城市交通系统降维状态判别规则

城市交通流是一个复杂多变、非线性、非结构化、时空变化的随机大系统。目前常用的固定阈值评价方法无法全面判别交通系统运行状态。随着我国智能交通系统的建设规模不断扩大，急需寻找一种适合我国城市交通流混合现象严重的、符合交通流运动机理的交通状态判别模型。本文在研究混合交通流的多维交通状态变量的基础上，利用粗糙集理论，建立四维状态判别模型，通过数据离散和属性约简得到二维决策表，提出一种城市交通系统降维状态判别规则，并以实例说明其能够有效剔除系统冗余信息，提高挖掘规则的精度。

Rules on Dimension-Reduced State Estimation in Urban Traffic System

Traffic flow is a complex, changeful, nonlinear, unstructured, space time-varying and random system. At present, the traffic movement condition on road can’t be estimated effectively by using the common fixed threshold value estimation methods. With the foundation and operating of the Intelligent Traffic System, it’s imperative to search for one traffic state estimation model, which is suitable for mixed-traffic in China and according with movement mechanism of traffic flow. On the basic of analysis of the multidimensional state characteristics of mixed-traffic, using the Rough Set Theory, the four-dimensional state estimation model is founded. By data discretization and attribute reduction, the two-dimensional decision table is gained, and the rules of multidimensional state estimation in urban traffic system are presented. A case is given and it indicates that this method can eliminate the redundancy information of the system effectively and improve the precision of rule mining.