Calibration of Highway Safety Manual Work Zone Crash Modification Factors, TPF-5(081), 2014

The Highway Safety Manual is the national safety manual that provides quantitative methods for analyzing highway safety. The HSM presents crash modification factors related to work zone characteristics such as work zone duration and length. These crash modification factors were based on high-impact work zones in California. Therefore there was a need to use work zone and safety data from the Midwest to calibrate these crash modification factors for use in the Midwest. Almost 11,000 Missouri freeway work zones were analyzed to derive a representative and stratified sample of 162 work zones. The 162 work zones was more than four times the number of work zones used in the HSM. This dataset was used for modeling and testing crash modification factors applicable to the Midwest. The dataset contained work zones ranging from 0.76 mile to 9.24 miles and with durations from 16 days to 590 days. A combined fatal/injury/non-injury model produced a R2 fit of 0.9079 and a prediction slope of 0.963. The resulting crash modification factors of 1.01 for duration and 0.58 for length were smaller than the values in the HSM. Two practical application examples illustrate the use of the crash modification factors for comparing alternate work zone setups.

Issue review : Iowa State Patrol airplane and vehicle fleet.

This issue review provides information about the Iowa State Patrol's general fund budget; specifically, vehicle depreciation and fuel expenses.

TraumaHawk: Improving Treatment for Crash Victims Using Photos and a Smartphone App

Photographic documentation of crashed vehicles at the scene can be used to improve triage of crash victims. A U.S. expert panel developed field triage rules to determine the likelihood of occupants sustaining serious injuries based on vehicle damage that would require transport to a trauma center (Sasser et al., 2011). The use of photographs for assessing vehicle damage and occupant compartment intrusion as it correlates to increased injury severity has been validated (Davidson et al., 2014). Providing trauma staff with crash scene photos remotely could assist them in predicting injuries. This would allow trauma care providers to assess the appropriate transport, as well as develop mental models of treatment options prior to patient arrival at the emergency department (ED). Crash-scene medical response has improved tremendously in the past 20-30 years. This is in part due to the increasing number of paramedics who now have advanced life support (ALS) training that allows independence in the field. However, while this advanced training provides a more streamlined field treatment protocol, it also means that paramedics focused on treating crash victims may not have time to communicate with trauma centers regarding crash injury mechanisms. As a result, trauma centers may not learn about severe trauma patients until just a few minutes before they arrive. The information transmitted by the TraumaHawk app allows interpretation of injury mechanisms from crash scene photos at the trauma center, providing clues about the type and severity of injury. With strategic crash scene photo documentation, trained trauma professionals can assess the severity and patterns of injury based on exterior crush and occupant intrusion. Intrusion increases the force experienced by vehicle occupants, which translates into a higher level of injury severity (Tencer et al., 2005; Assal et al., 2002; Mandell et al., 2010). First responders have the unique opportunity to assess the damaged vehicle at the crash scene, but often the mechanism of injury is limited or not even relayed to ED trauma staff. To integrate photographic and scene information, an app called TraumaHawk was created to capture images of crash vehicles and send them electronically to the trauma center. If efficiently implemented, it provides the potential advantage of increasing lead-time for preparation at the trauma center through the crash scene photos. Ideally, the result is better treatment outcomes for crash victims. The objective of this analysis was to examine if the extra lead-time granted by the TraumaHawk app could improve trauma team activation time over the current conventional communication method.

Rural Expressway Intersection Synthesis of Practice and Crash Analysis, 2004

This report presents a national synthesis of rural expressway, two-way stop -controlled (TWSC) intersection safety strategies and intersection designs and an analysis of Iowa expressway TWSC intersection crash characteristics. A rural expressway is a multi-lane highway with a divided median and with mostly at -grade intersections, although some intersections may be grade separated. The synthesis of intersection strategies is conducted in two parts. The first is a literature review and the second part is a national survey of strategies currently being applied by state transportation agencies. The characterization of crash patterns at TWSC expressway intersections is examined through the analysis of 5 years of crash data at 644 intersections.

Evaluation of Iowa's Driver Improvement Program, 2009

This study examines the effectiveness of Iowa’s Driver Improvement Program (DIP), measured as the reduction in the number of driver convictions subsequent to the DIP. The analysis involved a random sample of 9,055 drivers who had been instructed to attend DIP and corresponding data on driver convictions, crashes, and driver education training history that were provided by the Iowa Motor Vehicle Division. The sample was divided into two groups based on DIP outcome: satisfactory or unsatisfactory completion. Two evaluation periods were considered: one year after the DIP date (probation period) and the period from the 13th to 18th month after the DIP date. The evaluation of Iowa’s DIP showed that there is evidence of effectiveness in terms of reducing driver convictions subsequent to attending the DIP. Among the 6,790 (75%) drivers who completed the course satisfactorily, 73% of drivers had no actions and 93% were not involved in a crash during the probation period. Statistical tests confirmed these numbers. However, the positive effect of satisfactory completion of DIP on survival time (that is, the time until the first conviction) was not statistically significant 13 months after the DIP date. Econometric model estimation results showed that, regardless of the DIP outcome, the likelihood of conviction occurrence and frequency of subsequent convictions depends on other factors, such as age, driver history, and DIP location, and interaction effects among these factors. Low-cost, early intervention measures are suggested to enhance the effectiveness of Iowa’s DIP. These measures can include advisory and warning letters (customized based on the driver’s age) sent within the first year after the DIP date and soon after the end of the probation period, as well as a closer examination of DIP instruction across the 17 community colleges that host the program. Given the large number of suspended drivers who continued to drive, consideration should also be given to measures to reduce driving while suspended offenses.

Spatial Scale of Clustering of Motor Vehicle Crash Types and Appropriate Countermeasures, 2009

This project analyzes the characteristics and spatial distributions of motor vehicle crash types in order to evaluate the degree and scale of their spatial clustering. Crashes occur as the result of a variety of vehicle, roadway, and human factors and thus vary in their clustering behavior. Clustering can occur at a variety of scales, from the intersection level, to the corridor level, to the area level. Conversely, other crash types are less linked to geographic factors and are more spatially “random.” The degree and scale of clustering have implications for the use of strategies to promote transportation safety. In this project, Iowa's crash database, geographic information systems, and recent advances in spatial statistics methodologies and software tools were used to analyze the degree and spatial scale of clustering for several crash types within the counties of the Iowa Northland Regional Council of Governments. A statistical measure called the K function was used to analyze the clustering behavior of crashes. Several methodological issues, related to the application of this spatial statistical technique in the context of motor vehicle crashes on a road network, were identified and addressed. These methods facilitated the identification of crash clusters at appropriate scales of analysis for each crash type. This clustering information is useful for improving transportation safety through focused countermeasures directly linked to crash causes and the spatial extent of identified problem locations, as well as through the identification of less location-based crash types better suited to non-spatial countermeasures. The results of the K function analysis point to the usefulness of the procedure in identifying the degree and scale at which crashes cluster, or do not cluster, relative to each other. Moreover, for many individual crash types, different patterns and processes and potentially different countermeasures appeared at different scales of analysis. This finding highlights the importance of scale considerations in problem identification and countermeasure formulation.