【作者】 张敬磊；

【导师】 王晓原；

【作者基本信息】 山东理工大学 ， 载运工具运用工程， 2006， 硕士

【摘要】 对道路交通流进行预测、检测等分析研究,深入挖掘交通流动态信息,及时、准确地辨识未来和实时交通流状态,是实现动态交通管理的一个重要前提,也是目前广泛开展的智能运输系统(Intelligent Transportation Systems, ITS)项目,特别是其先进的交通管理系统(Advanced Traffic Management Systems, ATMS)和先进的出行者信息系统(Advanced Traveler Information Systems, ATIS)研究开发的基本要求。交通流状态辨识的主要作用是及时准确地识别当前和未来交通流状况,实施合理有效的交通管控和诱导措施,及时处理事件,减少人员伤亡和财产损失,诱导驾驶员行为并使交通流避开拥挤区域,达到平稳交通流、减轻拥挤区域交通压力、节约能源、减少污染的目的。本文从交通流的基本特点和动态交通管理、道路交通事件管理及交通流诱导的根本需求出发,对交通流状态辨识的两个方面——交通流短时预测和交通流实时检测的相关理论和方法进行深入研究。基于动态交通管理的基本思想,根据我国ITS框架,以事件管理系统为基础,设计了交通流状态辨识系统框架;在总结目前交通流短时预测和自动事件检测算法成果的基础上,依据数据挖掘技术中的非参数回归样条拟合、小波多分辨分析方法,提出了基于非参数回归样条拟合的交通流短时预测算法和基于多分辨分析的交通事件自动检测算法;利用英国南安普敦大学TRG(TransportationResearch Group)提供的实测数据以及交通仿真系统VISSIM的模拟数据分别对所建立的算法进行验证,结果表明本文所建立的两类交通流状态辨识算法均具有良好的性能。更多还原

【Abstract】 Traffic flow condition recognition is one of the important issues of Intelligent TransportationSystems (ITS), especially for its Advanced Traffic Management Systems (ATMS) and AdvancedTraveler Information Systems (ATIS) research. The objective of traffic flow condition recognition is torecognize the condition of traffic flow timely and accurately, take effective traffic incident management,traffic control and traffic flow guidance measures to clean out traffic incidents timely. In this way, theloss of casualties will be lowered and the second incidents will be avoided. In addition, the controlmeasures of transportation demand can be taken effectively and the route recommendations can beavailable to travelers so as to lessen traffic pressure, economize energy and reduce pollution.According to the demands of dynamic traffic management, traffic incident management and urbantraffic flow guidance, the theory of traffic flow condition recognition, including traffic flow short-termforecasting and real-time detection, is studied deeply in this paper. An architecture of traffic conditionrecognition system is put forward based on the basic thought of dynamic traffic management and thearchitecture of traffic incident management system. Two traffic flow condition recognition algorithms,namely the traffic flow short-term forecasting algorithm based on non-parameter regression splinefitting and the traffic incident detection algorithm based on wavelet multiresolution analysis, arepresented according to the characteristic of traffic flow. The parameters of algorithms are calibrated andtwo off-line tests are made according to the data under the condition of incident and non-incidentobtained from Transportation Research Group (TRG) of University of Southampton of UK and thesimulation. The results of tests proved that both algorithms perform better in traffic flow conditionrecognition.更多还原