Hybrid fuzzy logic-genetic algorithm technique for automated detection of traffic incidents on freeways

Incident detection has become an important and sophisticated task in today´s complex engineering environment, and is one of the key functions of modern traffic management systems. The incidents, depending on their severity, affect the traffic pattern on expressways and cause congestion. This paper presents a hybrid AI approach for immediate and automatic detection of traffic incidents on expressways. Specifically, a hybrid combination of fuzzy logic and genetic algorithm (GA) has been applied to automatically detect incidents on a traffic network. The flexible and robust nature of the developed fuzzy controller allows it to model functions of arbitrary complexity while at the same time being inherently highly tolerant of imprecise data. The maximizing capabilities of genetic algorithm, on the other hand, enable the fuzzy design parameters to be optimized to achieve optimal performance. A cascaded framework of 11 fuzzy controllers takes in traffic indices such as occupancy and volume to detect incidents along an expressway in California. The results obtained from this hybrid model demonstrate the superiority of this approach when compared with two commonly used conventional incident detection algorithms