Modeling Traffic Accidents at Signalized Intersections in the City of Norfolk, Va
Maheshwari, Sharad, D'Souza, Kelwyn A., Journal of Management Information and Decision Sciences
This study was an attempt to apply a proactive approach using traffic pattern and signalized intersection characteristics to predict accident rates at signalized intersections in a city's arterial network. An earlier analysis of accident data at selected intersections within the City of Norfolk indicated that in addition to traffic volume, other controllable factors contributed to traffic accidents at specific intersections. These factors included area topography, lane patterns, type of road signs, turning lanes, etc. It is also known that administrative factors such as signal types, signal polices, road closures, etc., and maintenance factors such as road conditions, condition of the signals, condition of road signs, etc. also impact road accidents.
The objective of this study was to relate these variables to accident rate and delineate variables that are statistically more significant for accident rate. Data on several topographical variables was collected in the City of Norfolk. These variables included number of lanes, turn lanes, pedestrian crossing, restricted lanes, etc. A linear regression model was used to establish relationship between these variables and the accident rate. The resulting regression model explained 60% of the variability. It also showed that four topographical variables are more important than other variables. These variables include number of lanes, number of turn lanes, presence of median and presence of permanent hazard like railway crossing. However, validation of model showed higher than expected variation. The model developed, in this study, overestimates the accident rate by 33%, thus, limiting its practical application.
The main objective of this research was to study the signalized intersection in a city to delineate intersection geometry and design factors which may be contributing to traffic accidents. The City of Norfolk was selected for this study since it is one of the largest and oldest cities in the Hampton Roads region; and is home to roughly quarter million people. In 2006 the Hampton Roads had the highest crash incidents in the state based on the millions of VMT (vehicle mile traveled) (Nichols, 2007). The City of Norfolk contributed roughly 17% of those crashes with annual traffic accident count of approximately 5,400.
The literature review shows that road design factors could impact traffic safety. Several highway engineering factors like lane widths, shoulder widths, horizontal curvature, vertical curvature, super-elevation rate, median, auxiliary lane, etc. are estimated based on some traffic safety considerations. Additional factors like road signage, vegetation, line sight of signal especially on horizontal and vertical curvature, and number of driveways have also been reported to have impact on the traffic safety. To study the impact of these factors along with traffic control rules, researchers have utilized variety of statistical models. The most often used model is multivariate regression where the dependent variable is generally based on traffic accidents and a set of independent variables including roadway design, traffic control, demographic variables, etc. The negative binomial model is used to account for large variability among the accident rates on different intersections. Research results show relationship exists between the various roadway design and control factors and traffic accidents. Research also indicates divergences on the importance of individual factor on the traffic safety. There is reported difference based on the regional demographic factors indicating regional accident rate differences due to interactions between design/control factors and local driving population. This study was designed to understand the impact of the road design factors on the traffic accident rate in a local area.
This study was preceded by a pilot study conducted in the City of Norfolk for signalized intersections (Maheshwari and D'Souza, 2008). …