Evaluation of Virtual Reality Snowplow Simulator Training: A Literature Review, September 2006

Each winter, the Iowa Department of Transportation (Iowa DOT) maintenance operators are primarily responsible for plowing snow off federal and state roads. Drivers typically work long shifts under treacherous conditions. In addition to properly navigating the vehicle, drivers are required to operate several plowing mechanisms simultaneously, such as plow controls and salt sprayers. However, operators have few opportunities during the year to practice and refine their skills. An ideal training program would provide operators with the opportunity to practice these skills under realistic yet safe conditions, as well as provide basic training to novice or less-experienced operators. Recent technological advancements have made driving simulators a desirable training and research tool. This literature review discusses much of the recent research establishing simulator fidelity and espousing its applicability. Additionally, this report provides a summary of behavioral and eye tracking research involving driving simulators. Other research topics include comparisons between novice and expert drivers’ behavioral patterns, methods for avoiding cybersickness in virtual environments, and a synopsis of current personality measures with respect to job performance and driving performance. This literature review coincides with a study designed to examine the effectiveness of virtual reality snowplow simulator training for current maintenance operators, using the TranSim VS III truck and snowplow simulator recently purchased by the Iowa DOT.

Evaluation of Virtual Reality Snowplow Simulator Training, January 2007

Each winter, Iowa Department of Transportation (Iowa DOT) maintenance operators are responsible for plowing snow off federal and state roads in Iowa. Drivers typically work long shifts under treacherous conditions. In addition to properly navigating the vehicle, drivers are required to operate several plowing mechanisms simultaneously, such as plow controls and salt spreaders. There is little opportunity for practicing these skills in real-world situations. A virtual reality training program would provide operators with the opportunity to practice these skills under realistic yet safe conditions, as well as provide basic training to novice or less-experienced operators. In order to provide such training to snowplow operators in Iowa, the Iowa DOT purchased a snowplow simulator. The Iowa DOT commissioned a study through Iowa State University designed to (1) assess the use of this simulator as a training tool and (2) examine personality and other characteristics associated with being an experienced snowplow operator. The results of this study suggest that Iowa DOT operators of all ages and levels of experience enjoyed and seemed to benefit from virtual reality snowplow simulator training. Simulator sickness ratings were relatively low, implying that the simulator is appropriate for training a wide range of Iowa DOT operators. Many reported that simulator training was the most useful aspect of training for them.

Report on a special investigation of programs administered by the Central Iowa Employment and Training Consortium (CIETC) and Iowa Workforce Development (IWD) for the period July 1, 2003 through December 15, 2005

Report on a special investigation of programs administered by the Central Iowa Employment and Training Consortium (CIETC) and Iowa Workforce Development (IWD) for the period July 1, 2003 through December 15, 2005

Improving Transition Outcomes: CASE (Career And Self Awareness) Prototype: Training Brochure

Promotional brochure for CASE (Career And Self Awareness) curriculum trainings.

Audit of the Indoor Multipurpose Use and Training Facility Revenue Bond Funds of Iowa State University of Science and Technology (Iowa State University) as of and for the year ended June 30, 2007

Audit of the Indoor Multipurpose Use and Training Facility Revenue Bond Funds of Iowa State University of Science and Technology (Iowa State University) as of and for the year ended June 30, 2007

Fiscal Year 2007 Workforce, Business Service Training Annual Report

Annual Report

Audit report on the Central Iowa Employment and Training Consortium (CIETC) for the year ended June 30, 2006

Audit report on the Central Iowa Employment and Training Consortium (CIETC) for the year ended June 30, 2006

Report of the Indoor Multipurpose Use and Training Facility Revenue Bond Funds of Iowa State University of Science and Technology as of and for the year ended June 30, 2008

Report of the Indoor Multipurpose Use and Training Facility Revenue Bond Funds of Iowa State University of Science and Technology as of and for the year ended June 30, 2008

Report on the Iowa Industrial New Jobs Training Program (NJTP) for the period July 1, 2000 through June 30, 2008

Report on the Iowa Industrial New Jobs Training Program (NJTP) for the period July 1, 2000 through June 30, 2008

Tax Credit Evaluation Study: Iowa New Jobs Training Program (260E) Part 1: Background

During the 2005 Legislative Session the Iowa Department of Revenue received an appropriation to establish the Tax Credits Tracking and Analysis Program (TCTAP) to track tax credit awards and claims. In addition, the Department was directed to perform periodic evaluations of tax credit programs. The purpose of these studies is three-fold: (1) To provide a comparison of the Iowa tax credit program to similar federal and other states’ programs (2) To summarize information related to the usage of the Iowa tax credit (3) To evaluate the economic impact of the tax credit program.

Report for Contract Services and Training

Pursuant to SF 2088, Section 64 - the Iowa Department of Transportation submits the Report for Contract Services and Training for Jan. 1, 2011 - June 30, 2011.

