压缩感知起始-终点矩阵估计
Compressive Origin-Destination Matrix Estimation
AI总结：
本文提出基于网络链路交通流量估计起始-终点流量及其路径拆分的压缩感知方法，通过合成网络和实际数据验证其在无噪声、有噪声及加权情况下的有效性，展示了其在车辆数量和车里行驶里程估算中的应用。
摘要：
本文提出了一种基于网络链路交通流量估计起始-终点（OD）流量及其路径拆分的方法，称为压缩感知起始-终点估计（CODE）。该方法受压缩感知（CS）技术启发。尽管估计问题通常是欠定的，当每对OD的替代路径数量较小时，CODE能够精确恢复未知变量。文中展示了CODE在无噪声、有噪声和加权情况下的合成网络应用，并在东普罗维登斯小地区的真实数据中进行验证。CODE的通用性体现在其可用于通过链路流量估计车辆数量和车里行驶里程（VMT）.
英文摘要：
The paper presents an approach to estimate Origin-Destination (OD) flows and their path splits, based on traffic counts on links in the network. The approach called Compressive Origin-Destination Estimation (CODE) is inspired by Compressive Sensing (CS) techniques. Even though the estimation problem is underdetermined, CODE recovers the unknown variables exactly when the number of alternative paths for each OD pair is small. Noiseless, noisy, and weighted versions of CODE are illustrated for synthetic networks, and with real data for a small region in East Providence. CODE's versatility is suggested by its use to estimate the number of vehicles and the Vehicle-Miles Traveled (VMT) using link counts.
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。
