Traffic Accident Prediction Based on Deep Spatio-Temporal Analysis

Traffic accidents usually lead to severe human casualties and huge economic losses. Timely accurate prediction of traffic accidents has great potential to protect public safety and reduce economic losses. However, it is a nontrivial endeavor to predict traffic accidents due to the complex causality of traffic accidents with multiple factors, the dynamic interactions of the related factors, and the intrinsic complexity of spatio-temporal traffic data. To overcome these difficulties, this paper provides a novel traffic accident prediction method, namely, STENN, which takes multiple information (Spatial distributions, Temporal dynamics, and External factors) into account, and aggregates these factors by a joint Neural Network structure. To evaluate the proposed method, we collect large-scale real-world data, which include accident records, real-time and citi-wide vehicle speeds, road networks, meteorological condition, and Point-of-Interest (POI) distributions. Experiments on the collected data set demonstrate that STENN is able to predict the traffic accidents in a fine-grained level and the prediction of accuracy outperforms four classical baselines.

[1]  Fei Wu,et al.  HST-LSTM: A Hierarchical Spatial-Temporal Long-Short Term Memory Network for Location Prediction , 2018, IJCAI.

[2]  You Song,et al.  A Deep Learning Approach to the Prediction of Short-term Traffic Accident Risk , 2017, ArXiv.

[3]  Lucia Romo,et al.  Description of Various Factors Contributing to Traffic Accidents in Youth and Measures Proposed to Alleviate Recurrence , 2017, Front. Psychiatry.

[4]  Marti A. Hearst.  Intelligent Connections: Battling with GA-Joe. , 1998 .

[5]  Razvan Pascanu,et al.  On the difficulty of training recurrent neural networks , 2012, ICML.

[6]  Guigang Zhang,et al.  Deep Learning , 2016, Int. J. Semantic Comput..

[7]  Jimmy Ba,et al.  Adam: A Method for Stochastic Optimization , 2014, ICLR.

[8]  Marti A. Hearst.  Trends & Controversies: Support Vector Machines , 1998, IEEE Intell. Syst..

[9]  Jason Weston,et al.  A unified architecture for natural language processing: deep neural networks with multitask learning , 2008, ICML '08.

[10]  Yu Zheng,et al.  Deep Spatio-Temporal Residual Networks for Citywide Crowd Flows Prediction , 2016, AAAI.

[11]  F. Costabile,et al.  A new approach to link transport emissions and air quality: An intelligent transport system based on the control of traffic air pollution , 2008, Environ. Model. Softw..

[12]  Ilsun You,et al.  Understanding urban dynamics based on pervasive sensing: An experimental study on traffic density and air pollution , 2013, Math. Comput. Model..

[13]  J. Ross Quinlan,et al.  Induction of Decision Trees , 1986, Machine Learning.

[14]  Tianbao Yang,et al.  Hetero-ConvLSTM: A Deep Learning Approach to Traffic Accident Prediction on Heterogeneous Spatio-Temporal Data , 2018, KDD.

[15]  Moinul Hossain,et al.  A Bayesian network based framework for real-time crash prediction on the basic freeway segments of urban expressways. , 2012, Accident; analysis and prevention.

[16]  Fei-Yue Wang,et al.  Long short-term memory model for traffic congestion prediction with online open data , 2016, 2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC).

[17]  Pengpeng Zhao,et al.  LC-RNN: A Deep Learning Model for Traffic Speed Prediction , 2018, IJCAI.

[18]  Strother H. Walker,et al.  Estimation of the probability of an event as a function of several independent variables. , 1967, Biometrika.

[19]  R. Tibshirani.  Regression Shrinkage and Selection via the Lasso , 1996 .

[20]  Yu Zheng,et al.  Deep Distributed Fusion Network for Air Quality Prediction , 2018, KDD.

[21]  Duo Zhang,et al.  Deep Learning for Real Time Crime Forecasting , 2017, ArXiv.

[22]  J J Hopfield,et al.  Neural networks and physical systems with emergent collective computational abilities. , 1982, Proceedings of the National Academy of Sciences of the United States of America.

[23]  Geoffrey E. Hinton,et al.  Rectified Linear Units Improve Restricted Boltzmann Machines , 2010, ICML.

[24]  Jun-ichi Imura,et al.  Traffic optimization via road pricing for queuing and flow congestion , 2017, 2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC).

[25]  Yisheng Lv,et al.  Real-Time Highway Traffic Accident Prediction Based on the k-Nearest Neighbor Method , 2009, 2009 International Conference on Measuring Technology and Mechatronics Automation.

[26]  Li-Yen Chang,et al.  Data mining of tree-based models to analyze freeway accident frequency. , 2005, Journal of safety research.

[27]  Adel W. Sadek,et al.  A Novel Variable Selection Method based on Frequent Pattern Tree for Real-time Traffic Accident Risk Prediction , 2015, ArXiv.

[28]  Xuan Song,et al.  Learning Deep Representation from Big and Heterogeneous Data for Traffic Accident Inference , 2016, AAAI.

[29]  Salissou Moutari,et al.  What are the factors that contribute to road accidents? An assessment of law enforcement views, ordinary drivers' opinions, and road accident records. , 2018, Accident; analysis and prevention.

[30]  Jürgen Schmidhuber,et al.  Long Short-Term Memory , 1997, Neural Computation.

[31]  Johan A. K. Suykens,et al.  Spatio-temporal Stacked LSTM for Temperature Prediction in Weather Forecasting , 2018, ArXiv.

[32]  Linpeng Huang,et al.  A Neural Attention Model for Urban Air Quality Inference: Learning the Weights of Monitoring Stations , 2018, AAAI.

[33]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[34]  Zhanxing Zhu,et al.  Spatio-temporal Graph Convolutional Neural Network: A Deep Learning Framework for Traffic Forecasting , 2017, IJCAI.