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...

Road crash prediction models are very useful tools in highway safety, given their potential for determining both the crash frequency occurrence and the degree severity of crashes. Crash frequency refers to the prediction of the number of crashes that would occur on a specific road segment or intersection in a time period, while crash severity models generally explore the relationship between crash severity injury and the contributing factors such as driver behavior, vehicle characteristics, roadway geometry, and road-environment conditions. Effective interventions to reduce crash toll include design of safer infrastructure and incorporation of road safety features into land-use and transportation planning; improvement of vehicle safety features; improvement of post-crash care for victims of road crashes; and improvement of driver behavior, such as setting and enforcing laws relating to key risk factors, and raising public awareness. Despite the great efforts that transportation agencies put into preventive measures, the annual number of traffic crashes has not yet significantly decreased. For instance, 35,092 traffic fatalities were recorded in the US in 2015, an increase of 7.2% as compared to the previous year. With such a trend, this paper presents an overview of road crash prediction models used by transportation agencies and researchers to gain a better understanding of the techniques used in predicting road accidents and the risk factors that contribute to crash occurrence.

Road traffic crash data are useful tools to support the development, implementation, and assessment of highway safety programs that tend to reduce road traffic crashes. Collecting road traffic crash data aims at gaining a better understanding of road traffic operational problems, locating hazardous road sections, identifying risk factors, developing accurate diagnosis and remedial measures, and evaluating the effectiveness of road safety programs. Furthermore, they can be used by many agencies and businesses such as: law enforcements to identify persons at fault in road traffic crashes; insurers seeking facts about traffic crash claims; road safety researchers to access traffic crash reliable database; decision makers to develop long-term, statewide strategic plans for traffic and highway safety; and highway safety administrators to help educate the public. Given the practical importance of vehicle crash data, this paper presents an overview of the sources, trends and problems associated with road traffic crash data.

Identifying vehicular crash high risk locations along highways is important for understanding the causes of vehicle crashes and to determine effective countermeasures based on the analysis. This paper presents a GIS approach to examine the spatial patterns of vehicle crashes and determines if they are spatially clustered, dispersed, or random. Moran's I and Getis-Ord Gi* statistic are employed to examine spatial patterns, clusters mapping of vehicle crash data, and to generate high risk locations along highways. Kernel Density Estimation (KDE) is used to generate crash concentration maps that show the road density of crashes. The proposed approach is evaluated using the 2013 vehicle crash data in the state of Indiana. Results show that the approach is efficient and reliable in identifying vehicle crash hot spots and unsafe road locations.

Multinomial logistic regression (MNL) is an attractive statistical approach in modeling the vehicle crash severity as it does not require the assumption of normality, linearity, or homoscedasticity compared to other approaches, such as the discriminant analysis which requires these assumptions to be met. Moreover, it produces sound estimates by changing the probability range between 0.0 and 1.0 to log odds ranging from negative infinity to positive infinity, as it applies transformation of the dependent variable to a continuous variable. The estimates are asymptotically consistent with the requirements of the nonlinear regression process. The results of MNL can be interpreted by both the regression coefficient estimates and/or the odd ratios (the exponentiated coefficients) as well. In addition, the MNL can be used to improve the fitted model by comparing the full model that includes all predictors to a chosen restricted model by excluding the non-significant predictors. As such, this paper presents a detailed step by step overview of incorporating the MNL in crash severity modeling, using vehicle crash data of the Interstate I70 in the State of Missouri, USA for the years (2013-2015).