Date of Graduation
Bachelor of Science in Industrial Engineering
Soo Nam, Chang
Considerable research has been made in recent years to evaluate road traffic safety. This is especially true with the United States, whose international rank in public safety is rapidly declining. In 2004, Arkansas ranked as the third highest in traffic fatalities among all the states. These are striking numbers that reflect the lack of attention that the state has received in terms of evaluating road traffic safety. Historically, this safety is measure by one of two methods: Statistical analyses of historical data or hands-on, observational analyses of present safety conditions. Rarely in research are both methods used within the same study. With this in hand, the objective of this research was to evaluate closely the issues involved with road traffic safety in the state of Arkansas. A database of all road traffic accidents within Arkansas between 2002 and 2004 was used in order to perform statistical testing and analyses. The study focused on intersection related crashes occurring on road segments within US highways, State highways, and Interstates with medium to heavy traffic volumes. In conjunction with these analyses, several handson observations of intersection locations were made to compare actual road safety with the statistical results, as well as to provide additional information that was not represented within any collected data. After carefully choosing key road segment locations throughout Arkansas, the intersections were surveyed for potential crash hazards. With the combination of these two approaches the leading factors for collisions in Arkansas were evaluated and preventative measures were recommended. Of all the potential factors, substantial attention was given to the human factors involved with road collisions. Historically, these factors have been found to be the most common of all factors, easiest to prevent, and therefore needing the most immediate attention. The statistical models developed for Arkansas roadways were the Poisson, Negative Binomial, and Logistic regression models. Among the significant contributors to crash frequency and severity were road width, number of lanes, pavement condition, horizontal and vertical curvature of the road design (p < 0.01). Also, weather and light conditions, seat belt usage, age, alcohol consumption, and number of passengers were shown to be significant to predicting crash frequencies and/or severities (p < 0.01). The observational analysis provided many insights on how road infrastructure and road surroundings can affect driving patterns and driver behavior. Poor signage, lane markings, traffic signals, and obstacles such as medians all can potentially decrement the driver?s experience and increase the risk of collision. The unique aspect of combining these two methods showed a vast improvement on the understanding of road traffic accidents and safety within the state of Arkansas. Their results give great insights and highlight potential issues of the driver behaviors and roadway characteristics that effect road traffic safety.
Mercer, Jacob, "Evaluating Arkansas roadway intersection accidents using traffic safety analysis methods: generalized estimating equations and roadway observation" (2008). Industrial Engineering Undergraduate Honors Theses. 9.