Secondary Accident Data Fusion for Assessing Long-Term Performance of Transportation Systems, 2007

Secondary accident statistics can be useful for studying the impact of traffic incident management strategies. An easy-to-implement methodology is presented for classifying secondary accidents using data fusion of a police accident database with intranet incident reports. A current method for classifying secondary accidents uses a static threshold that represents the spatial and temporal region of influence of the primary accident, such as two miles and one hour. An accident is considered secondary if it occurs upstream from the primary accident and is within the duration and queue of the primary accident. However, using the static threshold may result in both false positives and negatives because accident queues are constantly varying. The methodology presented in this report seeks to improve upon this existing method by making the threshold dynamic. An incident progression curve is used to mark the end of the queue throughout the entire incident. Four steps in the development of incident progression curves are described. Step one is the processing of intranet incident reports. Step two is the filling in of incomplete incident reports. Step three is the nonlinear regression of incident progression curves. Step four is the merging of individual incident progression curves into one master curve. To illustrate this methodology, 5,514 accidents from Missouri freeways were analyzed. The results show that secondary accidents identified by dynamic versus static thresholds can differ by more than 30%.

Final Report on the Portable Computerized Assessments of Sleepy Drivers in Operational Environments, June 29, 2011

Excessive daytime sleepiness underpins a large number of the reported motor vehicle crashes. Fair and accurate field measures are needed to identify at-risk drivers who have been identified as potentially driving in a sleep deprived state on the basis of erratic driving behavior. The purpose of this research study was to evaluate a set of cognitive tests that can assist Motor Vehicle Enforcement Officers on duty in identifying drivers who may be engaged in sleep impaired driving. Currently no gold standard test exists to judge sleepiness in the field. Previous research has shown that Psychomotor Vigilance Task (PVT) is sensitive to sleep deprivation. The first goal of the current study was to evaluate whether computerized tests of attention and memory, more brief than PVT, would be as sensitive to sleepiness effects. The second goal of the study was to evaluate whether objective and subjective indices of acute and cumulative sleepiness predicted cognitive performance. Findings showed that sleepiness effects were detected in three out of six tasks. Furthermore, PVT was the only task that showed a consistent slowing of both ‘best’, i.e. minimum, and ‘typical’ responses, median RT due to sleepiness. However, PVT failed to show significant associations with objective measures of sleep deprivation (number of hours awake). The findings indicate that sleepiness tests in the field have significant limitations. The findings clearly show that it will not be possible to set absolute performance thresholds to identify sleep-impaired drivers based on cognitive performance on any test. Cooperation with industry to adjust work and rest cycles, and incentives to comply with those regulations will be critical components of a broad policy to prevent sleepy truck drivers from getting on the road.

Evaluation of Steel Bridges, Volumes I & II, TR-493, 2007

This report is divided into two volumes. This volume (Volume I) summarizes a structural health monitoring (SHM) system that was developed for the Iowa DOT to remotely and continuously monitor fatigue critical bridges (FCB) to aid in the detection of crack formation. The developed FCB SHM system enables bridge owners to remotely monitor FCB for gradual or sudden damage formation. The SHM system utilizes fiber bragg grating (FBG) fiber optic sensors (FOSs) to measure strains at critical locations. The strain-based SHM system is trained with measured performance data to identify typical bridge response when subjected to ambient traffic loads, and that knowledge is used to evaluate newly collected data. At specified intervals, the SHM system autonomously generates evaluation reports that summarize the current behavior of the bridge. The evaluation reports are collected and distributed to the bridge owner for interpretation and decision making. Volume II summarizes the development and demonstration of an autonomous, continuous SHM system that can be used to monitor typical girder bridges. The developed SHM system can be grouped into two main categories: an office component and a field component. The office component is a structural analysis software program that can be used to generate thresholds which are used for identifying isolated events. The field component includes hardware and field monitoring software which performs data processing and evaluation. The hardware system consists of sensors, data acquisition equipment, and a communication system backbone. The field monitoring software has been developed such that, once started, it will operate autonomously with minimal user interaction. In general, the SHM system features two key uses. First, the system can be integrated into an active bridge management system that tracks usage and structural changes. Second, the system helps owners to identify damage and deterioration.