基于卡口车牌识别数据的车辆出行分析

车辆出行是城市道路交通的基本组成单元，掌握城市道路网车辆的出行信息，深入挖掘车辆出行特征与规律，能为城市交通管理提供决策信息.本文基于卡口车牌识别数据，提出了一套车辆出行分析框架.首先对全路网运行的所有车辆的个体出行进行辨识，提取所有车辆出行的路径和行程信息，并从个体和集计层面获取车辆出行的规律特征；利用车辆的多日出行信息和统计特征，提出了车辆职住地识别方法；基于外地车的出行特征，利用 K-means++ 算法对外地车进行分类.在实例分析中，以广州市道路网运行车辆作为研究对象，开展了车辆出行分析，实验结果验证了本文方法的有效性.通过本文方法挖掘的信息对城市道路交通管理具有重要意义.

Analyzing Methods of Vehicle' Travel Using Plate Recognition Data

Vehicle travel is the basic component of urban road traffic. There is an urgent need for urban traffic manager to obtain the trip information on urban road network, and make a better understanding of vehicles' travel characteristics. In this paper, an analysis framework based on plate recognition data is proposed. Firstly, the individual trip information of all vehicles on urban road network is identified and the trip information of all vehicles are extracted. And then vehicles' travel characteristics are obtained at the individual and aggregate level. A methodology is proposed for recognizing vehicles' residence and workplace based on trip information and statistical feature. K-means ++ algorithm is applied to cluster and classify the nonlocal vehicles. In case study, focused on the vehicles of Guangzhou City, the analysis of vehicles travel are conducted. The effectiveness of the proposed method is proved by the case study results. The information mined through the method is of great significant for urban road traffic management.