A Comparative Study of Count Models: Application to Pedestrian-Vehicle Crashes Along Malaysia Federal Roads

Objectives: The objective of this study was to examine the effects of various roadway characteristics on the incidence of pedestrian–vehicle crashes by developing a set of crash prediction models on 543 km of Malaysia federal roads over a 4-year time span between 2007 and 2010. Methods: Four count models including the Poisson, negative binomial (NB), hurdle Poisson (HP), and hurdle negative binomial (HNB) models were developed and compared to model the number of pedestrian crashes. Results and Conclusions: The results indicated the presence of overdispersion in the pedestrian crashes (PCs) and showed that it is due to excess zero rather than variability in the crash data. To handle the issue, the hurdle Poisson model was found to be the best model among the considered models in terms of comparative measures. Moreover, the variables average daily traffic, heavy vehicle traffic, speed limit, land use, and area type were significantly associated with PCs.

[1]  Craig Lyon,et al.  Pedestrian Collision Prediction Models for Urban Intersections , 2002 .

[2]  D. Graham,et al.  Spatial Variation in Road Pedestrian Casualties: The Role of Urban Scale, Density and Land-use Mix , 2003 .

[3]  J. Lawless,et al.  Tests for Detecting Overdispersion in Poisson Regression Models , 1989 .

[4]  Said M. Easa,et al.  Collision Prediction Models for Three-Dimensional Two-Lane Highways , 2009 .

[5]  Simon Washington,et al.  Modeling crash outcome probabilities at rural intersections: application of hierarchical binomial logistic models. , 2007, Accident; analysis and prevention.

[6]  John N Ivan,et al.  Factors influencing injury severity of motor vehicle-crossing pedestrian crashes in rural Connecticut. , 2003, Accident; analysis and prevention.

[7]  Satish V. Ukkusuri,et al.  The role of built environment on pedestrian crash frequency , 2012 .

[8]  Q. Vuong.  Likelihood Ratio Tests for Model Selection and Non-Nested Hypotheses , 1989 .

[9]  Maurizio Guida,et al.  A crash-prediction model for multilane roads. , 2007, Accident; analysis and prevention.

[10]  Dominique Lord,et al.  Further notes on the application of zero-inflated models in highway safety. , 2007, Accident; analysis and prevention.

[11]  Mohamed Abdel-Aty,et al.  Comprehensive analysis of vehicle-pedestrian crashes at intersections in Florida. , 2005, Accident; analysis and prevention.

[12]  Helai Huang,et al.  Modeling road traffic crashes with zero-inflation and site-specific random effects , 2010, Stat. Methods Appl..

[13]  A. Bilgiç,et al.  Using count data models to determine the factors affecting farmers' quantity decisions of precision farming technology adoption , 2008 .

[14]  D. Lindenmayer,et al.  Modelling the abundance of rare species: statistical models for counts with extra zeros , 1996 .

[15]  John N. Ivan,et al.  Estimating Pedestrian Exposure Prediction Model in Rural Areas , 2001 .

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

[17]  Hoong Chor Chin,et al.  Applying the random effect negative binomial model to examine traffic accident occurrence at signalized intersections. , 2003, Accident; analysis and prevention.

[18]  John N. Ivan,et al.  Hierarchical Bayesian Estimation of Safety Performance Functions for Two-Lane Highways Using Markov Chain Monte Carlo Modeling , 2005 .

[19]  Getachew A Dagne,et al.  Bayesian semiparametric zero-inflated Poisson model for longitudinal count data. , 2010, Mathematical biosciences.

[20]  Rajiv Bhatia,et al.  An area-level model of vehicle-pedestrian injury collisions with implications for land use and transportation planning. , 2009, Accident; analysis and prevention.

[21]  A. Welsh,et al.  Generalized additive modelling and zero inflated count data , 2002 .

[22]  Timothy C. Coburn,et al.  Statistical and Econometric Methods for Transportation Data Analysis , 2004, Technometrics.

[23]  Andrew Tarko,et al.  Pedestrian injury analysis with consideration of the selectivity bias in linked police-hospital data. , 2011, Accident; analysis and prevention.

[24]  J. Mullahy.  Specification and testing of some modified count data models , 1986 .

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

[26]  Wafaa Saleh,et al.  Modelling motorcyclist injury severity by various crash types at T-junctions in the UK , 2008 .

[27]  Alan Agresti,et al.  Categorical Data Analysis , 2003 .

[28]  Young-Jun Kweon,et al.  Development of Crash Prediction Models with Individual Vehicular Data , 2011 .

[29]  Simon Washington,et al.  Modeling crash types: New insights into the effects of covariates on crashes at rural intersections , 2006 .

[30]  A Veeraragavan,et al.  Random parameter models for accident prediction on two-lane undivided highways in India. , 2011, Journal of safety research.

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

[32]  Jean-Philippe Boucher,et al.  Discrete distributions when modeling the disability severity score of motor victims. , 2010, Accident Analysis and Prevention.

[33]  U Brüde,et al.  Models for predicting accidents at junctions where pedestrians and cyclists are involved. How well do they fit? , 1993, Accident; analysis and prevention.

[34]  Christopher Meaney,et al.  Modeling factors influencing the demand for emergency department services in ontario: a comparison of methods , 2011, BMC emergency medicine.

[35]  Ezra Hauer.  Safety Review of Highway 407: Confronting Two Myths , 1999 .

[36]  Craig Lyon,et al.  Pedestrian Safety Prediction Methodology for Urban Signalized Intersections , 2010 .

[37]  Venkataraman N. Shankar,et al.  Modeling crashes involving pedestrians and motorized traffic , 2003 .

[38]  R. Martínez-Espiñeira,et al.  ‘Adopt a Hypothetical Pup’: A Count Data Approach to the Valuation of Wildlife , 2007 .

[39]  Liping Fu,et al.  A Comparative Study of Alternative Model Structures and Criteria for Ranking Locations for Safety Improvements , 2006 .

[40]  K. Wannemuehler,et al.  On the Use of Zero-Inflated and Hurdle Models for Modeling Vaccine Adverse Event Count Data , 2006, Journal of biopharmaceutical statistics.

[41]  J. G. Cragg.  Some Statistical Models for Limited Dependent Variables with Application to the Demand for Durable Goods , 1971 .

[42]  Dominique Lord,et al.  Poisson, Poisson-gamma and zero-inflated regression models of motor vehicle crashes: balancing statistical fit and theory. , 2005, Accident; analysis and prevention.

[43]  H C Chin,et al.  Modeling Accident Occurrence at Signalized Tee Intersections with Special Emphasis on Excess Zeros , 2003, Traffic injury prevention.

[44]  Poul Greibe,et al.  Accident prediction models for urban roads. , 2003, Accident; analysis and prevention.

[45]  J. Hilbe.  Negative Binomial Regression: Preface , 2007 .

[46]  Chen Zhang,et al.  Effects of Geometric Characteristics on Head-On Crash Incidence on Two-Lane Roads in Connecticut , 2005 .

[47]  Rune Elvik,et al.  The non-linearity of risk and the promotion of environmentally sustainable transport. , 2009, Accident; analysis and prevention.