Study On Freeway Traffic Incident Detection Method Based On Multi-Sensor Information Fusion Technology

With the rapid development of freeway, it is urgent to study better AID methods, because Traffic Safety in freeways is more and more obvious and existing AID systems are unsatisfactory in practical application. As a new kind of comprehensive information processing method, multi-sensor information fusion technology provides a new way to design AID methods. And, designing a better method with it will not only has academic value in broadening the scope of application of information fusion technology, but also has practical application value in promoting the realization of real safe and efficient management target of freeways.This thesis preliminarily study the traffic incident information fusion theory.And then based on analyzing the problems of existing AID algorithms, applying multi-sensor information fusion theory and technology, and according to its system structure and levels, this thesis designs the AID method of freeway based on feature-level fusion with centralized structure, and the AID method of freeway based on decision-level fusion with distributed structure. According to the process of information fusion, on data low-grade pretreatment stage, this thesis utilizes wavelet theory for processing the data collected by loop vehicle detector and compares the denoising effect of commonly used two methods. In data high-grade processing stage, this thesis chooses PNN and LVQ Neural Network to realize AID method modules. Besides, based on analyzing the mutation characteristics of traffic flow of different cross-sections and different lanes in up and down stream when the traffic incident occurs in detail through real traffic data, the input parameters of these two levels fusion methods are designed, and during the course, feature-level fusion method absorb single-section algorithm and double-section algorithm. And these two methods both fuse weather and visibility information.Finally, this thesis uses real traffic data to verify the effect of the two algorithms proposed, and the results show that they both have higher detection rate, lower false alarm rate and better performances than some traditional AID methods.