Edge Computing-Empowered Large-Scale Traffic Data Recovery Leveraging Low-Rank Theory
Chaocan Xiang, Zhao Zhang, Yuben Qu, Dongyu Lu, Xiaochen Fan, Panlong Yang, Fan Wu
Abstract
Intelligent Transportation Systems (ITSs) have been widely deployed to provide traffic sensing data for a variety of smart traffic applications. However, the inevitable and ubiquitous missing data potentially compromises the performance of ITSs and even undermines the traffic applications. Therefore, accurate and real-time traffic data recovery is crucial to ITSs and its related services, especially for large-scale traffic networks. To leverage the characteristics in transportation networks for data recovery, we first conduct experimental explorations on a large-scale traffic dataset of an ITS and further quantify the spatiotemporal correlations of traffic data. Inspired by the observation results, we propose GTR, an edGe computing-empowered system for large-scale Traffic data recovery with low-Rank theory. GTR leverages the decentralized computing power of edge nodes to process massive traffic data from hundreds of traffic stations for accurate and real-time recovery. Specifically, we first propose a suboptimal edge node deployment algorithm with a theoretical performance guarantee, by exploiting the supermodularity in the NP-hard joint-optimization problem. Furthermore, to leverage the low-rank nature of traffic data, we transform the data recovery problem into a low-rank minimization problem, then utilize the fixed-point continuation iterative scheme to capture spatiotemporal correlations for accurate traffic recovery. Finally, the extensive trace-driven evaluations show that GTR only needs at most 5.7% extra total cost compared to the optimal deployment, while outperforming four baseline methods by 63.8% improvement in terms of traffic data recovery accuracy.