基于GPS探针和环路检测器数据融合的交通网络分层宏观基本图应用
Traffic Network Partitioning for Hierarchical Macroscopic Fundamental Diagram Applications Based on Fusion of GPS Probe and Loop Detector Data
摘要：
大部分宏观基本图的网络划分方法大多基于归一化割机制，即以每条链路的流量统计信息（如链路流量、速度或密度）为输入，计算两条链路之间的相似度，当链路之间的流量动态变化显著时，在每次迭代时进行图割，将网络划分为两个子网。这些方法假设整个网络上的完整链路级业务信息，例如，对于网络中的每个单个链路都存在业务状况的准确测量，这使得它们在现实世界设置时不适用。在本文中，我们提出了一种方法，该方法的基础上，融合探测车数据和环路检测器数据，提取局部同质子网络的网格级网络的方法，而不是处理详细的链路级网络。通过融合这两个数据源，我们利用PVD的更好覆盖率和LDD的全尺寸检测。
英文摘要：
Most network partitioning methods for Macroscopic Fundamental Diagram are mostly based on a normalized cut mechanism, which takes the traffic statistics of each link, e.g. link volume, speed or density, as input to calculate the degree of similarity between two links, and perform graph cut to divide a network into two sub-networks at each iteration when the traffic dynamics between links are dramatically different. These methods assume complete link-level traffic information over the entire network, e.g. the accurate measurement of the traffic conditions exist for every single link in the network, which makes them inapplicable when it comes to real-world setting. In this paper, we propose a method which, based on fusing Probe Vehicle Data and loop detector data, extracts the locally homogeneous subnetworks with a grid-level network approach instead of dealing with detailed link-level network. By fusing the two data sources, we take advantage of both better coverage from PVD and the full-size detection from LDD.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
