论文标题
基于GPS探针和LOOP探测器数据的层次宏观基本图应用的层次宏观基本图应用
Traffic Network Partitioning for Hierarchical Macroscopic Fundamental Diagram Applications Based on Fusion of GPS Probe and Loop Detector Data
论文作者
论文摘要
宏观基本图的大多数网络分配方法主要基于标准化的切割机制，该机制采用了每个链接的交通统计信息，例如链接量，速度或密度，作为计算两个链接之间相似性程度的输入，并在链接之间的流量动力学截然不同时，在每次迭代时执行图形切割以将网络划分为两个子网络。这些方法假设整个网络上的完整链接级流量信息，例如网络中每个链接的流量条件都存在准确的测量，这使得它们在现实世界设置方面不可能。在本文中，我们提出了一种方法，该方法基于融合探针车辆数据和循环检测器数据，以网格级网络方法提取本地同质子网络，而不是处理详细的链接级网络。通过融合这两个数据源，我们利用PVD的更好覆盖范围和LDD的全尺寸检测。
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.
