REAL-TIME ADAPTIVE ONLINE TRAFFIC INCIDENT DETECTION

A new approach to traffic incident detection is proposed in this paper . The method consists of two stages. First, a real-time adaptive on-li ne procedure is used to extract the significant components of traffic states, namely, average velocity and density of moving vehicles. Secon d, we apply a new neural network called Fuzzy CMAC (Cerebellar Arithme tic Computer) to identify traffic incidents. Fuzzy CMAC is an ideal ca ndidate for this purpose for the following reasons. First, the Fuzzy C MAC learning structure is a creative use of fuzzy logic and CMAC based neural networks. Expert knowledge in terms of linguistic rules can be incorporated into the design. Second, the learning process is well su ited for real-time application since the training process is an order of magnitude faster than conventional neural nets. Third, the Fuzzy CM AC can be implemented in high speed, highly parallel hardware. The imp ortance of this research is three-fold. One is that a good traffic inc ident detection system will help drivers to select an optimum route. T he second one is that the system will be able to provide information f or efficient dispatching of emergency services. Lastly, it will provid e accurate knowledge of existing traffic conditions in order to guide effective on-line traffic controls. (C) 1998 Elsevier Science B.V.