Deborah S McAvoy

Benchmarking road safety of U.S. states: A DEA-based Malmquist productivity index approach

In this study, a DEA based Malmquist index model was developed to assess the relative efficiency and productivity of U.S. states in decreasing the number of road fatalities. Even though the national trend in fatal crashes has reached to the lowest level since 1949 (Traffic Safety Annual Assessment Highlights, 2010), a state-by-state analysis and comparison has not been studied considering other characteristics of the holistic national road safety assessment problem in any work in the literature or organizational reports. In this study, a DEA based Malmquist index model was developed to assess the relative efficiency and productivity of 50 U.S. states in reducing the number of fatal crashes. The single output, fatal crashes, and five inputs were aggregated into single road safety score and utilized in the DEA-based Malmquist index mathematical model. The period of 2002-2008 was considered due to data availability for the inputs and the output considered. According to the results, there is a slight negative productivity (an average of -0.2 percent productivity) observed in the U.S. on minimizing the number of fatal crashes along with an average of 2.1 percent efficiency decline and 1.8 percent technological improvement. The productivity in reducing the fatal crashes can only be attributed to the technological growth since there is a negative efficiency growth is occurred. It can be concluded that even though there is a declining trend observed in the fatality rates, the efficiency of states in utilizing societal and economical resources towards the goal of zero fatality is not still efficient. More effective policy making towards increasing safety belt usage and better utilization of safety expenditures to improve road condition are derived as the key areas to focus on for state highway safety agencies from the scope of current research.

Driving Simulator Validation for Nighttime Construction Work Zone Devices

Research was conducted to ascertain the validity of a driving simulator in determining the effectiveness of temporary traffic control devices in a work zone at night. The research was conducted through a field study and a simulator study. The field study examined speeds at six sites. The simulator study involved 127 human subjects. Spot speeds were observed at three locations in a freeway work zone. Locations of the speed studies were at the beginning of the work zone near the transition area, in the middle of the work zone, and at the end of the work zone near the downstream taper. Statistical analyses were conducted to determine whether participants of the study performed differently in the simulator, compared with in the field. Research results established that because of the motorists perceived risk of work zones, driving simulators may not replicate mean speeds observed in the field.

Assessing Driver Distraction From Cell Phone Use: A Simulator-Based Study

The relative driving performance of 37 drivers was compared in a controlled laboratory environment to assess how cell phone use affects driver performance on urban arterials and local roads. The stimulus consisted of answering a call on a hand-held cell phone and engaging in a scripted conversation with study researchers. A driving simulator replicated various typical real-world driving environments and roadway situations. Subjects drove a control scenario (baseline condition) and a test scenario in which they were asked to answer a set of questions using a hand-held cell phone while driving. The subjects were required to navigate various conditions, such as respond to traffic signs and signals, negotiate vehicular traffic when turning, and yield to unexpected pedestrians and bicyclists. Driver performance was assessed for overall driver performance scores, speed profiles, vehicular lateral placement within travel lanes, and number of crashes that occurred during the simulator experiment. Changes in measures between control and test scenarios were subjected to a series of statistical tests. Analysis results indicated that when cell phones were used while driving, subject performance scores were significantly lower, average speeds significantly slower, and proportions of improper lateral placement observed significantly higher. In addition, twice as many crashes (also statistically significant) were observed when subjects used cell phones while driving as were observed under the control condition. In this controlled laboratory experiment, the distraction caused by answering a call and engaging in a conversation on a hand-held cell phone significantly degraded driving performance.

Simulator Study of Primary and Precipitating Factors in Work Zone Crashes

Research with a driving simulator was conducted to determine the impact of various primary and precipitating factors on work zone crashes and associated driver performance. The primary factors included in the study were roadway type (undivided and divided), traffic density (low, moderate, and high), and work zone type (lane closure and shoulder closure). Precipitating factors included elements that caused the driver behavior or the environment to change and initiate the potential for a crash, near crash, or incident. Twelve precipitating factors were investigated; all could be described as involving either a stopped or slow vehicle in the travel lane or an object in the roadway. Forty-five participants were exposed to 24 different work zone configurations for which performance measures of crash frequency, speed, lane deviation, and deceleration data were collected. The performance measures were used to determine the most hazardous work zone configurations. Neither the level of traffic density for mean speed nor the type of roadway for lane deviation was found to be statistically different. The remaining statistical test rejected the null hypothesis that the performance measures were similar. Overall, the most hazardous work zone configurations entailed a divided roadway with a lane closure during low-density traffic conditions and a stopped or braking truck or car.

Predicting nationwide road fatalities in the US: a neural network approach

Road crashes are among the top five leading causes of deaths in the US although the national trend in fatal crashes has reached to the lowest level since 1949. Therefore, this paper introduces a non-parametric prediction models, artificial neural network (ANN), to assist policy-makers in minimising fatal crashes across the United States. Seven input variables from four safety performance input domains while fatal crash was utilised as the single output variable for the scope of the research. ANN was utilised and the best neural network model was developed out of 1,000 networks. The proposed neural network model predicted data with 84% coefficient of determination. In addition, developed ANN model was benchmarked with a multiple linear regression model and outperformed in all performance metrics including r, R-square and the standard error of estimate.