Analysis of freeway accident frequencies: Negative binomial regression versus artificial neural network

The Poisson or negative binomial regression model has been employed to analyze vehicle accident frequency for many years. However, these models have the pre-defined underlying relationship between dependent and independent variables. If this assumption is violated, the model could lead to erroneous estimation of accident likelihood. In contrast, the artificial neural network (ANN), which does not require any pre-defined underlying relationship between dependent and independent variables, has been shown to be a powerful tool in dealing with prediction and classification problems. Thus, this study employs a negative binomial regression model and an ANN model to analyze 1997-1998 accident data for the National Freeway 1 in Taiwan. By comparing the prediction performance between the negative binomial regression model and ANN model, this study demonstrates that ANN is a consistent alternative method for analyzing freeway accident frequency.

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

[2]  Patrick S. McCarthy,et al.  Public policy and highway safety: a city-wide perspective , 1999 .

[3]  S. Miaou.  The relationship between truck accidents and geometric design of road sections: Poisson versus negative binomial regressions. , 1994, Accident; analysis and prevention.

[4]  Martin T. Hagan,et al.  Neural network design , 1995 .

[5]  Shing Chung Josh Wong,et al.  Urban traffic flow prediction using a fuzzy-neural approach , 2002 .

[6]  Lorenzo Mussone,et al.  Estimating the Accident Probability of a Vehicular Flow by Means of an Artificial Neural Network , 1996 .

[7]  W. T. Hung,et al.  Neural network modeling of vehicle discharge headway at signalized intersection: model descriptions and results , 2002 .

[8]  Dipti Srinivasan,et al.  DEVELOPMENT AND ADAPTATION OF CONSTRUCTIVE PROBABILISTIC NEURAL NETWORK IN FREEWAY INCIDENT DETECTION , 2002 .

[9]  Fred L. Mannering,et al.  Negative binomial analysis of intersection accident frequencies , 1996 .

[10]  M. Zhong,et al.  ESTIMATION OF MISSING TRAFFIC COUNTS USING FACTOR, GENETIC, NEURAL AND REGRESSION TECHNIQUES , 2004 .

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

[12]  J Carson,et al.  The effect of ice warning signs on ice-accident frequencies and severities. , 2001, Accident; analysis and prevention.

[13]  So Young Sohn,et al.  Data fusion, ensemble and clustering to improve the classification accuracy for the severity of road traffic accidents in Korea , 2003 .

[14]  Fang Yuan,et al.  INCIDENT DETECTION USING SUPPORT VECTOR MACHINES , 2003 .

[15]  Stephen G. Ritchie,et al.  Coordinated traffic-responsive ramp control via nonlinear state feedback , 2001 .

[16]  F Mannering,et al.  Modeling accident frequencies as zero-altered probability processes: an empirical inquiry. , 1997, Accident; analysis and prevention.

[17]  Mohamed Abdel-Aty,et al.  Identifying crash propensity using specific traffic speed conditions. , 2005, Journal of safety research.

[18]  L Mussone,et al.  An analysis of urban collisions using an artificial intelligence model. , 1999, Accident; analysis and prevention.

[19]  S. L. Dhingra,et al.  Another insight into artificial neural networks through behavioural analysis of access mode choice , 1998 .

[20]  Mark Dougherty,et al.  A REVIEW OF NEURAL NETWORKS APPLIED TO TRANSPORT , 1995 .

[21]  Haris N. Koutsopoulos,et al.  Modeling discrete choice behavior using concepts from fuzzy set theory, approximate reasoning and neural networks , 2003 .

[22]  M. Hadi,et al.  ESTIMATING SAFETY EFFECTS OF CROSS-SECTION DESIGN FOR VARIOUS HIGHWAY TYPES USING NEGATIVE BINOMIAL REGRESSION , 1995 .

[23]  Mukesh Khare,et al.  ARTIFICIAL NEURAL NETWORK BASED LINE SOURCE MODELS FOR VEHICULAR EXHAUST EMISSION PREDICTIONS OF AN URBAN ROADWAY , 2004 .

[24]  Fred L. Mannering,et al.  The relationship among highway geometrics, traffic-related elements and motor-vehicle accident frequencies , 1998 .

[25]  Fred Mannering,et al.  Impact of roadside features on the frequency and severity of run-off-roadway accidents: an empirical analysis. , 2002, Accident; analysis and prevention.

[26]  David A. Hensher,et al.  A comparison of the predictive potential of artificial neural networks and nested logit models for commuter mode choice , 1997 .

[27]  Kui Wang,et al.  Modeling young driver motor vehicle crashes: data with extra zeros. , 2002, Accident; analysis and prevention.