Development of Data Collection and Integration Framework for Road Inventory Data

Abstract

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.

Safety Evaluation of Horizontal Curves on Rural Undivided Roads

“…The data were collected on the Wisconsin state trunk network (STN) from the Wisconsin DOT Photolog data set, which has a scale of 0.01 mi (52.8 ft), by using an automated algorithm in a geographic information system (GIS) environment (24). The data were mapped with the Photolog lane mile routes, which were created to enable the integration of Photolog-based data with other Wisconsin DOT GIS databases (25).…”

Do Crash Barriers and Fences Have an Impact on Wildlife–Vehicle Collisions?—An Artificial Intelligence and GIS-Based Analysis

“…Therefore, de Frutos et al suggested to use smartphone-based cameras and apps for video inspections [19], an alternative option to the more expensive terrestrial laser scanning [20]. Besides, process chains to collect and evaluate image data using GIS-based handling of camera-based material and frames have been recommended [21]. The extraction of infrastructure data can be achieved using computer vision, which was already recommended for camera-based road inspections, but mainly applied for pavement analysis and road sign detection [22].…”

Horizontal Curves, Signs, and Safety

“…The horizontal curve data included attribute information, such as radius, degree of curvature, length, route, county, and the mile marker for the start and end point of each curve. The data were mapped with the photo log lane mile routes, which were created to enable the integration of photo log-based data with other Wisconsin DOT geographic information system (GIS) databases (22). For data analysis, the horizontal curve location was selected as the spatial unit of analysis for which other data elements and attributes would be collected and assembled.…”

“…However, the data are not readily integrated with the Wisconsin DOT STN database. Therefore, the Sign View data were mapped with the photo log lane mile routes to enable integration with the other data set (22). For the purpose of this research, the focus was only on turn (W1-1) and curve (W1-2) signs to explore the relationship between the use of the two signs and safety at horizontal curves.…”