for predictive learning in urban computing: A survey. IEEE Transactions on Knowledge and Data Engineering, page 1–20, 2023 a . ISSN 1558-2191. do...

    2023

  12. [21]

    Spatio-temporal graph neural point process for traffic congestion event prediction

    Guangyin Jin, Lingbo Liu, Fuxian Li, and Jincai Huang. Spatio-temporal graph neural point process for traffic congestion event prediction. Proceedings of the AAAI Conference on Artificial Intelligence, 37 0 (1212): 0 14268–14276, June 2023 b . ISSN 2374-3468. doi:10.1609/aaai....

    2023 doi

  13. [22]

    Residual unfairness in fair machine learning from prejudiced data

    Nathan Kallus and Angela Zhou. Residual unfairness in fair machine learning from prejudiced data. In International Conference on Machine Learning, pages 2439--2448. PMLR, 2018

    2018

  14. [23]

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

    Jintao Ke, Hongyu Zheng, Hai Yang, and Xiqun (Michael) Chen. Short-term forecasting of passenger demand under on-demand ride services: A spatio-temporal deep learning approach. Transportation Research Part C: Emerging Technologies, 85: 0 591–608, December 2017. ISSN 0968-090X....

    2017 doi

  15. [24]

    STGAT: Spatial-Temporal Graph Attention Networks for Traffic Flow Forecasting

    Xiangyuan Kong, Weiwei Xing, Xiang Wei, Peng Bao, Jian Zhang, and Wei Lu. STGAT: Spatial-Temporal Graph Attention Networks for Traffic Flow Forecasting . IEEE Access, 8: 0 134363--134372, 2020. ISSN 21693536. doi:10.1109/ACCESS.2020.3011186

    2020

  16. [25]

    Counterfactual fairness

    Matt J Kusner, Joshua Loftus, Chris Russell, and Ricardo Silva. Counterfactual fairness. In Advances in Neural Information Processing Systems, volume 30. Curran Associates, Inc., 2017. URL https://proceedings.neurips.cc/paper_files/paper/2017/hash/a486cd07e4ac3d270571622f4f316...

    2017

  17. [26]

    Dstagnn: Dynamic spatial-temporal aware graph neural network for traffic flow forecasting

    Shiyong Lan, Yitong Ma, Weikang Huang, Wenwu Wang, Hongyu Yang, and Pyang Li. Dstagnn: Dynamic spatial-temporal aware graph neural network for traffic flow forecasting. In International conference on machine learning, pages 11906--11917. PMLR, 2022

    2022

  18. [27]

    Diffusion convolutional recurrent neural network: Data-driven traffic forecasting

    Yaguang Li, Rose Yu, Cyrus Shahabi, and Yan Liu. Diffusion convolutional recurrent neural network: Data-driven traffic forecasting . 6th International Conference on Learning Representations, ICLR 2018 - Conference Track Proceedings, pages 1--16, 2018

    2018

  19. [28]

    Geoman: Multi-level attention networks for geo-sensory time series prediction

    Yuxuan Liang, Songyu Ke, Junbo Zhang, Xiuwen Yi, and Yu Zheng. Geoman: Multi-level attention networks for geo-sensory time series prediction. In Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, page 3428–3434, Stockholm, Sweden, July...

    2018 doi

  20. [29]

    Evaluating Transportation Equity

    Todd Alexander Litman. Evaluating Transportation Equity . March 2023

    2023

  21. [30]

    Largest: A benchmark dataset for large-scale traffic forecasting

    Xu Liu, Yutong Xia, Yuxuan Liang, Junfeng Hu, Yiwei Wang, Lei Bai, Chao Huang, Zhenguang Liu, Bryan Hooi, and Roger Zimmermann. Largest: A benchmark dataset for large-scale traffic forecasting. In Advances in Neural Information Processing Systems, 2023

    2023

  22. [31]

    Social graph transformer networks for pedestrian trajectory prediction in complex social scenarios

    Yao Liu, Lina Yao, Binghao Li, Xianzhi Wang, and Claude Sammut. Social graph transformer networks for pedestrian trajectory prediction in complex social scenarios. In Proceedings of the 31st ACM International Conference on Information & Knowledge Management, CIKM ’22, page 133...

