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.

References

[1]

Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. 2014. Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473 (2014).

[2]

Lida Barba, Nibaldo Rodr'iguez, and Cecilia Montt. 2014. Smoothing strategies combined with ARIMA and neural networks to improve the forecasting of traffic accidents. The Scientific World Journal, Vol. 2014 (2014).

[3]

Ruth Bergel-Hayat, Mohammed Debbarh, Constantinos Antoniou, and George Yannis. 2013. Explaining the road accident risk: weather effects. Accident Analysis & Prevention, Vol. 60 (2013), 456--465.

[4]

Chih-Chung Chang and Chih-Jen Lin. 2011. LIBSVM: a library for support vector machines. Transactions on Intelligent Systems and Technology (TIST), Vol. 2, 3 (2011), 27.

Digital Library

[5]

Heng-Tze Cheng, Levent Koc, et almbox. 2016. Wide & deep learning for recommender systems. In Recommender Systems Conference (Recsys). ACM, 7--10.

Digital Library

[6]

Junyoung Chung, Caglar Gulcehre, KyungHyun Cho, et almbox. 2014. Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv preprint arXiv:1412.3555 (2014).

[7]

Rupam Deb and Alan Wee-Chung Liew. 2016. Missing value imputation for the analysis of incomplete traffic accident data. Information Sciences, Vol. 339 (2016), 274--289.

Digital Library

[8]

Yuxiao Dong, Jing Zhang, Jie Tang, Nitesh V Chawla, and Bai Wang. 2015. Coupledlp: Link prediction in coupled networks. In International Conference on Knowledge Discovery and Data Mining (KDD). ACM, 199--208.

Digital Library

[9]

Jie Feng, Yong Li, Chao Zhang, Funing Sun, Fanchao Meng, Ang Guo, and Depeng Jin. 2018. Deepmove: Predicting human mobility with attentional recurrent networks. In International Conference on World Wide Web (WWW). ACM, 1459--1468.

Digital Library

[10]

Xiangnan He, Lizi Liao, Hanwang Zhang, Liqiang Nie, Xia Hu, and Tat-Seng Chua. 2017. Neural collaborative filtering. In International Conference on World Wide Web (WWW) . ACM, 173--182.

Digital Library

[11]

Sepp Hochreiter and Jürgen Schmidhuber. 1997. Long short-term memory. Neural computation, Vol. 9, 8 (1997), 1735--1780.

[12]

David W Hosmer Jr, Stanley Lemeshow, and Rodney X Sturdivant. 2013. Applied logistic regression . Vol. 398. John Wiley & Sons.

[13]

Chao Huang, Dong Wang, and Shenglong Zhu. 2017. Where are you from: Home location profiling of crowd sensors from noisy and sparse crowdsourcing data. In International Conference on Computer Communications (INFOCOM). IEEE, 1--9.

[14]

Chao Huang, Xian Wu, and Dong Wang. 2016. Crowdsourcing-based urban anomaly prediction system for smart cities. In International on Conference on Information and Knowledge Management (CIKM). ACM, 1969--1972.

Digital Library

[15]

Chao Huang, Chuxu Zhang, Jiashu Zhao, Xian Wu, Nitesh Chawla, and Dawei Yin. 2019. MiST: A Multiview and Multimodal Spatial-Temporal Learning Framework for Citywide Abnormal Event Forecasting. In The World Wide Web Conference (WWW). ACM, 717--728.

[16]

Chao Huang, Junbo Zhang, Yu Zheng, and Nitesh V Chawla. 2018. DeepCrime: Attentive Hierarchical Recurrent Networks for Crime Prediction. In International Conference on Information and Knowledge Management (CIKM). ACM, 1423--1432.

Digital Library

[17]

Diederik Kingma and Jimmy Ba. 2015. Adam: A method for stochastic optimization. International Conference on Learning Representations (ICLR) (2015).

[18]

Yaguang Li, Rose Yu, Cyrus Shahabi, and Yan Liu. 2018. Diffusion convolutional recurrent neural network: Data-driven traffic forecasting. International Conference on Learning Representations (ICLR) (2018).

