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Modeling the equivalent property damage only crash rate for road segments using the hurdle regression framework
Publication date: September 2016 Source:Analytic Methods in Accident Research, Volume 11 Author(s): Lu Ma, Xuedong Yan, Chong Wei, Jiangfeng Wang The understanding of the distributional characteristics of the equivalent property damage only (EPDO) crash rate is limited in the existing literature. Models without a proper distribution of EPDO rate could result in biased estimations and misinterpretations of factors. The importance of prediction accuracy and modeling performance for the EPDO rate should be acknowledged since they directly affect the allocation of limited public funds to safety management for road networ...
Source: Analytic Methods in Accident Research - August 11, 2016 Category: Accident Prevention Source Type: research

An empirical assessment of the effects of economic recessions on pedestrian-injury crashes using mixed and latent-class models
This study explores the differences in pedestrian injury severity in three distinct economic time periods from the recent global recession (the Great Recession): pre-recession, recession, and post-recession. Using data from pedestrian crashes in Chicago, Illinois over an eight-year period, separate time-period models of pedestrian-injury severities (with possible outcomes of severe injury, moderate injury, and minor injury) were estimated using latent-class logit and mixed logit models. Likelihood ratio tests were conducted to examine the overall stability of model estimates across time periods and marginal effects of each...
Source: Analytic Methods in Accident Research - August 6, 2016 Category: Accident Prevention Source Type: research

Exploring the application of the Negative Binomial –Generalized Exponential model for analyzing traffic crash data with excess zeros
Publication date: July 2015 Source:Analytic Methods in Accident Research, Volume 7 Author(s): Prathyusha Vangala, Dominique Lord, Srinivas Reddy Geedipally In order to analyze crash data, many new analysis tools are being developed by transportation safety analysts. The Negative Binomial–Generalized Exponential distribution (NB–GE) is such a tool that was recently introduced to handle datasets characterized by a large number of zero counts and is over-dispersed. As the name suggests, this three-parameter distribution is a combination of both Negative binomial and Generalized Exponential distributions. So far,...
Source: Analytic Methods in Accident Research - July 20, 2016 Category: Accident Prevention Source Type: research

Analysis of occupant injury severity in winter weather crashes: A fully Bayesian multivariate approach
The objective of this paper is to correctly determine the factors affecting occupant injury severity in winter seasons by addressing the within-crash and between-crash correlation of injury severity. To achieve this, fully Bayesian hierarchical multinomial logit models were developed for estimating occupant injury severity in weather-related crashes, non weather-related crashes, and all crashes. These models were developed using disaggregate crash data with occupants nested within crashes for four winter seasons in Iowa. Significant factors affecting occupant injury severity included factors related to occupants (gender, s...
Source: Analytic Methods in Accident Research - July 19, 2016 Category: Accident Prevention Source Type: research

Random parameters multivariate tobit and zero-inflated count data models: Addressing unobserved and zero-state heterogeneity in accident injury-severity rate and frequency analysis
Publication date: September 2016 Source:Analytic Methods in Accident Research, Volume 11 Author(s): Panagiotis Ch. Anastasopoulos This paper uses data collected over a five-year period between 2005 and 2009 in Indiana to estimate random parameters multivariate tobit and zero-inflated count data models of accident injury-severity rates and frequencies, respectively. The proposed modeling approach accounts for unobserved factors that may vary systematically across segments with and without observed or reported accident injury-severities, thus addressing unobserved, zero-accident state and non-zero-accident state hetero...
Source: Analytic Methods in Accident Research - July 19, 2016 Category: Accident Prevention Source Type: research

A spatially autoregressive and heteroskedastic space-time pedestrian exposure modeling framework with spatial lags and endogenous network topologies
Publication date: June 2016 Source:Analytic Methods in Accident Research, Volume 10 Author(s): Jungyeol Hong, Venky N. Shankar, Narayan Venkataraman The main objective of this study is to derive a modeling framework for characterizing the space-time exposure of pedestrians in crosswalks, where the spatial measure is characterized by pedestrian density and the temporal measure is characterized by crosswalk time occupancy. This characterization has not been observed in the literature, but is a characterization that allows one to differentiate the components of pedestrian exposure with enhanced resolution in space a...
Source: Analytic Methods in Accident Research - May 14, 2016 Category: Accident Prevention Source Type: research

Unobserved heterogeneity and the statistical analysis of highway accident data
Publication date: September 2016 Source:Analytic Methods in Accident Research, Volume 11 Author(s): Fred L. Mannering, Venky Shankar, Chandra R. Bhat Highway accidents are complex events that involve a variety of human responses to external stimuli, as well as complex interactions between the vehicle, roadway features/condition, traffic-related factors, and environmental conditions. In addition, there are complexities involved in energy dissipation (once an accident has occurred) that relate to vehicle design, impact angles, the physiological characteristics of involved humans, and other factors. With such a comp...
Source: Analytic Methods in Accident Research - May 8, 2016 Category: Accident Prevention Source Type: research