    2022

  23. [32]

    Make more connections: Urban traffic flow forecasting with spatiotemporal adaptive gated graph convolution network

    Bin Lu, Xiaoying Gan, Haiming Jin, Luoyi Fu, Xinbing Wang, and Haisong Zhang. Make more connections: Urban traffic flow forecasting with spatiotemporal adaptive gated graph convolution network. ACM Trans. Intell. Syst. Technol., 13 0 (2): 0 28:1--28:25, January 2022. ISSN 2157...

    2022 doi

  24. [33]

    A Survey on Bias and Fairness in Machine Learning , January 2022

    Ninareh Mehrabi, Fred Morstatter, Nripsuta Saxena, Kristina Lerman, and Aram Galstyan. A Survey on Bias and Fairness in Machine Learning , January 2022. URL http://arxiv.org/abs/1908.09635. arXiv:1908.09635 [cs]

    2022 arXiv

  25. [34]

    Social-stgcnn: A social spatio-temporal graph convolutional neural network for human trajectory prediction

    Abduallah Mohamed, Kun Qian, Mohamed Elhoseiny, and Christian Claudel. Social-stgcnn: A social spatio-temporal graph convolutional neural network for human trajectory prediction. 0 (arXiv:2002.11927), March 2020. URL http://arxiv.org/abs/2002.11927. arXiv:2002.11927 [cs]

    2002 arXiv

  26. [35]

    Stirnet: A spatial-temporal interaction-aware recursive network for human trajectory prediction

    Yusheng Peng, Gaofeng Zhang, Xiangyu Li, and Liping Zheng. Stirnet: A spatial-temporal interaction-aware recursive network for human trajectory prediction. In 2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW), page 2285–2293, October 2021. doi:10.1109...

    2021

  27. [36]

    A Review on Fairness in Machine Learning

    Dana Pessach and Erez Shmueli. A Review on Fairness in Machine Learning . ACM Computing Surveys, 55 0 (3): 0 51:1--51:44, February 2022. ISSN 0360-0300. doi:10.1145/3494672. URL https://doi.org/10.1145/3494672

    2022 doi

  28. [37]

    D-stgcn: Dynamic pedestrian trajectory prediction using spatio-temporal graph convolutional networks

    Bogdan Ilie Sighencea, Ion Rareș Stanciu, and Cătălin Daniel Căleanu. D-stgcn: Dynamic pedestrian trajectory prediction using spatio-temporal graph convolutional networks. Electronics, 12 0 (33): 0 611, January 2023. ISSN 2079-9292. doi:10.3390/electronics12030611

    2023 doi

  29. [38]

    Gummadi, Adish Singla, Adrian Weller, and Muhammad Bilal Zafar

    Till Speicher, Hoda Heidari, Nina Grgic-Hlaca, Krishna P. Gummadi, Adish Singla, Adrian Weller, and Muhammad Bilal Zafar. A unified approach to quantifying algorithmic unfairness: Measuring individual &group unfairness via inequality indices. In Proceedings of the 24th ACM SIG...

    2018

  30. [39]

    Spatial-temporal attention network for crime prediction with adaptive graph learning

    Mingjie Sun, Pengyuan Zhou, Hui Tian, Yong Liao, and Haiyong Xie. Spatial-temporal attention network for crime prediction with adaptive graph learning. In Elias Pimenidis, Plamen Angelov, Chrisina Jayne, Antonios Papaleonidas, and Mehmet Aydin, editors, Artificial Neural Netwo...

    2022 doi

  31. [40]

    Mfstgn: a multi-scale spatial-temporal fusion graph network for traffic prediction

    Ran Tian, Chu Wang, Jia Hu, and Zhongyu Ma. Mfstgn: a multi-scale spatial-temporal fusion graph network for traffic prediction. Applied Intelligence, 53 0 (19): 0 22582–22601, October 2023. ISSN 1573-7497. doi:10.1007/s10489-023-04703-4

    2023 doi

  32. [41]

    Attention is all you need

    Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, ukasz Kaiser, and Illia Polosukhin. Attention is all you need. Advances in neural information processing systems, 30, 2017

    2017

  33. [42]

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

    Beibei Wang, Youfang Lin, Shengnan Guo, and Huaiyu Wan. Gsnet: Learning spatial-temporal correlations from geographical and semantic aspects for traffic accident risk forecasting. Proceedings of the AAAI Conference on Artificial Intelligence, 35 0 (55): 0 4402–4409, May 2021 a...