[19]

Qiang Liu, Shu Wu, Liang Wang, and Tieniu Tan. 2016. Predicting the next location: A recurrent model with spatial and temporal contexts. In AAAI Conference on Artificial Intelligence (AAAI) .

[20]

Minh-Thang Luong, Hieu Pham, and Christopher D Manning. 2015. Effective approaches to attention-based neural machine translation. In International Conference on Empirical Methods in Natural Language Processing (EMNLP) .

[21]

Luis Moreira-Matias, Joao Gama, Michel Ferreira, Joao Mendes-Moreira, and Luis Damas. 2013. Predicting taxi--passenger demand using streaming data. Transactions on Intelligent Transportation Systems (TITS), Vol. 14, 3 (2013), 1393--1402.

Digital Library

[22]

Randa Oqab Mujalli, Griselda López, and Laura Garach. 2016. Bayes classifiers for imbalanced traffic accidents datasets. Accident Analysis & Prevention, Vol. 88 (2016), 37--51.

[23]

VA Olutayo and AA Eludire. 2014. Traffic accident analysis using decision trees and neural networks. International Journal of Information Technology and Computer Science, Vol. 2 (2014), 22--28.

[24]

World Health Organization. 2015. Global status report on road safety 2015 .World Health Organization.

[25]

Yao Qin, Dongjin Song, Haifeng Chen, Wei Cheng, Guofei Jiang, and Garrison Cottrell. 2017. A dual-stage attention-based recurrent neural network for time series prediction. In International Joint Conference on Artificial Intelligence (IJCAI). 2627--2633.

[26]

Honglei Ren, You Song, JingXin Liu, Yucheng Hu, and Jinzhi Lei. 2017. A Deep Learning Approach to the Prediction of Short-term Traffic Accident Risk. arXiv preprint arXiv:1710.09543 (2017).

[27]

Bharti Sharma, Vinod Kumar Katiyar, and Kranti Kumar. 2016. Traffic accident prediction model using support vector machines with Gaussian kernel. In ICSC . Springer, 1--10.

[28]

Xuan Song, Hiroshi Kanasugi, and Ryosuke Shibasaki. 2016. DeepTransport: Prediction and Simulation of Human Mobility and Transportation Mode at a Citywide Level. In International Joint Conference on Artificial Intelligence (IJCAI), Vol. 16. 2618--2624.

[29]

Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov. 2014. Dropout: a simple way to prevent neural networks from overfitting. The Journal of Machine Learning Research (JMLR), Vol. 15, 1 (2014), 1929--1958.

Digital Library

[30]

JD Tamerius, X Zhou, R Mantilla, and T Greenfield-Huitt. 2016. Precipitation effects on motor vehicle crashes vary by space, time, and environmental conditions. Weather, Climate, and Society, Vol. 8, 4 (2016), 399--407.

[31]

Jiexiong Tang, Chenwei Deng, and Guang-Bin Huang. 2015. Extreme learning machine for multilayer perceptron. Transactions on Neural Networks and Learning Systems (TNNLS), Vol. 27, 4 (2015), 809--821.

[32]

Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, and etc. 2017. Attention is all you need. In Advances in Neural Information Processing Systems (NIPS). 5998--6008.

[33]

Chenglong Wang, Feijun Jiang, and Hongxia Yang. 2017a. A hybrid framework for text modeling with convolutional rnn. In International Conference on Knowledge Discovery and Data Mining (KDD). ACM, 2061--2069.

Digital Library

[34]

Jing Wang and Xin Wang. 2011. An ontology-based traffic accident risk mapping framework. In Symposium on Spatial and Temporal Databases (SSTD). Springer, 21--38.

[35]

Senzhang Wang, Xiaoming Zhang, Jianping Cao, Lifang He, Leon Stenneth, Philip S Yu, Zhoujun Li, and Zhiqiu Huang. 2017b. Computing urban traffic congestions by incorporating sparse GPS probe data and social media data. Transactions on Information Systems (TOIS), Vol. 35, 4 (2017), 40.

