The US Department of Transportation (US-DOT) estimates that over half of all congestion events are caused by highway incidents rather than by rush-hour traffic in big cities. Real-time incident detection on freeways is an important part of any modern traffic control center operation because it offers an opportunity to maximize road system performance. An effective incident detection and management operation cannot prevent incidents, however, it can diminish the impacts of non-recurring congestion problems. The main purpose of real-time incident detection is to reduce delay and the number of secondary accidents, and to improve safety and travel information during unusual traffic conditions. The purpose of this project is to evaluate two recently developed automatic incident detection algorithms. The majority of automatic incident detection algorithms are focused on identifying traffic incident patterns but may not adequately investigate possible similarities in patterns observed under incident-free conditions. When traffic demand exceeds road capacity, the traffic speed decreases significantly and the traffic enters a highly unstable regime often referred to as “stop-and-go” conditions. The most challenging part of real-time incident detection is recognition of traffic pattern changes when incidents happen during stop-and-go conditions. This work describes a case study evaluation of two recently evolved incident detection methods using data from the Dallas, TX traffic control center.