Traffic Flow Decomposition and Prediction Based on Robust Principal Component Analysis

Research on traffic data analysis is becoming more available and important. One of the key challenges is how to accurately decompose the high-dimensional, noisy observation traffic flow matrix into sub-matrices that correspond to different classes of traffic flow which builds a foundation for traffic flow prediction, abnormal data detection and missing data imputation. While in traditional research, Principal Component Analysis (PCA) is usually used for traffic matrix analysis. However, as the traffic matrix is usually corrupted by large volume anomalies, the resulting principal components will be significantly skewed from those in the anomaly-free case. In this paper, the authors introduce the Robust Principal Component Analysis (robust PCA) for decomposition. It can mine more accurate and robust underlining temporal and spatial characteristics of traffic flow with all kinds of fluctuations. The authors performed a comparative experimental analysis based on robust PCA with PCA-based method on a real-life dataset and got better decomposition performance. In the real-life dataset, results show that through robust PCA most of the large volume anomalies are short-lived and well isolated in the residual traffic matrix while PCA failed. In traffic flow prediction experiments based on decomposition, it shows that the result based on robust PCA outperforms the PCA and simple average. It provides adequate evidence that robust PCA is more appropriate for traffic flow matrix analysis. Robust PCA shows promising abilities in improving the accuracy and reliability of traffic flow analysis.

Language

  • English

Media Info

  • Media Type: Web
  • Features: References;
  • Pagination: pp 2219-2224
  • Monograph Title: 18th International IEEE Conference on Intelligent Transportation Systems (ITSC 2015)

Subject/Index Terms

Filing Info

  • Accession Number: 01604664
  • Record Type: Publication
  • ISBN: 9781467365956
  • Files: TRIS
  • Created Date: May 2 2016 3:26PM