Abstract: In order to identify the traffic state freeway network real-timely and accurately, a method of traffic state identification for freeway network was proposed, combined with the characteristics of Macroscopic Fundamental Diagram (MFD) of freeway network. According to the detection data of floating car, the MFD of the freeway network was obtained and the freeway network was primary divided into 5 categories. Furthermore, according to the real-time data of road network, the state parameters of road network were further corrected by clustering algorithm. Based on the above and according to the relationship of flow and velocity, the freeway traffic state identification model was established, which comprehensively considered the influence of the main flow of freeway traffic, different combination forms of on-ramp and off-ramp and the condition of the road network adjacent to the ground. Finally, the proposed model was compared with the results obtained by road traffic operation index and vehicle travel time method, through the actual road network traffic data. The results show that the proposed traffic state identification model for freeway network is more real-time and accurate.

Key words: traffic engineering, freeway, traffic congestion, state identification, macroscopic fundamental diagram

摘要： 为了实时准确识别快速路网交通状态,结合快速路网的宏观基本图(macroscopic fundamental diagram，MFD)特性，提出了一种快速路网交通状态识别方 法。根据浮动车检测数据，获得快速路网的MFD，初步将快速路网划分为5种状态。根据路网实时数据，通过聚类算法进一步对路网状态参数进行修正。以此为基 础，综合考虑快速路主线流量、出入口匝道组合形式以及邻接地面路网状况的影响，根据流量速度关系建立快速路交通状态识别模型；并通过实际路网交通数据 ，将所建立的模型与道路交通运行指标和车辆行驶时间法得到的结果进行对比。结果表明：建立的快速路网交通状态识别模型更加实时准确。

关键词: 交通工程, 快速路, 交通拥挤, 状态识别, 宏观基本图