基于多源数据融合的城市道路网络宏观基本图模型  

Macroscopic Fundamental Diagram Model of Urban Network Based on Multi-source Data Fusion

作　　者：金盛[1] 沈莉潇 贺正冰 JIN Sheng1, SHEN Li-xiao1, HE Zheng-bing2(1. College of Civil Engineering and Architecture, Zhejiang University, Hangzhou 310058, China; 2. Institute of Transportation System Science and Engineering, Beijing Jiaotong University, Beijing 100044, Chin)

机构地区：[1]浙江大学建筑工程学院,杭州310058 [2]北京交通大学交通系统科学与工程研究院,北京100044

出　　处：《交通运输系统工程与信息》2018年第2期108-115,127,共9页Journal of Transportation Systems Engineering and Information Technology

基　　金：国家自然科学基金(91746105;71501009);浙江省重点研发计划项目(2018C01007)~~

摘　　要：宏观基本图(MFD)是描述路网平均流量和平均密度的关系模型,在路网服务水平评估、区域控制和宏观交通建模中具有重要的作用.本文以路网加权流量和加权密度为MFD描述指标,提出了一种融合微波检测器数据和车牌识别数据的MFD构建方法.为了评估不同数据源下路网MFD的有效性,提出了采用状态比指标描述MFD的差异性.以青岛市实际数据为例,分析了单一数据来源和融合数据下的MFD变化规律.结果表明,在路网中存在不同类型检测器时,融合模型仍能够精确描述路网MFD.The macroscopic fundamental diagram（MFD） is the relationship between average flow and average density of networks and plays an important role in network service level assessment, regional control and macro traffic modeling. In this paper, an MFD method is proposed to fuse the data of the remote traffic microwave sensor and the license plate recognition data by using the networked weighted flow and the weighted density as the MFD description indexes. In order to evaluate the effectiveness of MFD under different data sources, the difference of MFD is described by traffic state ratio. Taking the data of Qingdao City as an example, the MFD＇s law of single data source and fusion data are analyzed. The results show that the fusion model can accurately describe the network MFD when there are different types of detectors in the network.

关 键 词：交通工程 宏观基本图 数据融合 微波数据 车牌识别数据 状态比 

分 类 号：U491.4[交通运输工程—交通运输规划与管理]