交通事件小波神经网络算法的探讨

Study of the Wavelet and Neural Network Algorithm in Traffic Incident Detection

回顾了传统的交通事件检测的算法,结合当前世界流行的检测方法,提出利用小波分析和神经网络进行交通事件的检测的方法,并详细介绍了事件检测的流程图和算法的思想。与传统算法相比较,小波分析和神经网络结合用于交通事件检测的算法具有检测率高、误报率低和检测时间短等优点,但同时也指出了其不足之处,为以后进一步的研究提供了方向。

An updated detection method based on wavelet analysis and neural network is proposed on the basis of a brief review of several traditional algorithms applied in traffic incident detection and study of contemporary international detection methods. Both flow chart of traffic detection and the rationale of algorithm are introduced in details. The integrated performance of wavelets analysis and neural networks applied in traffic incident detection shows its advantages compared with traditional algorithms in many aspects, such as its higher detection rate, lower false alarm rate and shorter mean detection time. Whereas, defects of this algorithm are pointed out, offering an access to further researches.