【作者】 胡丹龙；

【导师】 余立建；

【作者基本信息】 西南交通大学 ， 交通信息工程及控制， 2013， 硕士

【摘要】 近些年,国家大力发展交通运输事业,高速公路领域发展十分迅猛,到2012年底,我国高速公路通车里程达到了96000公里,超越了美国成为了世界最大规模的高速公路系统,但同时也因为高速路车流量的增大,交通拥挤、事故发生的几率增大,极大的影响了高速公路的通行能力,降低了运行效率,甚至造成人员伤亡财产损失等严重后果。如何快速准确检测出交通事件,尽可能做到有效的改善高速公路通行能力,是目前普遍关注的一个问题。其中高速公路交通事件检测算法对交通事件的检测效率有着直接影响,因此研究高速公路交通事件检测算法有着十分重要的意义。本文说明了高速路交通流特性以及交通事件的检测原理,通过分析支持向量机应用到高速路交通事件检测中的可行性,设计了基于SVM-AID模型,针对SVM参数选择上过于依赖人为经验等问题,采用PSO优化算法对支持向量机参数进行选优,并且对PSO算法进行了改进,通过调整惯性权重、动态选择加速常数等改进策略,取得了不错的检测效果。其次,本论文在第五章中将混沌与粒子群优化算法相结合,利用混沌具有的遍历性、随机性等特点,能够更好的跳出局部最小,达到全局最优；构建出了基于CPSO-SVM的高速公路交通事件检测模型,利用该模型进行交通事件检测。本文的实验数据来自于1-880交通流数据库,分别构建了训练数据集和测试数据集作为论文的实验数据。仿真软件采用MATLAB7.0。通过对实验结果的分析得知,采用混沌优化的粒子群优化支持向量机算法在具有较高的分类精度、检测率,并且误检率较低,能够在高速路交通事件检测中取得很好的检测效果。更多还原

【Abstract】 In recent years, the state has vigorously the development of the transportation industry, the highway is very rapid developments in the field, by the end of2012, Chinese highway mileage has reached96,000kilometers, beyond the United States to become the world’s largest highway system. Highway development has brought many economic benefits for the community, but also because of highway traffic volume increases, traffic congestion, increased the chance of accidents, great highway capacity, reduce operating efficiency, or even cause casualties and property damage and other serious consequences. How to quickly and accurately detect the traffic incident as much as possible to improve highway capacity is the current issue of universal concern. Quality of highway traffic incident detection algorithm directly affect the efficiency of the traffic incident detection, and therefore of great significance to study the highway traffic incident detection algorithm.Highway traffic flow characteristics as well as the basic principles of the traffic incident detection analysis, by highway traffic incident detection in adaptive analysis applied to the support vector machine, to build a model based on SVM-AID, simulation experiment for SVM based on experience, the choice of the model parameters using PSO optimization algorithm and PSO algorithm has been improved by adjusting the inertia weight dynamically select the acceleration constant improvement strategies, and achieved good detection effect. Secondly, in this paper, in the fifth chapter chaotic combination with particle swarm optimization algorithm, using the chaos’s traversal property, randomness and other characteristics, and better able to escape from the local minimum, the globally optimal; build out based on CPSO-SVM model of freeway traffic incident detection, traffic incident detection using the model.The experimental data from1-880traffic flow database build a training data set and test data set of experimental data as a thesis. Simulation software uses MATLAB7.0. Learned through the analysis of the experimental results, the particle swarm optimization chaos optimization support vector machine algorithm has higher classification accuracy in detection rate and low false detection rate, good highway traffic incident detection results.更多还原