Development of an intelligent technique for traffic network incident detection
Automated incident detection and alternative path planning form important activities within a modern expressway traffic management system which aims to ensure a smooth flow of traffic along expressways. This is done by adopting efficient technologies and processes that can be directly applied for the automated detection of non-recurrent congestion, the formulation of response strategies, and the use of management techniques to suggest alternative routes to the road-users, resulting in significant overall reductions in both congestion and inconvenience to motorists. A delicate balance has to be struck here between the incident detection rate and the false-alarm rate. This paper presents the development of a hybrid artificial intelligence technique for automatically detecting incidents on a traffic network. The overall framework, algorithm development, implementation and evaluation of this hybrid fuzzy-logic genetic-algorithm technique are discussed in the paper. 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 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 algorithms, on the other hand, enable the fuzzy design parameters to be optimized to achieve optimal performance. The results obtained for the traffic network give a high detection rate of 70.0%, while giving a low false-alarm rate of 0.83%. A comparison between this approach and four other incident-detection algorithms demonstrates the superiority of this approach.
incident detection genetic algorithms hybrid ai systems fuzzy logic traffic engineering