Traffic Conflict Studies

Manual TMCs require technicians to physically count every vehicle passing through an intersection, which makes the process both time-consuming and limited in scope. Due to the labor-intensive nature and high cost, manual counts are typically conducted only once or twice a year, and usually cover a few peak hours rather than a full day or multiple days. As a result, it’s not feasible to capture turning movement data for all intersections or time periods using manual methods alone.

This limited data collection often leads to gaps in understanding real-world travel patterns. When agencies attempt to extrapolate short-term data into monthly or annual projections, the results can be unreliable and less representative of actual traffic behavior.

To overcome these limitations, many agencies are adopting automated data collection systems. Tools like pneumatic rubber tubes are commonly used for vehicle detection on straight road segments. However, these systems are less effective at intersections due to angled approaches—vehicles may strike the sensors inconsistently, leading to inaccurate counts. Additionally, the cost of installing such equipment at every intersection is often prohibitive.

This is where Big Data analytics is transforming TMC collection. By leveraging anonymized location pings from connected vehicles, smartphones, and IoT devices—combined with digital road network maps and demographic data—agencies can access continuous, comprehensive turning movement analytics. These systems provide 24/7 data coverage for virtually any intersection, including both signalized and unsignalized ones.

For instance, platforms like StreetLight’s TMC offer scalable, on-demand traffic counts across the United States and Canada—without requiring physical infrastructure or manual labor. This modern approach reduces costs, improves accuracy, and enables data-driven decisions for transportation planning and traffic management.