Turning movement count data; that is, vehicle volumes broken down by movement, approach, and time period, are the foundation of signal performance evaluations and a crucial component of data-driven decision-making processes used by transportation agencies. Unfortunately, the availability of quality turning movement count data is arguably not the norm for agencies. In fact, the 2012 National Traffic Signal Report Card conducted by the National Transportation Operations Coalition identified traffic monitoring and data collection practices in the United States as weak by giving the practices an “F” grade. To this day, some of the current practices rely on manual procedures that limit the amount of data available. Automated methods can be temporarily installed at an intersection, but these are intended to improve on the traditional manual counts used and not to produce continuous count volumes. Permanent counting systems are unable to classify vehicles into their corresponding movements on shared lanes unless supplemental infrastructure is installed or additional count zones are defined. As part of this NCHRP IDEA Stage 1 project the research team has shown that an algorithm that produces turning movement counts reports using vehicle trajectory data extracted from existing vehicle detection infrastructure can be created. The algorithm developed in this NCHRP IDEA project differs from existing approaches in that it does not rely on count zones on exit approaches or the use of time stamps of detection calls. As a proof of concept, the algorithm has shown significant promise in terms of performance at typical intersections. While changes to the algorithm are needed, results obtained are encouraging and can be used by those familiar with data analysis and collection techniques.