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.)

An Investigation of User Costs and Benefits of Winter Road Closures, June 2005

This project explores the user costs and benefits of winter road closures. Severe winter weather makes travel unsafe and dramatically increases crash rates. When conditions become unsafe due to winter weather, road closures should allow users to avoid crash costs and eliminate costs associated with rescuing stranded motorists. Therefore, the benefits of road closures are the avoided safety costs. The costs of road closures are the delays that are imposed on motorists and motor carriers who would have made the trip had the road not been closed. This project investigated the costs and benefits of road closures and found that evaluating the benefits and costs is not as simple as it appears. To better understand the costs and benefits of road closures, the project investigates the literature, conducts interviews with shippers and motor carriers, and conducts case studies of road closures to determine what actually occurred on roadways during closures. The project also estimates a statistical model that relates weather severity to crash rates. Although, the statistical model is intended to illustrate the possibility to quantitatively relate measurable and predictable weather conditions to the safety performance of a roadway. In the future, weather conditions such as snow fall intensity, visibility, etc., can be used to make objective measures of the safety performance of a roadway rather than relying on subjective evaluations of field staff. The review of the literature and the interviews clearly illustrate that not all delays (increased travel time) are valued the same. Expected delays (routine delays) are valued at the generalized costs (value of the driver’s time, fuel, insurance, wear and tear on the vehicle, etc.), but unexpected delays are valued much higher because they result in interruption of synchronous activities at the trip’s destination. To reduce the costs of delays resulting from road closures, public agencies should communicate as early as possible the likelihood of a road closure.

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 Rates and Crash Densities on Secondary Roads in Iowa by Surface Type 2001 – 2009, July 6, 2010

Crash Rates and Crash Densities on Secondary Roads in Iowa by Surface Type produced by the Iowa Department of Transportation.

Crash Rates and Crash Densities on Secondary Roads in Iowa by Functional Class 2001 – 2009, July 6, 2010

Crash Rates and Crash Densities on Secondary Roads in Iowa by Functional Class produced by the Iowa Department of Transportation.

Historical Summary of Travel, Crashes, Fatalities, and Rates 1970 – 2009, July 6, 2010

Historical Summary of Travel, Crashes, Fatalities, and Rates 1970 – 2009, produced by Iowa Department of Transportation.

Historical Summary of Travel, Crashes, Fatalities, and Rates 2001 – 2009, July 6, 2010

Historical Summary of Travel, Crashes, Fatalities, and Rates 2001 – 2009, produced by Iowa Department of Transportation.

Moving On Moves Recidivism Rates Lower, Data Download, Iowa Department of Corrections, October 2009

The results of a recently conducted evaluation show that a gender-responsive program for women probationers holds promise in significantly reducing recidivism rates. The University of Cincinnati conducted the evaluation of the cognitive-behavioral program, Moving On, in which it compared recidivism outcomes for moderate to high-risk Iowa offenders completing the program with a similar group of offenders not having attended any cognitive program.

Iowa Unemployment Rates by County, January 2014

Iowa Unemployment Rates by County map produced by the Iowa Workforce Development.

Unemployment Rates For Iowa Counties, March 2014

Iowa Unemployment Rates by County map produced by the Iowa Workforce Development.

Iowa Unemployment Rates by County, April 2014

Iowa Unemployment Rates by County map produced by the Iowa Workforce Development.

Iowa Unemployment Rates by County, July 2014

Iowa Unemployment Rates by County map produced by the Iowa Workforce Development.

Iowa Unemployment Rates by County, August 2014

Iowa Unemployment Rates by County map produced by the Iowa Workforce Development.

Iowa Unemployment Rates by County, September 2014

Iowa Unemployment Rates by County map produced by the Iowa Workforce Development.

Iowa Unemployment Rates by County, October 2014

Iowa Unemployment Rates by County map produced by the Iowa Workforce Development.