基于小世界模型的交通网络拥堵状态研究

Researching the Congestion Status of Transportation Network Based on Small World Model

在对海量数据进行分析和利用的同时,数据挖掘作为一种首选的工具已经普遍应用到各个领域中。为了解决交通网络中车辆拥堵的状态,在利用复杂网络中的小世界模型建立交通网络模型时,借助数据挖掘中的谱聚类方法对交通网络的拥堵状态进行分析,通过计算道路的平均拥堵时间控制交通灯的放行时间,使得整个交通网络不出现异常拥堵情况。采用Net Logo 5.0.3作为试验平台,对模拟交通网络进行分析,成功实现对交通流的调节,避免了长时间拥堵情况的发生。

In analyzing and applying massive data, as the preferred tool, data mining has been popular used in various areas. In order to solve the problem of congestion status of transportation network, in establishing transportation network model by adopting complex network small world model, the method of spectral clustering in data mining is used to analyze the congestion status of transportation network. Through calculating the average congestion time to control the traffic lights, thus abnormal congestion of the transportation network may not appear. With NetLogo 5. 0. 3 as the experimental platform, the emulated transportation network is analyzed, and the traffic flow is regulated successfully, and long period congestion is avoided.