摘要
为了及时检测出高速公路上发生的交通事件,减少由于交通事件带来的损失,提出了一种基于遗传优化的BP_AdaBoost算法用于交通事件检测.提取高速公路上下游的车流量、车速与占有率作为BP(back propagation)神经网络的输入值,利用遗传算法全局搜索的性能优化BP神经网络初始连接权值和输出阈值,再通过多个新的BP神经网络弱分类器构建成AdaBoost强分类器,设计基于遗传算法优化BP_AdaBoost算法的交通事件分类器.以在东京高速公路采集的真实数据进行性能验证,试验结果表明,该算法可以提高BP弱分类器的性能,检测率达到97%,误报率降至3.34%,适用于高速公路交通事件的检测.
In order to detect the traffic incidents occurred on highway and reduce the loss brought by traffic incident, this paper presents an improved BP_AdaBoost algorithm based on genetic algorithm for traffic incident detection. The inputs of BP （Back Propagation）neural network value are vehicle quantity, velocity and occupancy in upstream and downstream of highway. Genetic algorithm is used for each BP neural network classification model for optimizing weights and thresholds due to its performance of global searching. Theoptimized BP neural network model is applied as a new weak classifier, then through the AdaBoost algorithm, many of these new weak classifier is composed as strong classifier model. This improved algorithm is validated with real data from Tokyo expressway ultra-sonic sensors. The experimental results show that the algorithm can improve the performance of BP weak classifier. The detection rate of improved BP_ AdaBoost algorithm is up to 97%, and false alarm rate is lower to 3.34%. Experiment indicate that the algorithm is suitable for detecting highway traffic incidents.
出处
《同济大学学报（自然科学版）》
EI
CAS
CSCD
北大核心
2015年第12期1829-1833,共5页
Journal of Tongji University:Natural Science
基金
国家"八六三"高技术研究发展计划(2013AA12A206)
国家自然科学基金(51008143)
江苏省高校自然科学基金(14KJD520002)
