research-article

Deep Dynamic Fusion Network for Traffic Accident Forecasting

Authors:

Liefeng BoAuthors Info & Claims

CIKM '19: Proceedings of the 28th ACM International Conference on Information and Knowledge Management

Pages 2673 - 2681

https://doi.org/10.1145/3357384.3357829

Published: 03 November 2019 Publication History

Abstract

Traffic accident forecasting is a vital part of intelligent transportation systems in urban sensing. However, predicting traffic accidents is not trivial because of two key challenges: i) the complexities of external factors which are presented with heterogeneous data structures; ii) the complex sequential transition regularities exhibited with time-dependent and high-order inter-correlations. To address these challenges, we develop a deep Dynamic Fusion Network framework (DFN), to explore the central theme of improving the ability of deep neural network on modeling heterogeneous external factors in a fully dynamic manner for traffic accident forecasting. Specifically, DFN first develops an integrative architecture, i.e., with the cooperation of a context-aware embedding module and a hierarchical fusion network, to effectively transferring knowledge from different external units for spatial-temporal pattern learning across space and time. After that, we further develop a temporal aggregation neural network layer to automatically capture relevance scores from the temporal dimension. Through extensive experiments on real-world data collected from New York City, we validate the effectiveness of our framework against various competitive methods. Besides, we also provide a qualitative analysis on prediction results to show the model interpretability.

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Cited By

Zou DWang SLi XPeng HWang YLiu CSheng KZhang BAngélica LLattanzi SMuñoz Medina AAkoglu LGionis AVassilvitskii S(2024)MultiSPANS: A Multi-range Spatial-Temporal Transformer Network for Traffic Forecast via Structural Entropy OptimizationProceedings of the 17th ACM International Conference on Web Search and Data Mining10.1145/3616855.3635820(1032-1041)Online publication date: 4-Mar-2024
https://dl.acm.org/doi/10.1145/3616855.3635820
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
Lee YBradford BPosch K(2024)The Effectiveness of Big Data-Driven Predictive Policing: Systematic ReviewJustice Evaluation Journal10.1080/24751979.2024.2371781(1-34)Online publication date: 5-Jul-2024
https://doi.org/10.1080/24751979.2024.2371781
Show More Cited By

Index Terms

Deep Dynamic Fusion Network for Traffic Accident Forecasting
1. Information systems
  1. Information systems applications
    1. Spatial-temporal systems

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cover image ACM Conferences

CIKM '19: Proceedings of the 28th ACM International Conference on Information and Knowledge Management

November 2019

3373 pages

ISBN:9781450369763

DOI:10.1145/3357384

General Chairs:
Wenwu Zhu
Tsinghua University, China
,
Dacheng Tao
University of Massachusetts, USA
,
Xueqi Cheng
Institute of Computing Technology, CAS, China
,
Program Chairs:
Peng Cui
Tsinghua University, China
,
Elke Rundensteiner
Worcester Polytechnic Institute, USA
,
David Carmel
Amazon Research, USA
,
Qi He
LinkedIn, USA
,
Jeffrey Xu Yu
Chinese University of Hong Kong, China

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Published: 03 November 2019

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CIKM '19: The 28th ACM International Conference on Information and Knowledge Management

November 3 - 7, 2019

Beijing, China

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CIKM '19 Paper Acceptance Rate 202 of 1,031 submissions, 20%;

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

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Cited By

Zou DWang SLi XPeng HWang YLiu CSheng KZhang BAngélica LLattanzi SMuñoz Medina AAkoglu LGionis AVassilvitskii S(2024)MultiSPANS: A Multi-range Spatial-Temporal Transformer Network for Traffic Forecast via Structural Entropy OptimizationProceedings of the 17th ACM International Conference on Web Search and Data Mining10.1145/3616855.3635820(1032-1041)Online publication date: 4-Mar-2024
https://dl.acm.org/doi/10.1145/3616855.3635820
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
Lee YBradford BPosch K(2024)The Effectiveness of Big Data-Driven Predictive Policing: Systematic ReviewJustice Evaluation Journal10.1080/24751979.2024.2371781(1-34)Online publication date: 5-Jul-2024
https://doi.org/10.1080/24751979.2024.2371781
Wang SZhang YPiao XLin XHu YYin B(2024)Data-unbalanced traffic accident prediction via adaptive graph and self-supervised learningApplied Soft Computing10.1016/j.asoc.2024.111512157(111512)Online publication date: May-2024
https://doi.org/10.1016/j.asoc.2024.111512
Yang JHan SChen Y(2023)Prediction of Traffic Accident Severity Based on Random ForestJournal of Advanced Transportation10.1155/2023/76414722023(1-8)Online publication date: 1-Feb-2023
https://doi.org/10.1155/2023/7641472
Yang GZhang YHang JFeng XXie ZZhang DYang YFrommholz IHopfgartner FLee MOakes MLalmas MZhang MSantos R(2023)CARPG: Cross-City Knowledge Transfer for Traffic Accident Prediction via Attentive Region-Level Parameter GenerationProceedings of the 32nd ACM International Conference on Information and Knowledge Management10.1145/3583780.3614802(2939-2948)Online publication date: 21-Oct-2023
https://dl.acm.org/doi/10.1145/3583780.3614802
Liu ZChen YXia FBian JZhu BShen GKong X(2023)TAP: Traffic Accident Profiling via Multi-Task Spatio-Temporal Graph Representation LearningACM Transactions on Knowledge Discovery from Data10.1145/356459417:4(1-25)Online publication date: 24-Feb-2023
https://dl.acm.org/doi/10.1145/3564594
Wang SZhang JLi JMiao HCao J(2023)Traffic Accident Risk Prediction via Multi-View Multi-Task Spatio-Temporal NetworksIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2021.313562135:12(12323-12336)Online publication date: 1-Dec-2023
https://doi.org/10.1109/TKDE.2021.3135621
Rahmani SBaghbani ABouguila NPatterson Z(2023)Graph Neural Networks for Intelligent Transportation Systems: A SurveyIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2023.325775924:8(8846-8885)Online publication date: Aug-2023
https://doi.org/10.1109/TITS.2023.3257759
Trirat PYoon SLee J(2023)MG-TAR: Multi-View Graph Convolutional Networks for Traffic Accident Risk PredictionIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2023.323707224:4(3779-3794)Online publication date: Apr-2023
https://doi.org/10.1109/TITS.2023.3237072
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