    2021 doi

  34. [43]

    Hagen: Homophily-aware graph convolutional recurrent network for crime forecasting

    Chenyu Wang, Zongyu Lin, Xiaochen Yang, Jiao Sun, Mingxuan Yue, and Cyrus Shahabi. Hagen: Homophily-aware graph convolutional recurrent network for crime forecasting. Proceedings of the AAAI Conference on Artificial Intelligence, 36 0 (4): 0 4193–4200, June 2022. ISSN 2374-346...

    2022 doi

  35. [44]

    Boosting urban prediction tasks with domain-sharing knowledge via meta-learning

    Dongkun Wang, Jieyang Peng, Xiaoming Tao, and Yiping Duan. Boosting urban prediction tasks with domain-sharing knowledge via meta-learning. Information Fusion, page 102324, 2024 a

    2024

  36. [45]

    Qingyi Wang, Shenhao Wang, Yunhan Zheng, Hongzhou Lin, Xiaohu Zhang, Jinhua Zhao, and Joan Walker. Deep hybrid model with satellite imagery: How to combine demand modeling and computer vision for travel behavior analysis? Transportation Research Part B: Methodological, 179: 0 ...

    2024

  37. [46]

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

    Yuandong Wang, Hongzhi Yin, Hongxu Chen, Tianyu Wo, Jie Xu, and Kai Zheng. Origin-destination matrix prediction via graph convolution: a new perspective of passenger demand modeling. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data M...

    2019

  38. [47]

    Gallat: A spatiotemporal graph attention network for passenger demand prediction

    Yuandong Wang, Hongzhi Yin, Tong Chen, Chunyang Liu, Ben Wang, Tianyu Wo, and Jie Xu. Gallat: A spatiotemporal graph attention network for passenger demand prediction. In 2021 IEEE 37th International Conference on Data Engineering (ICDE), page 2129–2134, April 2021 b . doi:10....

    2021

  39. [48]

    Salim, Xuan Song, and Ryosuke Shibasaki

    Zhaonan Wang, Renhe Jiang, Hao Xue, Flora D. Salim, Xuan Song, and Ryosuke Shibasaki. Event-aware multimodal mobility nowcasting. 0 (arXiv:2112.08443), December 2021 c . doi:10.48550/arXiv.2112.08443. URL http://arxiv.org/abs/2112.08443. arXiv:2112.08443 [cs]

    2021 doi arXiv

  40. [49]

    Inductive graph neural networks for spatiotemporal kriging

    Yuankai Wu, Dingyi Zhuang, Aurelie Labbe, and Lijun Sun. Inductive graph neural networks for spatiotemporal kriging. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 35, pages 4478--4485, 2021

    2021

  41. [50]

    Spatial-temporal sequential hypergraph network for crime prediction with dynamic multiplex relation learning

    Lianghao Xia, Chao Huang, Yong Xu, Peng Dai, Liefeng Bo, Xiyue Zhang, and Tianyi Chen. Spatial-temporal sequential hypergraph network for crime prediction with dynamic multiplex relation learning. In Proceedings of the Thirtieth International Joint Conference on Artificial Int...

    2021 doi

  42. [51]

    Fairness- Aware Demand Prediction for New Mobility

    An Yan and Bill Howe. Fairness- Aware Demand Prediction for New Mobility . Proceedings of the AAAI Conference on Artificial Intelligence, 34 0 (01): 0 1079--1087, April 2020. ISSN 2374-3468. doi:10.1609/aaai.v34i01.5458. URL https://ojs.aaai.org/index.php/AAAI/article/view/545...

    2020 doi

  43. [52]

    Predicting traffic propagation flow in urban road network with multi-graph convolutional network

    Haiqiang Yang, Zihan Li, and Yashuai Qi. Predicting traffic propagation flow in urban road network with multi-graph convolutional network. Complex & Intelligent Systems, 10 0 (1): 0 23–35, February 2024. ISSN 2198-6053. doi:10.1007/s40747-023-01099-z

    2024 doi

  44. [53]

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

    Jiexia Ye, Juanjuan Zhao, Kejiang Ye, and Chengzhong Xu. How to build a graph-based deep learning architecture in traffic domain: A survey. IEEE Transactions on Intelligent Transportation Systems, 23 0 (5): 0 3904–3924, May 2022. ISSN 1524-9050, 1558-0016. doi:10.1109/TITS.202...

