Safety Analysis of Low-Volume Rural Roads in Iowa, 2010

Iowa features an extensive surface transportation system, with more than 110,000 miles of roadway, most of which is under the jurisdiction of local agencies. Given that Iowa is a lower-population state, most of this mileage is located in rural areas that exhibit low traffic volumes of less than 400 vehicles per day. However, these low-volume rural roads also account for about half of all recorded traffic crashes in Iowa, including a high percentage of fatal and major injury crashes. This study was undertaken to examine these crashes, identify major contributing causes, and develop low-cost strategies for reducing the incidence of these crashes. Iowa’s extensive crash and roadway system databases were utilized to obtain needed data. Using descriptive statistics, a test of proportions, and crash modeling, various classes of rural secondary roads were compared to similar state of Iowa controlled roads in crash frequency, severity, density, and rate for numerous selected factors that could contribute to crashes. The results of this study allowed the drawing of conclusions as to common contributing factors for crashes on low-volume rural roads, both paved and unpaved. Due to identified higher crash statistics, particular interest was drawn to unpaved rural roads with traffic volumes greater than 100 vehicles per day. Recommendations for addressing these crashes with low-cost mitigation are also included. Because of the isolated nature of traffic crashes on low-volume roads, a systemic or mass action approach to safety mitigation was recommended for an identified subset of the entire system. In addition, future development of a reliable crash prediction model is described.

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.

Systematic Identification of High Crash Locations Final Report, 2001

Federal and state policy makers increasingly emphasize the need to reduce highway crash rates. This emphasis is demonstrated in Iowa’s recently released draft Iowa Strategic Highway Safety Plan and by the U.S. Department of Transportation’s placement of “improved transportation safety” at the top of its list of strategic goals. Thus, finding improved methods to enhance highway safety has become a top priority at highway agencies. The objective of this project is to develop tools and procedures by which Iowa engineers can identify potentially hazardous roadway locations and designs, and to demonstrate the utility of these tools by developing candidate lists of high crash locations in the State. An initial task, building an integrated database to facilitate the tools and procedures, is an important product, in and of itself. Accordingly, the Iowa Department of Transportation (Iowa DOT) Geographic Information Management System (GIMS) and Geographic Information System Accident Analysis and Location System (GIS-ALAS) databases were integrated with available digital imagery. (The GIMS database contains roadway characteristics, e.g., lane width, surface and shoulder type, and traffic volume, for all public roadways. GIS-ALAS records include data, e.g., vehicles, drivers, roadway conditions, and the crash severity, for crashes occurring on public roadways during then past 10 years.)

The Effect of Four-Lane to Three-Lane Conversion on the Number of Crashes and Crash Rates in Iowa Roads, June 2005

