Kelvin R Santiago-Chaparro

Development of Data Collection and Integration Framework for Road Inventory Data

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.

Application and Integration of Lattice Data Analysis, Network K-Functions, and Geographic Information System Software to Study Ice-Related Crashes

Advances in geographic information system (GIS) software and exploratory spatial data analysis (ESDA) techniques give transportation safety engineers tools to observe and analyze safety-related data from a new perspective. This research takes the use of GIS software and ESDA techniques one step further by incorporating advanced statistical techniques for a more thorough and complex analysis of safety data. This is achieved by implementing a network-constrained cross K-function to analyze the relationship between bridges and the occurrences of ice-related crashes within a county. The counties in Wisconsin included in the analysis were selected through the use of the local Morans I statistic; this statistic allows for the selection of counties within the same geographical area, which have similar parameters (in this case, ice-related crash rates). The objective of this research is to explore the relationship between ice-related crashes and bridges in counties that display similar ice-related crash rates, to compare and analyze winter maintenance techniques. The results identify clustering of ice-related crashes around bridges in four counties with similar ice-related crash rates in southeast Wisconsin. Similarly, two of four counties show clustering of ice-related crashes around bridges in northwest Wisconsin. These results make a strong case to suggest that counties in these regions should focus additional winter maintenance efforts at bridge locations. In addition, this research shows how the use of advanced spatial statistical techniques, particularly network-based statistics applied within a GIS environment, can be used as a unique and innovative approach toward safety data analysis.

Spatial effectiveness of speed feedback signs

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.

Evaluation of Performance of Automatic Vehicle Location and TowPlow for Winter Maintenance Operations in Wisconsin

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

Proposed Safety Index Based on Risk-Taking Behavior of Drivers

46 million to

Automated Turning Movement Counts for Shared Lanes: Leveraging Vehicle Detection Data

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.

Automatic network-level identification of sight distance values from existing datasets

A new safety indicator takes into consideration the risk-taking behavior of drivers as well as the prevailing traffic conditions at an intersection. The indicator is based on the idea that an intersection at which drivers are willing to take a higher risk is not as safe as one at which drivers are not willing to take high risks. Driver risk-taking behavior is modeled as a function of a drivers reaction to a possible collision scenario. Binary logistic regression was used to understand how the probability of a driver reacting to a possible collision scenario changes as a function of the variables defining the scenario. The data collection and safety index definition are presented from the perspective of permissive left turns; however, the concept of risk taking is universal; thus it is a feasible alternative for other maneuver types if appropriate data are obtained. Use of a safety index based on risk taking helps solve the engineers dilemma of which of two intersections that have no crash history, or that have equal crash history, should be the target of a safety improvement program. The methodology presented can remove the subjective judgment that often takes place in such a scenario and provides the engineer with an objective alternative.

TRAFFIC DEMAND DYNAMICS DURING URBAN FREEWAY SHORT-TERM LANE CLOSURES

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.

Expanding the capabilities of existing vehicle detection infrastructure to monitor red light running

Abstract Many local, state, and national highways were built years before computer-based tools like geographic information systems (GISs) and computer aided design were available. Therefore, keeping track of existing roadways design parameters is a difficult task since the characteristics of those design are not easily available through modern day GIS and computer aided design-based datasets. Those technologies have become the standard method of information management in agencies and have replaced old paper-based methodologies. As a result, for some of the existing infrastructure, the only information available to decision makers is the location of the highway centerline, along with other asset management information such as shoulder presence, pavement type, and roadside features. The lack of information in GIS datasets about basic design characteristics, such as radius and centerline elevation, means that field surveys are required as the only available method to determine if a highway meets the latest design standards and guidelines. Frequently, field procedures necessary to collect existing geometric data can be not only labor-intensive, but also cost-prohibitive, especially in times of economic constraints. This paper focuses on the use of existing photographic logs commonly created and managed by transportation agencies to automate the process of computing the sight distance available, along an entire route. While the analysis presented is focused on identifying road segments in need of a no-passing zone, results from the methodology discussed can also be used to identify segments where advisory speeds need to be established, as well as those segments where posted speeds should be increased/decreased in order to improve safety. Through the application of the methods presented in this paper, the authors demonstrate how value can be added to existing datasets that were originally collected for completely different purposes such as the creation of a sign inventory.