    2022

  45. [54]

    Coupled layer-wise graph convolution for transportation demand prediction

    Junchen Ye, Leilei Sun, Bowen Du, Yanjie Fu, and Hui Xiong. Coupled layer-wise graph convolution for transportation demand prediction. Proceedings of the AAAI Conference on Artificial Intelligence, 35 0 (55): 0 4617–4625, May 2021. ISSN 2374-3468. doi:10.1609/aaai.v35i5.16591

    2021 doi

  46. [55]

    Spatio-Temporal Graph Convolutional Networks : A Deep Learning Framework For Traffic Forecasting

    Bing Yu, Haoteng Yin, and Zhanxing Zhu. Spatio-Temporal Graph Convolutional Networks : A Deep Learning Framework For Traffic Forecasting . ProceedLearningings of the Twenty-Seventh International Joint Conference on Artificial Intelligence (IJCAI-18), pages 3634--3640, 2018. UR...

    2018

  47. [56]

    Deep spatio-temporal graph convolutional network for traffic accident prediction

    Le Yu, Bowen Du, Xiao Hu, Leilei Sun, Liangzhe Han, and Weifeng Lv. Deep spatio-temporal graph convolutional network for traffic accident prediction. Neurocomputing, 423: 0 135–147, January 2021. ISSN 0925-2312. doi:10.1016/j.neucom.2020.09.043

    2021 doi

  48. [58]

    Enhancing Fairness in AI -based Travel Demand Forecasting Models , March 2023 b

    Xiaojian Zhang, Qian Ke, and Xilei Zhao. Enhancing Fairness in AI -based Travel Demand Forecasting Models , March 2023 b . URL http://arxiv.org/abs/2303.01692. arXiv:2303.01692 [cs]

    2023 arXiv

  49. [59]

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

    Yang Zhang and Tao Cheng. Graph deep learning model for network-based predictive hotspot mapping of sparse spatio-temporal events. Computers, Environment and Urban Systems, 79: 0 101403, January 2020. ISSN 0198-9715. doi:10.1016/j.compenvurbsys.2019.101403

    2020

  50. [60]

    Equality of opportunity in travel behavior prediction with deep neural networks and discrete choice models

    Yunhan Zheng, Shenhao Wang, and Jinhua Zhao. Equality of opportunity in travel behavior prediction with deep neural networks and discrete choice models. Transportation Research Part C: Emerging Technologies, 132: 0 103410, November 2021. ISSN 0968090X. doi:10.1016/j.trc.2021.1...

    2021

  51. [62]

    Fairness-enhancing deep learning for ride-hailing demand prediction, March 2023 b

    Yunhan Zheng, Qingyi Wang, Dingyi Zhuang, Shenhao Wang, and Jinhua Zhao. Fairness-enhancing deep learning for ride-hailing demand prediction, March 2023 b . URL http://arxiv.org/abs/2303.05698. arXiv:2303.05698 [cs]

    2023 arXiv

  52. [63]

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

    Hongye Zhou, Feng Zhang, Zhenhong Du, and Renyi Liu. Forecasting pm2.5 using hybrid graph convolution-based model considering dynamic wind-field to offer the benefit of spatial interpretability. Environmental Pollution, 273: 0 116473, March 2021. ISSN 0269-7491. doi:10.1016/j....

    2021

  53. [64]

    Signet: A siamese graph convolutional network for multi-class urban change detection

    Yanpeng Zhou, Jinjie Wang, Jianli Ding, Bohua Liu, Nan Weng, and Hongzhi Xiao. Signet: A siamese graph convolutional network for multi-class urban change detection. Remote Sensing, 15 0 (99): 0 2464, January 2023. ISSN 2072-4292. doi:10.3390/rs15092464

    2023 doi

  54. [65]

    Uncertainty quantification of sparse travel demand prediction with spatial-temporal graph neural networks

    Dingyi Zhuang, Shenhao Wang, Haris Koutsopoulos, and Jinhua Zhao. Uncertainty quantification of sparse travel demand prediction with spatial-temporal graph neural networks. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pages 4639--4647, 2022

    2022

  55. [66]

    Advancing transportation mode share analysis with built environment: Deep hybrid models with urban road network

    Dingyi Zhuang, Qingyi Wang, Yunhan Zheng, Xiaotong Guo, Shenhao Wang, Haris N Koutsopoulos, and Jinhua Zhao. Advancing transportation mode share analysis with built environment: Deep hybrid models with urban road network. arXiv preprint arXiv:2405.14079, 2024

    2024 arXiv

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Reviewed August 10, 2026 · model on record in the stance chip above.