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Spatio-Temporal-Categorical Graph Neural Networks for Fine-Grained Multi-Incident Co-Prediction

Authors:

Kyoung-Sook Kim,

Ryosuke ShibasakiAuthors Info & Claims

CIKM '21: Proceedings of the 30th ACM International Conference on Information & Knowledge Management

Pages 2060 - 2069

https://doi.org/10.1145/3459637.3482482

Published: 30 October 2021 Publication History

Abstract

Forecasting incident occurrences (e.g. crime, EMS, traffic accident) is a crucial task for emergency service providers and transportation agencies in performing response time optimization and dynamic fleet management. However, such events are by nature rare and sparse, which causes the label imbalance problem and inferior performance of models relying on data sufficiency. The existing studies circumvent, instead of truly solving, this issue by defining the incident prediction problem in a coarse-grained temporal (e.g. daily) setting, which leaves the proposed models unrobust to fine-grained dynamics and trivial for the real-world decision making. In this paper, we tackle the temporally fine-grained incident prediction problem in a sparse setting by explicitly exploiting the behind-the-scene chainlike triggering mechanism. Moreover, this chain effect roots in multiple domains (i.e. spatial, categorical), which further entangles with the temporal dimension and happens to be time-variant. To be specific, we propose a novel deep learning framework, namely Spatio-Temporal-Categorical Graph Neural Networks (STC-GNN), to handle the multidimensional and dynamic chain effect for performing fine-grained multi-incident co-prediction. Extensive experiments on three real-world city-level incident datasets verify the insightfulness of our perspective and effectiveness of the proposed model.

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Cited By

Lu YWang WBai RZhou SGarg LBashir AJiang WHu X(2025)Hyper-relational interaction modeling in multi-modal trajectory prediction for intelligent connected vehicles in smart citesInformation Fusion10.1016/j.inffus.2024.102682114(102682)Online publication date: Feb-2025
https://doi.org/10.1016/j.inffus.2024.102682
Qiu HZhang JYang LHan KYang XGao Z(2025)Spatial–temporal multi-task learning for short-term passenger inflow and outflow prediction on holidays in urban rail transit systemsTransportation10.1007/s11116-025-10583-zOnline publication date: 18-Jan-2025
https://doi.org/10.1007/s11116-025-10583-z
Deng JJiang RZhang JSong XLarson K(2024)Multi-modality spatio-temporal forecasting via self-supervised learningProceedings of the Thirty-Third International Joint Conference on Artificial Intelligence10.24963/ijcai.2024/223(2018-2026)Online publication date: 3-Aug-2024
https://dl.acm.org/doi/10.24963/ijcai.2024/223
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Index Terms

Spatio-Temporal-Categorical Graph Neural Networks for Fine-Grained Multi-Incident Co-Prediction

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cover image ACM Conferences

CIKM '21: Proceedings of the 30th ACM International Conference on Information & Knowledge Management

October 2021

4966 pages

ISBN:9781450384469

DOI:10.1145/3459637

General Chairs:
Gianluca Demartini
The University of Queensland, Australia
,
Guido Zuccon
The University of Queensland, Australia
,
Program Chairs:
J. Shane Culpepper
RMIT University, Australia
,
Zi Huang
The University of Queensland, Australia
,
Hanghang Tong
University of Illinois at Urbana-Champaign, USA

Copyright © 2021 ACM.

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Published: 30 October 2021

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JST Strategic International Collaborative Research Program
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CIKM '21: The 30th ACM International Conference on Information and Knowledge Management

November 1 - 5, 2021

Queensland, Virtual Event, Australia

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Overall Acceptance Rate 1,861 of 8,427 submissions, 22%

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Cited By

Lu YWang WBai RZhou SGarg LBashir AJiang WHu X(2025)Hyper-relational interaction modeling in multi-modal trajectory prediction for intelligent connected vehicles in smart citesInformation Fusion10.1016/j.inffus.2024.102682114(102682)Online publication date: Feb-2025
https://doi.org/10.1016/j.inffus.2024.102682
Qiu HZhang JYang LHan KYang XGao Z(2025)Spatial–temporal multi-task learning for short-term passenger inflow and outflow prediction on holidays in urban rail transit systemsTransportation10.1007/s11116-025-10583-zOnline publication date: 18-Jan-2025
https://doi.org/10.1007/s11116-025-10583-z
Deng JJiang RZhang JSong XLarson K(2024)Multi-modality spatio-temporal forecasting via self-supervised learningProceedings of the Thirty-Third International Joint Conference on Artificial Intelligence10.24963/ijcai.2024/223(2018-2026)Online publication date: 3-Aug-2024
https://dl.acm.org/doi/10.24963/ijcai.2024/223
Khoshraftar SAn A(2024)A Survey on Graph Representation Learning MethodsACM Transactions on Intelligent Systems and Technology10.1145/363351815:1(1-55)Online publication date: 16-Jan-2024
https://dl.acm.org/doi/10.1145/3633518
Wang CLiang YTan GAngélica LLattanzi SMuñoz Medina AAkoglu LGionis AVassilvitskii S(2024)CityCAN: Causal Attention Network for Citywide Spatio-Temporal ForecastingProceedings of the 17th ACM International Conference on Web Search and Data Mining10.1145/3616855.3635764(702-711)Online publication date: 4-Mar-2024
https://dl.acm.org/doi/10.1145/3616855.3635764
Jin GLiang YFang YShao ZHuang JZhang JZheng Y(2024)Spatio-Temporal Graph Neural Networks for Predictive Learning in Urban Computing: A SurveyIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2023.333382436:10(5388-5408)Online publication date: 1-Oct-2024
https://dl.acm.org/doi/10.1109/TKDE.2023.3333824
Bhardwaj NPal ABhumika Das D(2024)Adaptive Context Based Road Accident Risk Prediction Using Spatio-Temporal Deep LearningIEEE Transactions on Artificial Intelligence10.1109/TAI.2023.33285785:6(2872-2883)Online publication date: Jun-2024
https://doi.org/10.1109/TAI.2023.3328578
Lone MAkhtar N(2024)Spatio-Temporal Data Analytics for Intelligent Transportation Systems: An Overview2024 First International Conference on Pioneering Developments in Computer Science & Digital Technologies (IC2SDT)10.1109/IC2SDT62152.2024.10696009(228-233)Online publication date: 2-Aug-2024
https://doi.org/10.1109/IC2SDT62152.2024.10696009
Zhang PYang MWang YYang TYu HYan X(2024)Integrating metro passenger flow data to improve the classification of urban functional regions using a heterogeneous graph neural networkInternational Journal of Digital Earth10.1080/17538947.2024.244346817:1Online publication date: 23-Dec-2024
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Wang ZChen YZhu FZheng ZMa JZhou B(2024)GENIIExpert Systems with Applications: An International Journal10.1016/j.eswa.2023.121565237:PBOnline publication date: 1-Feb-2024
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