基于模糊C均值聚类的城市道路交通状态判别

为及时判别城市道路交通状态，考虑城市道路交通特征的差异性和交通流的波动特性，对状态指标的合理性进行分析；将交通状态划分为畅通、缓行、拥堵、阻塞4类，提出一种基于模糊C均值聚类（FC M ）判别城市道路交通状态的算法。选取车速、流量、占有率作为交通状态判断指标，根据不同指标设计3种方案，用MATLAB模糊逻辑工具箱分析出仿真数据的聚类中心，对不同指标组合下的各样本交通状态进行判断，验证算法判别的可行性。结果表明，以速度、流量、占有率为参数的FCM算法能较好地判别城市道路交通状态，精度较高。

Urban Road Traffic State Identification Based on Fuzzy C-Mean Clustering

In order to estimate traffic state of urban road in time , considering the differences of characteristics of urban road traffic and the variation characteristics of traffic flow ,and the analysis on the rationality of the states indicators .The traffic state was divided into 4 classes ,included smooth ,relatively smooth ,congestion ,jam .Based on the fuzzy c‐means clustering (FCM ) ,an estimation algorithm of traffic state of urban road was developed .In this algorithm ,the different combinations of velocity ,flow and time occupancy for the estimation parameter were selected to cluster analysis . MATLAB fuzzy logic of toolboxes was used to find clustering center of simulation data .The traffic condition of each sample is judged by different indexes ,and the feasibility of the algorithm is verified .The results show that uses the velocity ,flow and occupancy as the parameter of the FCM algorithm can estimate traffic state of urban road effectively .This algorithm possesses higher accuracy and is easier to handle .