Development of a global road safety performance function using deep neural networks

Abstract This paper explores the idea of applying a machine learning approach to develop a global road safety performance function (SFP) that can be used to predict the expected crash frequencies of different highways from different regions. A deep belief network (DBN) – one of the most popular deep learning models is introduced as an alternative to the traditional regression models for crash modelling. An extensive empirical study is conducted using three real world crash data sets covering six classes of highways as defined by location (urban vs. rural), number of lanes, access control, and region. The study involves a number of experiments aiming at addressing several critical questions pertaining to the relative performance of the DBN in terms of network structure, training method, data size, and generalization ability, as compared to the traditional regression models. The experimental results have shown that a DBN model could be trained with different crash datasets with prediction performance being at least comparable to that of the locally calibrated negative binomial (NB) model.

[1]  Geoffrey E. Hinton,et al.  Reducing the Dimensionality of Data with Neural Networks , 2006, Science.

[2]  Mohamed Abdel-Aty,et al.  Analyzing angle crashes at unsignalized intersections using machine learning techniques. , 2011, Accident; analysis and prevention.

[3]  Simon Washington,et al.  Statistical and Econometric Methods for Transportation Data Analysis (2nd Edition) , 2010 .

[4]  Alan Wood,et al.  Methodology for fitting and updating predictive accident models with trend. , 2013, Accident; analysis and prevention.

[5]  Li-Yen Chang,et al.  Analysis of freeway accident frequencies: Negative binomial regression versus artificial neural network , 2005 .

[6]  S. Washington,et al.  Statistical and Econometric Methods for Transportation Data Analysis , 2010 .

[7]  Iasonas Kokkinos,et al.  Modeling local and global deformations in Deep Learning: Epitomic convolution, Multiple Instance Learning, and sliding window detection , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[8]  Srinivas Reddy Geedipally,et al.  The Poisson-Weibull generalized linear model for analyzing motor vehicle crash data , 2013 .

[9]  Yoshua Bengio,et al.  On the Expressive Power of Deep Architectures , 2011, ALT.

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

[11]  J R Stewart,et al.  SAFETY EFFECTS OF THE CONVERSION OF RURAL TWO-LANE TO FOUR-LANE ROADWAYS BASED ON CROSS-SECTIONAL MODELS , 1999 .

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

[13]  Paul P Jovanis,et al.  Analysis of Road Crash Frequency with Spatial Models , 2008 .

[14]  Geoffrey E. Hinton.  Training Products of Experts by Minimizing Contrastive Divergence , 2002, Neural Computation.

[15]  Mohamed M. Ahmed,et al.  Exploring a Bayesian hierarchical approach for developing safety performance functions for a mountainous freeway. , 2011, Accident; analysis and prevention.

[16]  Yuanchang Xie,et al.  Predicting motor vehicle collisions using Bayesian neural network models: an empirical analysis. , 2007, Accident; analysis and prevention.

[17]  Fred L. Mannering,et al.  The statistical analysis of crash-frequency data: A review and assessment of methodological alternatives , 2010 .

[18]  Qiao Jun-fei,et al.  Design and Application of Continuous Deep Belief Network , 2015 .

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

[20]  J. Bared,et al.  Accident Models for Two-Lane Rural Segments and Intersections , 1998 .

[21]  Junfei Qiao,et al.  An improved RBM based on Bayesian Regularization , 2014, 2014 International Joint Conference on Neural Networks (IJCNN).

[22]  Liping Fu,et al.  A disaggregate model for quantifying the safety effects of winter road maintenance activities at an operational level. , 2012, Accident; analysis and prevention.

[23]  Geoffrey E. Hinton,et al.  Deep Learning , 2015, Nature.

[24]  Geoffrey E. Hinton,et al.  Modeling Human Motion Using Binary Latent Variables , 2006, NIPS.

[25]  Yichuan Tang,et al.  Deep Learning using Linear Support Vector Machines , 2013, 1306.0239.

[26]  F Mannering,et al.  Effect of roadway geometrics and environmental factors on rural freeway accident frequencies. , 1995, Accident; analysis and prevention.

[27]  S. Emerson,et al.  AASHTO (American Association of State Highway and Transportation Officials). 2001. A Policy on Geometric Design of Highways and Streets. Fourth Edition. Washington, D.C. , 2007 .

[28]  Dong Yu,et al.  Large vocabulary continuous speech recognition with context-dependent DBN-HMMS , 2011, 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[29]  Alan F. Murray,et al.  Continuous restricted Boltzmann machine with an implementable training algorithm , 2003 .

[30]  Liping Fu,et al.  Model-Based Versus Data-Driven Approach for Road Safety Analysis: Do More Data Help? , 2016 .

[31]  Yoshua. Bengio,et al.  Learning Deep Architectures for AI , 2007, Found. Trends Mach. Learn..

[32]  Ren Zhang,et al.  A model with Fuzzy Granulation and Deep Belief Networks for exchange rate forecasting , 2014, 2014 International Joint Conference on Neural Networks (IJCNN).

[33]  Geoffrey E. Hinton.  A Practical Guide to Training Restricted Boltzmann Machines , 2012, Neural Networks: Tricks of the Trade.

[34]  Shaw-Pin Miaou,et al.  Modeling Traffic Crash-Flow Relationships for Intersections: Dispersion Parameter, Functional Form, and Bayes Versus Empirical Bayes Methods , 2003 .