A Graph-Based Approach to Conflating State Department of Transport and Probe Data Vendor Roadway Networks of Varying Resolution

Probe vehicle data has become increasingly prevalent in transportation applications over the last two decades. Despite its widespread use, many transportation agencies struggle to integrate this data with their existing data sets because of disparities between the proprietary roadway networks used by data vendors and those used by the agencies themselves. This paper presents a fully automated and efficient conflation process to address the challenge of conflating third-party probe data networks with the state Department of Transport Linear Referencing System networks. The process leverages graph data structures to effectively account for network connectivity and link directionality, which have been a challenge for existing conflation methods. In addition, it incorporates new spatial metrics to enhance matching accuracy. The process is developed in Python, which ensures full transparency, traceability, and reproducibility. It successfully conflates the Kentucky Transportation Cabinet’s all roads network with the high-resolution HERE network in under 75 min, achieving a substantial reduction in execution time compared with the previous method. The process also conflates the Traffic Message Channel network covering the National Highway System roads in Kentucky in just 3 min. A comprehensive review of conflation results demonstrates its accuracy, particularly in complex areas such as interstate junction areas, double-crossover diamond interchanges, and roundabouts. This paper provides transportation agencies with a robust tool for integrating disparate roadway networks, allowing them to better utilize third-party probe data in transportation applications.