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.