Evaluation of Third-Party Turning-Movement-Count Data: A Kentucky Case Study

Turning-movement counts (TMCs) are among the fundamental datasets in the transportation industry. This type of traffic count is regularly conducted and utilized as a core input to intersection design, signal timing, and more. However, traditional methods for collecting this data are expensive, labor-intensive, and time-consuming. Thus, the data collected from specific locations within short time periods does not necessarily capture all traffic variation. In recent years, advancements in tracking technologies have enabled third-party vendors to passively generate TMCs for different locations and time intervals. This study primarily focuses on evaluating TMCs estimated by a third-party vendor through comparison with ground-truth data. Actual TMCs are obtained from manually collected data for several planning studies across Kentucky between 2016 and 2024. Moreover, estimated counts are acquired from the StreetLight platform for these same locations. TMCs are evaluated across different functional classifications (FCs) and area types to assess their impact on the StreetLight data accuracy. The overall comparison of TMCs yields Pearson correlation coefficients greater than 0.75 and mean absolute percentage errors (MAPE) greater than 40%. When analyzed by area type and FC, urban intersections with higher functional importance show stronger agreement with observed counts than rural intersections show. The analysis suggests that variations in annual average daily traffic (AADT) had a measurable impact on estimation performance, with errors generally tending to decrease as AADT increases. Results of this study will help provide transportation agencies with a better understanding of the accuracy and utility of estimated data from third-party vendors, specifically TMCs.