基于数据挖掘的交通仿真模型参数标定

摘要

Aiming at the limitation of the classical speed-density model on describing the dynamic change characteristics of the traffic flow, this paper puts more road detected information in the process of parameters calibration of traffic simulation model. After preprocessing the detector data, the data mining methods are used to calibrate the vehicle speed. It also proposes a novel locally weighted regression based on agglomerative hierarchical cluster. It first clusters the training samples and uses the agglomerative hierarchical clustering algorithm to establish a clustering tree for each constraint-clustering. Then it applies the k-nearest neighbor method to cluster new stage samples into the best fit clustering. Finally, the vehicle speed is estimated. The vehicle density, densities and flows are taken as the variables. The test with a huge of field data shows that the proposed algorithms performs well on parameters estimation precision and efficiency. It is appropriate for DTA based simulation.%为了克服经典速度-密度模型刻画道路交通流动态变化特性的缺陷,将更丰富的路段检测信息运用到交通仿真模型参数的标定过程中.提出在预处理检测器数据后,采用一种基于凝聚层次聚类的局部加权回归算法标定车辆速度.该算法先对训练样本进行聚类,然后用凝聚层次聚类法对每一个约束类生成一棵聚类树；其次用k-最近邻方法将与待估计速度相关的新样本划入适当的类中,最后采用局部加权回归标定车辆速度.利用现场数据对算法进行了大量测试,分别将车流密度,密度与流量作为变量标定车速.结果表明,提出的算法是有效的,适用于基于仿真的动态交通分配系统.