Modeling nonlinear relationship between crash frequency by severity and contributing factors by neural networks
This study develops neural network models to explore the nonlinear relationship between crash frequency by severity and risk factors. To eliminate the possibility of over-fitting and to deal with black-box characteristic, a network structure optimization and a rule extraction method are proposed. A case study compares the performance of the modified neural network models with that of the traditional multivariate Poisson-lognormal model for predicting crash frequency by severity on road segments in Hong Kong. The results indicate that the trained and optimized neural networks have better fitting and predictive performance t...
Source: Analytic Methods in Accident Research - April 5, 2016 Category: Accident Prevention Source Type: research

The effect of speed limits on drivers' choice of speed: A random parameters seemingly unrelated equations approach
Publication date: June 2016 Source:Analytic Methods in Accident Research, Volume 10 Author(s): Panagiotis Ch. Anastasopoulos, Fred L. Mannering Drivers’ choice of speed has long been known to be a critical factor in both the likelihood and severity of vehicle crashes. Given this, understanding drivers’ choice of speed and the possible effect that posted speed limits may have on this choice, is a critical element of safety research. This paper seeks to provide new insights on drivers’ speed-choice process by studying U.S. interstate highways (all of which are constructed to the same design-speed standard) unde...
Source: Analytic Methods in Accident Research - April 5, 2016 Category: Accident Prevention Source Type: research

Fast Bayesian inference for modeling multivariate crash counts
Publication date: March 2016 Source:Analytic Methods in Accident Research, Volume 9 Author(s): Volodymyr Serhiyenko, Sha A. Mamun, John N. Ivan, Nalini Ravishanker This paper investigates the multivariate Poisson Lognormal modeling of counts for different types of crashes. This multivariate model can account for the overdispersion as well as positive and/or negative association between counts. Approximate Bayesian inference via the Integrated Nested Laplace Approximations significantly decreases computational time which makes it attractive for researchers. The models are developed for single vehicle, same direc...
Source: Analytic Methods in Accident Research - March 5, 2016 Category: Accident Prevention Source Type: research

Multilevel Dirichlet process mixture analysis of railway grade crossing crash data
This article introduces a flexible Bayesian semiparametric approach to analyzing crash data that are of hierarchical or multilevel nature. We extend the traditional varying intercept (random effects) multilevel model by relaxing its standard parametric distributional assumption. While accounting for unobserved cross-group heterogeneity in the data through intercept, the proposed method allows identifying latent subpopulations (and consequently outliers) in data based on a Dirichlet process mixture. It also allows estimating the number of latent subpopulations using an elegant mathematical structure instead of prespecifying...
Source: Analytic Methods in Accident Research - February 28, 2016 Category: Accident Prevention Source Type: research

Evaluating crash type covariances and roadway geometric marginal effects using the multivariate Poisson gamma mixture model
Publication date: March 2016 Source:Analytic Methods in Accident Research, Volume 9 Author(s): Ghasak I.M.A. Mothafer, Toshiyuki Yamamoto, Venkataraman N. Shankar This paper investigates the correlations and covariances among the rear end, sideswipe, fixed object and other crash types on freeway sections using three-year crash data for 274 multilane freeway segments in the State of Washington, U.S.A. A multivariate Poisson gamma mixture count model (MVPGM) is developed assuming positive correlation among crash types. The model parameters are estimated using a maximum likelihood approach. Based on the empirical re...
Source: Analytic Methods in Accident Research - December 17, 2015 Category: Accident Prevention Source Type: research

Multivariate random parameters collision count data models with spatial heterogeneity
This study investigated the effects of including spatial heterogeneity in multivariate random parameters models and their influence on different collision severity levels. The models were developed for severe (injury and fatal) and no-injury collisions using three years of collision data from the city of Vancouver. Three different modeling formulations were applied to measure the effects of spatial heterogeneity in a multivariate random parameters model. The proposed models were estimated in a Full Bayesian (FB) context using Markov Chain Monte Carlo (MCMC) simulation. The Deviance Information Criteria (DIC) values indicat...
Source: Analytic Methods in Accident Research - December 11, 2015 Category: Accident Prevention Source Type: research

An efficient parallel sampling technique for Multivariate Poisson-Lognormal model: Analysis with two crash count datasets
This study investigates the Multivariate Poisson-lognormal (MVPLN) model that jointly models crash frequency and severity accounting for correlations. The ordinary univariate count models analyze crashes of different severity level separately ignoring the correlations among severity levels. The MVPLN model is capable to incorporate the general correlation structure and also takes account of the overdispersion in the data that leads to a superior data fitting. However, the traditional estimation approach for MVPLN model is computationally expensive, which often limits the use of MVPLN model in practice. In this work, a para...
Source: Analytic Methods in Accident Research - November 21, 2015 Category: Accident Prevention Source Type: research

Multilevel models to analyze before and after speed data
Publication date: December 2015 Source:Analytic Methods in Accident Research, Volume 8 Author(s): Md Tazul Islam, Karim El-Basyouny Analyzing before–after speed data is often limited to a standard comparison of various speed parameters. Although a few studies have used a model-based approach, various limitations exist in terms of both data and methodology. The aim of this paper was to examine the applicability of using multilevel models to analyze before–after speed data and to explore the effect of various temporal, geometrical, and traffic characteristics on traffic speed in an urban residential context. Two ...
Source: Analytic Methods in Accident Research - November 21, 2015 Category: Accident Prevention Source Type: research