Methodology for Conflating Large-Scale Roadway Networks

Abstract

Transportation agencies are increasingly integrating third-party traffic data into their core business function areas such as system performance monitoring, project programming, traffic incident management, and safety analysis. However, linking private-sector data with agency asset inventory data has been a major challenge because the networks typically have different referencing systems, segmentation schemes, and representations of travel directions. This paper presents an effective conflation algorithm that associates spatial features between large-scale road networks. Instead of breaking lines into smaller pieces, which is a common technique in transportation applications, we use an intersection-based approach that leverages the inherent topological similarities between networks. The underlying uncertainty and imprecision in network geometries and road names are addressed through application of a fuzzy logic inference technique. We then implement an effective mechanism to handle differences in representations of divided roadways and travel directions in the two networks. The algorithm was tested on Kentucky statewide roadway networks and achieved a matching accuracy of over 99%. This approach has been successfully applied by the Kentucky Transportation Cabinet in its project identification and prioritization process.