Effectiveness of Dynamic Messaging on Driver Behavior for Late Merge Lane Road Closures, 2009

Efforts to improve safety and traffic flow through merge areas on high volume/high speed roadways have included early merge and late merge concepts and several studies of the effectiveness of these concepts, many using Intelligent Transportation Systems for implementation. The Iowa Department of Transportation (Iowa DOT) planned to employ a system of dynamic message signs (DMS) to enhance standard temporary traffic control for lane closures and traffic merges at two bridge construction projects in western Iowa (Adair County and Cass County counties) on I-80 during the 2008 construction season. To evaluate the DMS system’s effectiveness for impacting driver merging actions, the Iowa DOT contracted with Iowa State University’s Center for Transportation Research and Education to perform the evaluation and make recommendations for future use of this system based on the results. Data were collected over four weekends, beginning August 1–4 and ending October 16–20, 2008. Two weekends yielded sufficient data for evaluation, one of transition traffic flow and the other with a period of congestion. For both of these periods, a statistical review of collected data did not indicate a significant impact on driver merging actions when the DMS messaging was activated as compared to free flow conditions with no messaging. Collection of relevant project data proved to be problematic for several reasons. In addition to personnel safety issues associated with the placement and retrieval of counting devices on a high speed roadway, unsatisfactory equipment performance and insufficient congestion to activate the DMS messaging hampered efforts. A review of the data that was collected revealed different results taken by the tube counters compared to the older model plate counters. Although variations were not significant from a practical standpoint, a statistical evaluation showed that the data, including volumes, speeds, and classifications from the two sources were not comparable at a 95% level of confidence. Comparison of data from the Iowa DOT’s automated traffic recorders (ATRs) in the area also suggested variations in results from these data collection systems. Additional comparison studies were recommended.

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.

Work Zone Simulation Model, 1999

To support the analysis of driver behavior at rural freeway work zone lane closure merge points, Center for Transportation Research and Education staff collected traffic data at merge areas using video image processing technology. The collection of data and the calculation of the capacity of lane closures are reported in a companion report, "Traffic Management Strategies for Merge Areas in Rural Interstate Work Zones". These data are used in the work reported in this document and are used to calibrate a microscopic simulation model of a typical, Iowa rural freeway lane closure. The model developed is a high fidelity computer simulation with an animation interface. It simulates traffic operations at a work zone lane closure. This model enables traffic engineers to visually demonstrate the forecasted delay that is likely to result when freeway reconstruction makes it necessary to close freeway lanes. Further, the model is also sensitive to variations in driver behavior and is used to test the impact of slow moving vehicles and other driver behaviors. This report consists of two parts. The first part describes the development of the work zone simulation model. The simulation analysis is calibrated and verified through data collected at a work zone in Interstate Highway 80 in Scott County, Iowa. The second part is a user's manual for the simulation model, which is provided to assist users with its set up and operation. No prior computer programming skills are required to use the simulation model.

Evaluating the Relationship between the Driver and Roadway to Address Rural Intersection Safety using the SHRP 2 Naturalistic Driving Study Data, RB05-013, 2016

Rural intersections account for 30% of crashes in rural areas and 6% of all fatal crashes, representing a significant but poorly understood safety problem. Transportation agencies have traditionally implemented countermeasures to address rural intersection crashes but frequently do not understand the dynamic interaction between the driver and roadway and the driver factors leading to these types of crashes. The Second Strategic Highway Research Program (SHRP 2) conducted a large-scale naturalistic driving study (NDS) using instrumented vehicles. The study has provided a significant amount of on-road driving data for a range of drivers. The present study utilizes the SHRP 2 NDS data as well as SHRP 2 Roadway Information Database (RID) data to observe driver behavior at rural intersections first hand using video, vehicle kinematics, and roadway data to determine how roadway, driver, environmental, and vehicle factors interact to affect driver safety at rural intersections. A model of driver braking behavior was developed using a dataset of vehicle activity traces for several rural stop-controlled intersections. The model was developed using the point at which a driver reacts to the upcoming intersection by initiating braking as its dependent variable, with the driver’s age, type and direction of turning movement, and countermeasure presence as independent variables. Countermeasures such as on-pavement signing and overhead flashing beacons were found to increase the braking point distance, a finding that provides insight into the countermeasures’ effect on safety at rural intersections. The results of this model can lead to better roadway design, more informed selection of traffic control and countermeasures, and targeted information that can inform policy decisions. Additionally, a model of gap acceptance was attempted but was ultimately not developed due to the small size of the dataset. However, a protocol for data reduction for a gap acceptance model was determined. This protocol can be utilized in future studies to develop a gap acceptance model that would provide additional insight into the roadway, vehicle, environmental, and driver factors that play a role in whether a driver accepts or rejects a gap.