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基于数据挖掘的短时交通流预测及辅助诱导
其他题名Short-term Traffic Flow Forecasting and Routing based on Data Mining
宫晓燕
学位类型工学博士
导师王飞跃
2003-05-15
学位授予单位中国科学院研究生院
学位授予地点中国科学院自动化研究所
学位专业控制理论与控制工程
关键词智能交通系统 短时交通流预测 数据挖掘 交通流数据过滤 非参数回归 Cbr 交通流辅助诱导 Intelligent Transportation Systems Short-time Traffic Flow Forecasting Data Mining Traffic Data Screening Non Parametric Regress
摘要近年来,短时交通流预测成为ITS研究的热门课题。准确实时的短时交通流预测 是实现交通控制与管理、交通事件检测和交通流诱导的前提及关键;是使智能交通系 统从“被动式反应”转变到“主动式动作”的关键。 对于单点交通流预测,现有的各种算法都有其特定的应用条件,需要建立“无参 数”、可移植的、预测精度高的算法。对于路网交通流预测,主要采用基于0D的动态 交通量分配算法,但OD难以检测和预测,出行者的出行行为难以确定,算法优化时间 过长,需要建立实时的路网交通流预测算法。。 基于上述背景,结合数据挖掘技术,本文开展四个方面的研究: 1)基于数据挖掘的交通流数据过滤算法 本文提出在综合结合阀值和交通流机理的算法的基础上,采用基于时间和空间检 验的错误交通流数据剔除算法;同时提出基于时间序列方法,并综合考虑交通事件的 丢失数据估计算法。 2)基于数据挖掘的单点实时交通流预测算法 首先对现有的非参数回归预测算法进行三点改进:基于相关分析和自相关分析的 状态向量的选取;基于密集度的变K历史数据搜索方法;基于动态聚类和散列函数的 历史数据组织方式。接下来结合上述算法,通过对一天的平均交通流进行基于数据挖 掘的算法分析,提出基于不同时段的综合预测方法。 3)基于数据挖掘的路网实时交通流预测算法 首先对路网中各路段的交通流数据进行的各种基于数据挖掘的算法分析,得出路 段流量之间的相关关系和路段流量之间的关联规则。然后基于这些关系和规则提出一 种新颖的实时的路网流量预测算法。 4)基于CBR的交通流辅助诱导算法 这部分研究内容是上述内容的延伸。为了提高目前诱导算法的实时性,本文将 CBN(Case Based Reasoning)应用于辅助诱导。通过搜索与当前路网状态即“当前事 件”的相似的“历史事件”来确定诱导方案。
其他摘要Recently, short-time traffic flow forecasting becomes a research hotspot, because only with realtime and accurate predict of traffic flow, can assure traffic control, event detection and traffic-routing. For an intersection, there exists some algorithms, but each has its special applied situation. So a new kind of algorithm with "no parameter", portability and high accuracy is in need. For a traffic network, So far many algorithms have been proposed, but problems in accuracy and timeliness still remain to be the major obstacle for their successful applications. For example, presumed human travel habit and vehicle turning probabilities at intersections have greatly limited the use of dynamic assignment algorithm. Based on data mining tech, this paper following researches: 1) Traffic data screening algorithm based on data mining Considering valve value and traffic flow theory, this paper proposed a kind of screening method based on space and time verification. Considering effect of traffic event, this paper proposed a kind of estimation for lost data based on time series, 2) Single point traffic flow forecasting based on data mining First three improvements are put forward which are effective traffic state vector selection method based on self-association analysis and association analysis, improved variable K search method based on "dense degree" and advanced data structures based on dynamic cluster method and hash-function transformation. And then based on this algorithm and analysis of a whole day traffic flow curve, a comprehensive method is proposed. 3) Traffic network flow forecasting based on data mining After analysis of traffic flow of each link in a traffic network with data mining tech, relationship among links and association rules among links are educed. Then with these rules and relationship, a new network flow forecasting method is proposed. 4) Traffic supportive routing algorithm based on CBR In order to improve the timeliness of the current traffic routing algorithm, Case Based Reasoning(CBR) is applied to support traffic routing. Through searching similar history event to the current event, realtime routing answer can easily got.
馆藏号XWLW730
其他标识符730
语种中文
文献类型学位论文
条目标识符http://ir.ia.ac.cn/handle/173211/5764
专题毕业生_博士学位论文
推荐引用方式
GB/T 7714
宫晓燕. 基于数据挖掘的短时交通流预测及辅助诱导[D]. 中国科学院自动化研究所. 中国科学院研究生院,2003.
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