基于模式的城市交通状态分类与性质研究

Pattern-Based Study on Urban Transportation System States and Properties with Fuzzy Reasoning Methods

首先选用较多的交通参数,设计提出了基于聚类方法的细粒度交通模式划分算法,将交通模式划分为较多的类别.结合实际数据,进行了实验,在四个路段参数的基础上,划分得到了十类交通模式.这其中都用到了模糊集合划分和定性推理.使用状态核确定一系列模式,随后系统状态被划分成若干个模式.提出了模式转移方程来描述在定性推理基础之上的状态转移,并从模式转移的角度进一步研究了系统的稳定性.一个城市交通系统的应用实例显示了本文提出方法的有效性,可以看出细粒度交通模式划分要比粗粒度有更多的优势,同时得出了聚类算法在细粒度交通模式划分中的劣势.

In this paper,we propose a novel pattern-based method to model the classification and transition properties of traffic flow.First,fuzzy set classification method is utilized to divide the traffic states,where the states are partitioned into a number of patterns.Then,fuzzy qualitative reasoning is applied to analyze the transitions between the states.Based on the probability of transition,stability of the traffic states is further investigated.Finally,a case study on urban transportation system is performed to demonstrate the usage of the proposed approach.