Crash Frequency Analysis

Abstract

Modeling highway traffic crash frequency is an important approach for identifying high crash risk areas that can help transportation agencies allocate limited resources more efficiently, and find preventive measures. This paper applies a Poisson regression model, Negative Binomial regression model and then proposes an Artificial Neural Network model to analyze the 2008-2012 crash data for the Interstate I-90 in the State of Minnesota in the US. By comparing the prediction performance between these three models, this study demonstrates that the Neural Network is an effective alternative method for predicting highway crash frequency. KeywordsPoisson Regression, Negative Binomial Regression, Artificial Neural Network, Crash Frequency 2011 fatalities, and an additional of 2,362,000 people were injured in crashes with an increase of 6.5% over 2011 injuries. Therefore, there should be further research studies on the risk factors associated with traffic accidents. The occurrence of crashes can be attributed to driver, vehicle, environment, and roadway characteristics. This paper begins with a literature review of modeling accident frequencies, followed by a description of the data used in the analysis, then introduces the methodological approach of evaluating Poisson regression and Negative Binomial regression, and then proposes the Artificial Neural Network approach to improve upon the A. Abdulhafedh 170 two previous methods, followed by discussion of findings, and comparison of results. The paper concludes with a summary and directions for future researches. Literature ReviewModeling of crash count data is very important topic in highway safety analysis, and in the past few decades, modelers have proposed a significant number of analysis tools for analyzing crash data. The number of crashes per year (or per more than one year, such as five years) is called the crash frequency, which has been widely used as an indicator of the crash occurrence at highways or certain segments of the roads. A variety of independent variables can affect crash frequency that are related to the driver behaviors, road geometric, vehicle, and environment. The influence of such variables on crash occurrence could significantly vary on case by case basis, but in general, past researches have shown that both behavioral factors related to the driver's errors, and nonbehavioral factors related to the road geometry, vehicle, and environment can significantly affect traffic accidents, and researchers usually extract only a limited number of variables from each class to be used as independent variables in the modeling process [1]. Previous researches in the literature that attempted to estimate crash frequency can be classified into two types. One type includes conventional univariate regression models, such as the Poisson regression model, Poisson-Gamma (Negative Binomial) model, Poisson-lognormal model, zero-inflated model, and Conway-Maxwell-Poisson model. The second type includes more specification-based models such as generalized additive models...

Statistical Analysis of Climate Factors Influencing Dengue Incidences in Colombo, Sri Lanka: Poisson and Negative Binomial Regression Approach

“…The assumption is that exp(e) = δ has a Gamma distribution with paramters E(δ) = 1 and Var(δ) = 1/ν. The result of the combination of Poisson and Gamma distributions is the negative binomial distribution [16]. The expected value of the reponse variable is the same as the for the Poisson distribution, but the conditional variance differs:…”

Road Crash Prediction Models: Different Statistical Modeling Approaches

“…Hence, a Poisson regression model based upon a generalized linear framework was soon adopted over conventional multiple linear regression techniques. Several such Poisson regression approaches for exploring the relationship between the risk factors and crash frequency have been proposed [15] [20]. However, it has been found that Poisson regression approaches have one important constraint that the mean must be equal to the variance which if violated, the standard errors estimated by the maximum likelihood method, will be biased, and the test statistics derived from the model will be incorrect.…”

“…For instance, regression models need a pre-defined relationship or functional form between the dependent variable (crash frequency) and the independent explanatory variables that can be estimated by some statistical approaches, whereas the ANNs do not require the establishment of these functional forms, and can be easily applied in the analysis. On the other hand, the ANNs differ from the statistical models in that they behave as black-boxes and do not provide interpretation for the parameter estimates [15] [18] [35] [36]. Fuzzy logic applications have increasingly been proven to have a significant crash-predicting capability in recent years [49].…”

Road Traffic Crash Data: An Overview on Sources, Problems, and Collection Methods

“…Over-dispersion in crash data can result from a variety of factors, such as the clustering of data, unaccounted temporal correlation, and model miss-specification (Cameron and Trivedi 1998). When data are over-dispersed, estimation of a crash model can lead to biased parameter estimates, which in turn could lead to incorrect inferences regarding the factors that determine crash-frequencies [29] [30] [31] [32].…”

“…theoretical) variance, and most likely to occur with small sample sizes. Although rare, however, under-dispersion can lead to incorrect parameter estimates and crash prediction [29] [31] [32] [33].…”