Speed feedback signs (SFS), also known as dynamic speed displays, provide drivers with feedback about their speed in relationship to the posted speed limit. When appropriately complemented with police enforcement, SFS can be an effective method for reducing speeds at a desired location. However, as reported in the literature, effectiveness of SFS is limited not only in regard to time after the deployment but also for distance. Therefore, a need exists to understand how far upstream and downstream of the SFS speed reductions are maintained. Through a unique data collection methodology, researchers obtained trajectories of free-flowing vehicles that approached an SFS, as well as trajectories of vehicles receding from the SFS. Trajectory data were used by researchers to determine the locations at which drivers willing to reduce their speed when approaching the SFS actually started the reduction. Downstream of the SFS, the distance at which drivers started increasing their speed after complying with the sign was also determined. Results showed the feasibility of determining the spatial effectiveness of SFS. By using the methods as presented, speed enforcement personnel can understand how drivers in an area of interest react to SFS and therefore can determine the best locations for SFS as well as the number of SFS that need to be deployed to achieve a speed reduction over a segment of road.

The availability and quality of transportation data is a cornerstone of any data-driven program. There is a continuous need to identify and develop alternative, reliable, and inexpensive sources of data and efficient and robust integration techniques. This research presents an innovative cost-effective application to collect geographic information system (GIS)–compatible data from image-based databases. Road inventory data on guardrail end-type locations along with other road features on more than 8,000 mi of Wisconsin State Trunk Network highways were collected. Data collected from image-based sources with Global Positioning System coordinates presented the familiar problem of spatial mismatch. A framework was developed based on the principles of dynamic segmentation to integrate the data and resolve the spatial mismatch problem. The principles of dynamic segmentation and route calibration are well established in literature. However, there were no specific examples of a framework that created a workable program and addressed issues pertaining to practical solutions for statewide data. The framework developed presents an efficient and automated solution for data integration, which is applicable to any relevant data set. A quantitative assessment of the performance of the data collection and map-matching procedures was conducted to assess the results. The results showed that road features collected from the image-based data sets were located within an average distance of 6 to 7 m of their location on the Wisconsin Department of Transportation GIS base maps, which were highly accurate, given the limitations of the data sets.

Understanding how vehicle drivers and pedestrians interact is key to identifying countermeasures that improve the safety of the interactions. As a result, techniques that can be used to evaluate the effectiveness of safety countermeasures and traffic control devices without the need to wait for the availability of crash data are needed. Using video, the interactions between right-turning vehicles and conflicting pedestrians were documented and quantified using vehicle and pedestrian position timestamps. Interactions documented were purposely narrow in scope to obtain a controlled dataset. Logged timestamps enabled the calculation of values such as time to complete a right turn and time for a pedestrian to reach a critical conflict point when a vehicle initiated a right turn. A nonprobabilistic regression model explaining the relationship between the calculated values was created. The model described the expected behavior of right-turning drivers: when drivers perceive the possibility of a pedestrian reaching a critical conflict point at the same time as them, they will modify their behavior, even if not coming to a complete stop. This behavior is not a surprise and has been previously documented in the literature. The primary contribution of this research is demonstrating that by analyzing a narrow set of interactions, clear and simple models that mostly explain the interactions between right-turning vehicles and pedestrians can be developed using nonprobabilistic linear regression techniques. An argument is made that the model parameters can be used to evaluate the effectiveness of traffic control devices.

Turning movement count data (i.e., vehicle volumes broken down by movement, approach, and time periods) are the foundation of signal performance evaluations and a crucial component of a data-driven decision-making process used by transportation agencies. In this paper, the authors show how data available from intersections equipped with radar-based vehicle detection can be used to produce turning movement counts. A classification algorithm developed and discussed by the authors is capable of producing turning movement counts regardless of lane configuration and without the need for definition of detection zones. The algorithm works by using the data produced by vehicle detection systems that go unused and are never communicated to the signal controller. The nature of the data collection process makes the algorithm independent of the controller used. Results from the algorithm are promising; an average error of −0.26 vehicles per 15-min count period (absolute error of 2.31 vehicles) was obtained with the algorithm. Furthermore, the application of the algorithm provides an opportunity beyond signal operations. Processed trajectory data and results from the algorithm could be used to break the boundary that often exists between operations, planning, and safety, and thus show how a monitoring system that relies on the algorithm could help a traffic monitoring program meet the different—and sometimes competing—interests of agencies.

Winter maintenance operations are a major expense for state departments of transportation located within the Snow Belt of North America. Winter maintenance-related expenses for 2005 through 2010 ranged from $46 million to $87 million per year for state highways in Wisconsin. During the past two winters, the Wisconsin Department of Transportation implemented TowPlow and automatic vehicle location (AVL) technologies to optimize winter maintenance operations. A TowPlow is a plow that is attached to a regular plow truck to increase the snow removal capacity. AVL is a combination of systems capable of monitoring the location of a vehicle, material application rates, and road conditions from a central location. In this paper, qualitative and quantitative evaluations are presented for these two technologies. Findings from both evaluations showed that the implementation of these technologies would result in potential cost savings resulting from lower salt usage (AVL) and more efficient operations (TowPlow). The use of a TowPlow to perform the same task as a regular plow truck resulted in 32% to 43% operational cost savings. Implementation challenges, maintenance issues, and reduction in salt usage by counties that implemented AVL were evaluated. Implementation of AVL resulted in about 6% savings in salt usage from increased plow operator compliance with guidelines. When only the savings in salt usage and none of the intangible benefits were considered, the benefit–cost ratio values ranged from 1.05 to 1.89 depending on the cost of salt and percentage of reduction in salt usage.