Observing and modeling drivers’ behavior in work zones using SHRP 2 naturalistic driving study data

Abstract

<p>The presence of a work zone increases disturbances to traffic flow and produces high cognitive workloads for drivers, which can increase the safety risks. There was an increase of about 11% in work zone-related fatalities from 2010 to 2014 despite a small decrease in non-work zone-related fatalities in the U.S.</p> <p>A number of studies concluded speeding and distractions are the main unsafe driver behaviors contributing to work zone crashes. Federal Highway Administration (FHWA) crash facts indicated speeding as a contributing factor for 28% of work zone crashes in 2014. A series of countermeasures have been used to get drivers’ attention to comply with work zone conditions. There is limited information about which safety features are the most effective in accomplishing this goal. The effectiveness of safety features can sometimes vary due to driver behavior that has not been truly investigated due to limited information in our traditional crash data.</p> <p>The Naturalistic Driving Study (NDS) data, developed by the Strategic Highway Research Program (SHRP) 2 provides a unique opportunity to observe actual driver behavior, to identify main contributing factors associated with crashes and near-crashes, and to understand how drivers negotiate work zones.</p> <p>The aim of this dissertation is to develop models that provide a better understanding of driver behavior in work zones. The additional objective is to determine the most effective safety features to get drivers’ attention in reducing their speed in work zones. The task was accomplished by conducting three studies.</p> <p>The first paper developed a logistic regression model using a number of explanatory variables which included driver behavior, work zone characteristics, and environmental conditions to predict the crash/near-crash event outcome.</p> <p>In the second paper, the speed trajectory time series data were used to develop models to accurately and efficiently estimate the location of changepoint in mean speed reacting to safety features utilized in work zones to encourage safe driving.</p> <p>The final paper utilized the methods of functional data analysis to understand and analyze driver behavior interacting with safety features applied in work zone with various characteristics. The methods were used to identify the effectiveness of various safety features.</p>