Digital Library

[36]

Xian Wu, Yuxiao Dong, Chao Huang, Jian Xu, Dong Wang, and Nitesh V Chawla. 2017. Uapd: Predicting urban anomalies from spatial-temporal data. In The European Conference on Machine Learning & Principles and Practice of Knowledge Discovery in Databases (ECML/PKDD). Springer, 622--638.

[37]

Xian Wu, Baoxu Shi, Yuxiao Dong, Chao Huang, Louis Faust, and Nitesh V Chawla. 2018. RESTFul: Resolution-Aware Forecasting of Behavioral Time Series Data. In International Conference on Information and Knowledge Management (CIKM). ACM, 1073--1082.

Digital Library

[38]

Carl Yang, Lanxiao Bai, Chao Zhang, Quan Yuan, and Jiawei Han. 2017. Bridging collaborative filtering and semi-supervised learning: a neural approach for poi recommendation. In International Conference on Knowledge Discovery & Data Mining (KDD). ACM, 1245--1254.

Digital Library

[39]

Huaxiu Yao, Xianfeng Tang, Hua Wei, Guanjie Zheng, and Zhenhui Li. 2019. Revisiting Spatial-Temporal Similarity: A Deep Learning Framework for Traffic Prediction. In AAAI Conference on Artificial Intelligence (AAAI) .

[40]

Huaxiu Yao, Fei Wu, Jintao Ke, Xianfeng Tang, Yitian Jia, Siyu Lu, Pinghua Gong, and Jieping Ye. 2018. Deep multi-view spatial-temporal network for taxi demand prediction. In AAAI Conference on Artificial Intelligence (AAAI). 2588--2595.

[41]

Quanzeng You, Hailin Jin, Zhaowen Wang, Chen Fang, and Jiebo Luo. 2016. Image captioning with semantic attention. In International Conference on Computer Vision and Pattern Recognition (CVPR). 4651--4659.

[42]

Rose Yu, Yaguang Li, Cyrus Shahabi, Ugur Demiryurek, and Yan Liu. 2017. Deep learning: A generic approach for extreme condition traffic forecasting. In SIAM International Conference on Data Mining (SDM). SIAM, 777--785.

[43]

Jing Yuan, Yu Zheng, and Xing Xie. 2012. Discovering regions of different functions in a city using human mobility and POIs. In International Conference on Knowledge Discovery and Data Mining (KDD). ACM, 186--194.

Digital Library

[44]

Nicholas Jing Yuan, Yu Zheng, Xing Xie, Yingzi Wang, Kai Zheng, and Hui Xiong. 2014. Discovering urban functional zones using latent activity trajectories. Transactions on Knowledge and Data Engineering (TKDE), Vol. 27, 3 (2014), 712--725.

Digital Library

[45]

Zhuoning Yuan, Xun Zhou, and Tianbao Yang. 2018. Hetero-ConvLSTM: A deep learning approach to traffic accident prediction on heterogeneous spatio-temporal data. In International Conference on Knowledge Discovery and Data Mining (KDD). ACM, 984--992.

Digital Library

[46]

Yisong Yue, Thomas Finley, Filip Radlinski, and Thorsten Joachims. 2007. A support vector method for optimizing average precision. In International Conference on Research and Development in Information Retrieval (SIGIR). ACM, 271--278.

Digital Library

[47]

Chao Zhang, Mengxiong Liu, Zhengchao Liu, Carl Yang, Luming Zhang, and Jiawei Han. 2018. Spatiotemporal Activity Modeling Under Data Scarcity: A Graph-Regularized Cross-Modal Embedding Approach. In AAAI Conference on Artificial Intelligence (AAAI) .

[48]

Liang Zhao, Feng Chen, Chang-Tien Lu, and Naren Ramakrishnan. 2016. Multi-resolution Spatial Event Forecasting in Social Media. In International Conference on Data Mining (ICDM). IEEE, 689--698.

[49]

Shenglin Zhao, Tong Zhao, Irwin King, and Michael R Lyu. 2017. Geo-teaser: Geo-temporal sequential embedding rank for point-of-interest recommendation. In International Conference on World Wide Web (WWW) . ACM, 153--162.

