Modeling and Predicting Traffic Accidents at Signalized Intersections in the City of Norfolk, VA

By Maheshwari, Sharad; D'Souza, Kelwyn A. | Academy of Information and Management Sciences Journal, January 2013 | Go to article overview
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Modeling and Predicting Traffic Accidents at Signalized Intersections in the City of Norfolk, VA

Maheshwari, Sharad, D'Souza, Kelwyn A., Academy of Information and Management Sciences Journal


The main objective of this research was to study the signalized intersections in the City of Norfolk to delineate intersection geometry, road signage and other design factors which may be contributing significantly to traffic accidents. This research project is an extension of the previously completed study on the accident-patterns in the same city in which a multiple-regression model was developed on a selected set of intersections for the City. The City of Norfolk is one of the largest and oldest cities in the Hampton Roads region; and is home to roughly quarter million people. It is one of the most congested cities in the region by the population density. Furthermore, in 2006 the Hampton Roads had the highest crash incidents in the state compared to other regions on the basis of millions of VMT (vehicle mile traveled) (Nichols, 2007). The City of Norfolk contributed roughly 17% of those crashes in the region with annual traffic accident count of approximately 5,400. These data suggest that the traffic safety study could be useful to the City and to the Hampton Roads region.

There is evidence in the literature suggesting that road design factors could impact the traffic safety. Several highway engineering factors like lane widths, shoulder widths, horizontal curvature, vertical curvature, super-elevation rate, median and auxiliary lane were estimated and designed based on some traffic safety considerations. Additional factors like road signage, vegetation, line of sight of a traffic signal, horizontal and vertical curvature, and number of driveways close to an intersection have also been reported to have an impact on traffic safety. To study the impact of these factors along with traffic control rules, researchers have utilized a variety of statistical models (Maheshwari & D'Souza, 2010; 2006). The most popular model is the multivariate regression model where the dependent variable is generally based on traffic accidents and a set of independent variables include roadway design, traffic control, demographic variables and more. To mitigate the impact of large variability among the accident rates on different intersections, a negative binomial model was employed in the regression analysis. Regardless of statistical techniques used, research results show a relationship between the various roadway design and control factors with traffic accidents. Research also indicates divergence on the importance of individual factor on traffic safety. There is also a reported difference based on the regional demographic factors indicating regional accident rate differences due to interactions between design and control factors and the local driving population. Therefore, this study was designed to investigate the impact of the road design factors on the traffic accident rate in a local area.

The previous multiple regression model established a relationship between road design factors and accident rates but the predicted value from the model showed significant variability from the actual accident rate (Maheshwari & D'Souza, 2010). To improve upon the results from previous study, both, the data set (expanded independent variables) and statistical techniques were modified. Data on speed limit, vegetation and road signage were included in the dataset, along with exploratory statistical method and cluster analysis to enhance the predictive power of the regression model. Road signage data was limited to speed limit, name of the next street, turn lane, next signal, chevrons and other safety related posting. The objectives of this proposal were to:

1. Develop an exploratory statistical model that would provide a valid explanation of traffic accidents. A set of geometric, design, control and road signage factors would be used as independent variables for model development.

2. Validate the statistical model developed at step one.


Automobile accidents contribute to the staggering amount of property damage and the large number of deaths in the United States.

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Modeling and Predicting Traffic Accidents at Signalized Intersections in the City of Norfolk, VA


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