We analyze crash data collected by the Iowa Department of Transportation using Bayesian methods. The data set includes monthly crash numbers, estimated monthly traffic volumes, site length and other information collected at 30 paired sites in Iowa over more than 20 years during which an intervention experiment was set up. The intervention consisted in transforming 15 undivided road segments from four-lane to three lanes, while an additional 15 segments, thought to be comparable in terms of traffic safety-related characteristics were not converted. The main objective of this work is to find out whether the intervention reduces the number of crashes and the crash rates at the treated sites. We fitted a hierarchical Poisson regression model with a change-point to the number of monthly crashes per mile at each of the sites. Explanatory variables in the model included estimated monthly traffic volume, time, an indicator for intervention reflecting whether the site was a “treatment” or a “control” site, and various interactions. We accounted for seasonal effects in the number of crashes at a site by including smooth trigonometric functions with three different periods to reflect the four seasons of the year. A change-point at the month and year in which the intervention was completed for treated sites was also included. The number of crashes at a site can be thought to follow a Poisson distribution. To estimate the association between crashes and the explanatory variables, we used a log link function and added a random effect to account for overdispersion and for autocorrelation among observations obtained at the same site. We used proper but non-informative priors for all parameters in the model, and carried out all calculations using Markov chain Monte Carlo methods implemented in WinBUGS. We evaluated the effect of the four to three-lane conversion by comparing the expected number of crashes per year per mile during the years preceding the conversion and following the conversion for treatment and control sites. We estimated this difference using the observed traffic volumes at each site and also on a per 100,000,000 vehicles. We also conducted a prospective analysis to forecast the expected number of crashes per mile at each site in the study one year, three years and five years following the four to three-lane conversion. Posterior predictive distributions of the number of crashes, the crash rate and the percent reduction in crashes per mile were obtained for each site for the months of January and June one, three and five years after completion of the intervention. The model appears to fit the data well. We found that in most sites, the intervention was effective and reduced the number of crashes. Overall, and for the observed traffic volumes, the reduction in the expected number of crashes per year and mile at converted sites was 32.3% (31.4% to 33.5% with 95% probability) while at the control sites, the reduction was estimated to be 7.1% (5.7% to 8.2% with 95% probability). When the reduction in the expected number of crashes per year, mile and 100,000,000 AADT was computed, the estimates were 44.3% (43.9% to 44.6%) and 25.5% (24.6% to 26.0%) for converted and control sites, respectively. In both cases, the difference in the percent reduction in the expected number of crashes during the years following the conversion was significantly larger at converted sites than at control sites, even though the number of crashes appears to decline over time at all sites. Results indicate that the reduction in the expected number of sites per mile has a steeper negative slope at converted than at control sites. Consistent with this, the forecasted reduction in the number of crashes per year and mile during the years after completion of the conversion at converted sites is more pronounced than at control sites. Seasonal effects on the number of crashes have been well-documented. In this dataset, we found that, as expected, the expected number of monthly crashes per mile tends to be higher during winter months than during the rest of the year. Perhaps more interestingly, we found that there is an interaction between the four to three-lane conversion and season; the reduction in the number of crashes appears to be more pronounced during months, when the weather is nice than during other times of the year, even though a reduction was estimated for the entire year. Thus, it appears that the four to three-lane conversion, while effective year-round, is particularly effective in reducing the expected number of crashes in nice weather.

Crash Data Validation: An Iowa Case Study, February, 2007

With the quickening pace of crash reporting, the statistical editing of data on a weekly basis, and the ability to provide working databases to users at CTRE/Iowa Traffic Safety Data Service, the University of Iowa, and the Iowa DOT, databases that would be considered incomplete by past standards of static data files are in “public use” even as the dynamic nature of the central DOT database allows changes to be made to both the aggregate of data and to the individual crashes already reported. Moreover, “definitive” analyses of serious crashes will, by their nature, lag seriously behind the preliminary data files. Even after these analyses, the dynamic nature of the mainframe data file means that crash numbers can continue to change long after the incident year. The Iowa DOT, its Office of Driver Services (the “data owner”), and institutional data users/distributors must establish data use, distribution, and labeling protocols to deal with the new, dynamic nature of data. In order to set these protocols, data must be collected concerning the magnitude of difference between database records and crash narratives and diagrams. This study determines the difference between database records and crash narratives for the Iowa Department of Transportation’s Office of Traffic and Safety crash database and the impacts of this difference.

Groundwater Availability Modeling: Lower Dakota Aquifer, 2008

This monthly report from the Iowa Department of Natural Resources is about the water quality management of Iowa's rivers, streams and lakes.

Motor Vehicle Crash Fatalities for Week Ending, March 21, 2008

Motor Vehicle Crash Fatalities

Motor Vehicle Crash Fatalities for Week Ending, March 28, 2008

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Motor Vehicle Crash Fatalities for Week Ending: April 4, 2008

Vehicle fatalities from around the state of Iowa.

Motor Vehicle Crash Fatalities for Week Ending, April 4, 2008

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Motor Vehicle Crash Fatalities for Week Ending, April 11, 2008

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Motor Vehicle Crash Fatalities for Week Ending, April 18, 2008

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Motor Vehicle Crash Fatalities for Week Ending, April 25, 2008

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Motor Vehicle Crash Fatalities for Week Ending, May 9, 2008

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Motor Vehicle Crash Fatalities for Week Ending, May 16, 2008

Motor Vehicle Crash Fatalities