基于检测器脉冲数据的高速公路事件自动检测算法研究

提出了一种全新的高速公路事件自动检测算法,直接利用车辆检测器输出的脉冲宽度和脉冲间隔作为参数,运用LVQ神经网络对获得的脉冲宽度数据和脉冲间隔数据进行处理来判断是否有事件发生,从而大大减少了检测时间,为交通事件的快速处理提供了可靠的依据。此外,该算法利用神经网络的自学习能力,可以很好地确定各条道路发生交通事件的门限值。仿真结果表明:该算法具有较高的事件检测率(约为97%)、较短的检测时间和较低的误警率(约为0 41%),具有很好的应用前景。

Freeway incident detection algorithm based on pulse data of loop detector

Authors attempt to develop a new freeway incident detection algorithm that uses the data of pulse length and pulse gap from vehicle detectors as parameters and apply LVQ neural network to process the data to determine if an incident occurs.This algorithm reduces greatly incident detection time, so it offers a reliable basis to rapidly process the traffic incidents. Meanwhile,the algorithm can make use of the self-learning ability of neural network to determine the different thresholds for various freeways. At last,as the simulation results show,the new algorithm for incident detection has a lower false alarm rate(about 0.41%),a faster detection speed and a higher detection rate(about 97%). It is found to be potentially applicable in practice.