A Deep Learning Approach to the Prediction of Short-term Traffic Accident Risk

With the rapid development of urbanization, the boom of vehicle numbers has resulted in serious traffic accidents, which led to casualties and huge economic losses. The ability to predict the risk of traffic accident is important in the prevention of the occurrence of accidents and to reduce the damages caused by accidents in a proactive way. However, traffic accident risk prediction with high spatiotemporal resolution is difficult, mainly due to the complex traffic environment, human behavior, and lack of real-time traffic-related data. In this study, we collected heterogeneous traffic-related data, including traffic accident, traffic flow, weather condition and air pollution from the same city; proposed a deep learning model based on recurrent neural network toward a prediction of traffic accident risk. The predictive accident risk can be potential applied to the traffic accident warning system. We ranked the predictive power of various factors considered in our model through the method of Granger causality analysis, and established the order of predictive power as traffic flow > traffic accident > geographical position >> weather + air quality + holiday + time period, which indicate that traffic flow is the most essential factor for the occurrence of traffic accidents. The proposed method can be integrated into an intelligent traffic control system toward a more reasonable traffic prediction and command organization.

[1]  Gaël Varoquaux,et al.  Scikit-learn: Machine Learning in Python , 2011, J. Mach. Learn. Res..

[2]  Guangnan Zhang,et al.  Risk factors associated with traffic violations and accident severity in China. , 2013, Accident; analysis and prevention.

[3]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[4]  Latifa Oukhellou,et al.  Forecasting dynamic public transport Origin-Destination matrices with long-Short term Memory recurrent neural networks , 2016, 2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC).

[5]  Huachun Tan,et al.  Short-term traffic flow forecasting with spatial-temporal correlation in a hybrid deep learning framework , 2016, ArXiv.

[6]  Alekseĭ Grigorʹevich Ivakhnenko,et al.  CYBERNETIC PREDICTING DEVICES , 1966 .

[7]  Geoffrey E. Hinton,et al.  Learning representations by back-propagating errors , 1986, Nature.

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

[9]  Jian Sun,et al.  Dynamic Bus Travel Time Prediction Models on Road with Multiple Bus Routes , 2015, Comput. Intell. Neurosci..

[10]  Paul E. Utgoff,et al.  Incremental Induction of Decision Trees , 1989, Machine Learning.

[11]  Yoshua Bengio,et al.  Gradient-based learning applied to document recognition , 1998, Proc. IEEE.

[12]  Nitish Srivastava,et al.  Improving neural networks by preventing co-adaptation of feature detectors , 2012, ArXiv.

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

[14]  Mohamed Abdel-Aty,et al.  Predicting Freeway Crashes from Loop Detector Data by Matched Case-Control Logistic Regression , 2004 .

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

[16]  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.

[17]  Alex Graves,et al.  Neural Turing Machines , 2014, ArXiv.

[18]  Fei-Yue Wang,et al.  Traffic Flow Prediction With Big Data: A Deep Learning Approach , 2015, IEEE Transactions on Intelligent Transportation Systems.

[19]  Li Li,et al.  Traffic signal timing via deep reinforcement learning , 2016, IEEE/CAA Journal of Automatica Sinica.

[20]  Jason Weston,et al.  Memory Networks , 2014, ICLR.

[21]  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.

[22]  Li Li,et al.  Robust causal dependence mining in big data network and its application to traffic flow predictions , 2015 .

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

[24]  Yunpeng Wang,et al.  Large-Scale Transportation Network Congestion Evolution Prediction Using Deep Learning Theory , 2015, PloS one.

[25]  Quoc V. Le,et al.  Sequence to Sequence Learning with Neural Networks , 2014, NIPS.

[26]  Seong-hun Park,et al.  Highway traffic accident prediction using VDS big data analysis , 2016, The Journal of Supercomputing.

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

[28]  Tara N. Sainath,et al.  Deep Neural Networks for Acoustic Modeling in Speech Recognition: The Shared Views of Four Research Groups , 2012, IEEE Signal Processing Magazine.