From Empirical Bayes to Full Bayes: Methods for Analysing Traffice Safety Data, October 25, 2004

Traffic safety engineers are among the early adopters of Bayesian statistical tools for analyzing crash data. As in many other areas of application, empirical Bayes methods were their first choice, perhaps because they represent an intuitively appealing, yet relatively easy to implement alternative to purely classical approaches. With the enormous progress in numerical methods made in recent years and with the availability of free, easy to use software that permits implementing a fully Bayesian approach, however, there is now ample justification to progress towards fully Bayesian analyses of crash data. The fully Bayesian approach, in particular as implemented via multi-level hierarchical models, has many advantages over the empirical Bayes approach. In a full Bayesian analysis, prior information and all available data are seamlessly integrated into posterior distributions on which practitioners can base their inferences. All uncertainties are thus accounted for in the analyses and there is no need to pre-process data to obtain Safety Performance Functions and other such prior estimates of the effect of covariates on the outcome of interest. In this slight, fully Bayesian methods may well be less costly to implement and may result in safety estimates with more realistic standard errors. In this manuscript, we present the full Bayesian approach to analyzing traffic safety data and focus on highlighting the differences between the empirical Bayes and the full Bayes approaches. We use an illustrative example to discuss a step-by-step Bayesian analysis of the data and to show some of the types of inferences that are possible within the full Bayesian framework.

From Empirical Bayes to Full Bayes: Methods for Analyzubg Traffic Safey Data, October 25, 2004

Traffic safety engineers are among the early adopters of Bayesian statistical tools for analyzing crash data. As in many other areas of application, empirical Bayes methods were their first choice, perhaps because they represent an intuitively appealing, yet relatively easy to implement alternative to purely classical approaches. With the enormous progress in numerical methods made in recent years and with the availability of free, easy to use software that permits implementing a fully Bayesian approach, however, there is now ample justification to progress towards fully Bayesian analyses of crash data. The fully Bayesian approach, in particular as implemented via multi-level hierarchical models, has many advantages over the empirical Bayes approach. In a full Bayesian analysis, prior information and all available data are seamlessly integrated into posterior distributions on which practitioners can base their inferences. All uncertainties are thus accounted for in the analyses and there is no need to pre-process data to obtain Safety Performance Functions and other such prior estimates of the effect of covariates on the outcome of interest. In this light, fully Bayesian methods may well be less costly to implement and may result in safety estimates with more realistic standard errors. In this manuscript, we present the full Bayesian approach to analyzing traffic safety data and focus on highlighting the differences between the empirical Bayes and the full Bayes approaches. We use an illustrative example to discuss a step-by-step Bayesian analysis of the data and to show some of the types of inferences that are possible within the full Bayesian framework.

Task 4: Testing Iowa Portland Cement Concrete Mixtures for the AASHTO Mechanistic-Empirical Pavement Design Procedure, May 2008

The present research project was designed to identify the typical Iowa material input values that are required by the Mechanistic-Empirical Pavement Design Guide (MEPDG) for the Level 3 concrete pavement design. It was also designed to investigate the existing equations that might be used to predict Iowa pavement concrete for the Level 2 pavement design. In this project, over 20,000 data were collected from the Iowa Department of Transportation (DOT) and other sources. These data, most of which were concrete compressive strength, slump, air content, and unit weight data, were synthesized and their statistical parameters (such as the mean values and standard variations) were analyzed. Based on the analyses, the typical input values of Iowa pavement concrete, such as 28-day compressive strength (f’c), splitting tensile strength (fsp), elastic modulus (Ec), and modulus of rupture (MOR), were evaluated. The study indicates that the 28-day MOR of Iowa concrete is 646 + 51 psi, very close to the MEPDG default value (650 psi). The 28-day Ec of Iowa concrete (based only on two available data of the Iowa Curling and Warping project) is 4.82 + 0.28x106 psi, which is quite different from the MEPDG default value (3.93 x106 psi); therefore, the researchers recommend re-evaluating after more Iowa test data become available. The drying shrinkage (εc) of a typical Iowa concrete (C-3WR-C20 mix) was tested at Concrete Technology Laboratory (CTL). The test results show that the ultimate shrinkage of the concrete is about 454 microstrain and the time for the concrete to reach 50% of ultimate shrinkage is at 32 days; both of these values are very close to the MEPDG default values. The comparison of the Iowa test data and the MEPDG default values, as well as the recommendations on the input values to be used in MEPDG for Iowa PCC pavement design, are summarized in Table 20 of this report. The available equations for predicting the above-mentioned concrete properties were also assembled. The validity of these equations for Iowa concrete materials was examined. Multiple-parameters nonlinear regression analyses, along with the artificial neural network (ANN) method, were employed to investigate the relationships among Iowa concrete material properties and to modify the existing equations so as to be suitable for Iowa concrete materials. However, due to lack of necessary data sets, the relationships between Iowa concrete properties were established based on the limited data from CP Tech Center’s projects and ISU classes only. The researchers suggest that the resulting relationships be used by Iowa pavement design engineers as references only. The present study furthermore indicates that appropriately documenting concrete properties, including flexural strength, elastic modulus, and information on concrete mix design, is essential for updating the typical Iowa material input values and providing rational prediction equations for concrete pavement design in the future.

Iowa DOT GeoMedia Training Manual,1998.

The varying title of this manual is : Coordinated Transportation Analysis and Management System. It gives instructions on how to use GeoMedia in order to integrate data from multiple sources and formats into one environment, perform sophisticated queries and spatial analyses, and quickly produce complex maps.