基于信号处理技术的ITS数据压缩方法与应用

Signal-processing-based ITS data compression techniques and applications

当前智能交通系统(ITS)数据采集技术的飞速发展所引发的ITS数据海量特性,将严重妨碍数据在交通系统内的存储、传输和发布。在ITS数据自身特征分析的基础上,利用信号处理领域中的小波变换、离散余弦变换、量化和编码技术,通过设计适当的特征抽取阈值和量化器,提出了有限失真条件下(以一定的失真为代价)的高效ITS数据压缩与重构方法。压缩的出发点是在控制失真度的前提下,尽可能多的保留有用信息。通过对北京市三环路上的交通流数据进行测试,表明采用本文提出的数据压缩方法与常用的无损压缩方法(WinZip软件)相比,能够将文件的压缩幅度相对提高61.92%,且重构数据中与原始数据差值大于1的记录仅占1.27%,证明在有限的失真度前提下压缩效果得到明显改善。

The rapid development of Intelligent Transportation Systems （ITS） data collection technologies has generated massive ITS data, presenting a critical obstacle to storing, transmitting and disseminating the data within the transportation system. After analyzing the characteristics of ITS data, this paper proposes a highly effective ITS data compression and reconstruction approach with a limited distortion by designing the appropriate feature-distilling threshold and quantizer, based on the Wavelet Transform, Discrete Cosine Transform （DCT） , and Quantizing and Coding techniques for signal-processing. The objective of the compression is to minimize the data redundancy, keep the maximum amount of useful information and limit the level of distortion. Test on the traffic flow data of the 3rd ring expressway in Beijing demonstrates that the proposed technique has increased the compression rate by 61.92 percent in comparison with the widely used lossless compression approach （WinZip software） , and that only 1.27 percent of the data has shown a larger-than-five-percent error between the reconstructed and the original raw data. This indicates that the proposed technique can significantly improve the effectiveness of compression with a limited distortion.