Digital Library

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
Deng WLi H(2024)FAMT: Fusion of Feature Attention Mechanisms and Multiscale Temporal Relationships for Traffic Accident Prediction2024 International Joint Conference on Neural Networks (IJCNN)10.1109/IJCNN60899.2024.10651432(1-8)Online publication date: 30-Jun-2024
https://doi.org/10.1109/IJCNN60899.2024.10651432
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

Recommendations

A Deep Spatial-Temporal Network for Vehicle Trajectory Prediction
Wireless Algorithms, Systems, and Applications
Abstract
To plan travel routes reasonably and alleviate traffic congestion effectively, trajectory prediction of vehicles plays an important and necessary role in intelligent transportation. This paper presents a deep spatial-temporal network for long-term ...
Forecasting road traffic accident using deep artificial neural network approach in case of Oromia Special Zone
Abstract
Millions of people are dying, and billions of properties are damaged by road traffic accidents each year worldwide. In the case of our country Ethiopia, the effect of traffic accidents is even more by causing injuries, death, and property damage. ...
Intelligent Transportation System Based on Smart Soft-Sensors to Analyze Road Traffic and Assist Driver Behavior Applicable to Smart Cities
Highlights
- The Intelligent Transportation System (ITS) can assist the behavior driver on road, based vision sensor to monitoring steering angle actuator (steering wheel)...
Abstract
Sensors are considered as a great invention that radically contributed to saving time and effort for humans while moving or transporting things. That's the reason why, researchers are constantly working on a smart vision sensor that ...

Comments

Information & Contributors

Information

Published In

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

Copyright © 2019 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

Sponsors

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 03 November 2019

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Conference

CIKM '19

Sponsor:

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

November 3 - 7, 2019

Beijing, China

Acceptance Rates

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

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

Upcoming Conference

CIKM '25

Sponsor:
sigir
sigir

The 34th ACM International Conference on Information and Knowledge Management

November 10 - 14, 2025

Seoul , Republic of Korea

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

32
Total Citations
View Citations
723
Total Downloads

Downloads (Last 12 months)60
Downloads (Last 6 weeks)2

Reflects downloads up to 28 Feb 2025

Other Metrics

View Author Metrics

Citations

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
Deng WLi H(2024)FAMT: Fusion of Feature Attention Mechanisms and Multiscale Temporal Relationships for Traffic Accident Prediction2024 International Joint Conference on Neural Networks (IJCNN)10.1109/IJCNN60899.2024.10651432(1-8)Online publication date: 30-Jun-2024
https://doi.org/10.1109/IJCNN60899.2024.10651432
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
Alhaek FLi TRajeh TJaved MLiang W(2024)Encoding Global Semantic and Localized Geographic Spatial-Temporal Relations for Traffic Accident Risk PredictionInformation Sciences10.1016/j.ins.2024.121767(121767)Online publication date: Dec-2024
https://doi.org/10.1016/j.ins.2024.121767
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
Gao XJiang XHaworth JZhuang DWang SChen HLaw S(2024)Uncertainty-aware probabilistic graph neural networks for road-level traffic crash predictionAccident Analysis & Prevention10.1016/j.aap.2024.107801208(107801)Online publication date: Dec-2024
https://doi.org/10.1016/j.aap.2024.107801
Santosh Kumar Patra PHari Krishna BKrishna JKrishna Satya Varma MBharath Simha Reddy V(2024)Enhanced Deep Learning Model for Road Transportation Safety with Accident Detection and Traffic Flow AnalysisRecent Trends in Artificial Intelligence Towards a Smart World10.1007/978-981-97-6790-8_11(295-320)Online publication date: 10-Sep-2024
https://doi.org/10.1007/978-981-97-6790-8_11
Malik MNandal RChhikara R(2024)Revolutionizing Road Safety: Machine Learning Approaches for Predicting Road Accident SeverityComputation of Artificial Intelligence and Machine Learning10.1007/978-3-031-71481-8_17(218-225)Online publication date: 25-Sep-2024
https://doi.org/10.1007/978-3-031-71481-8_17
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
Show More Cited By

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Figures

Tables

Media

View Table of Conten