A cross-correlation-based method for spatial-temporal traffic analysis
Analyzing spatial-temporal characteristics of traffic in large-scale networks requires both a suitable analysis method and a means to reduce the amount of data that must be collected. Of particular interest would be techniques that reduce the amount of data needed, while simultaneously retaining the ability to monitor spatial-temporal behavior network-wide. In this paper, we propose such a method, motivated by insights about network dynamics at the macroscopic level. We define a weight vector to build up information about the influence of local behavior over the whole network. By taking advantage of increased correlations arising in large networks, this method might require only a few observation points to capture shifting network wide patterns over time. This paper explains the principles underlying our proposed method, and describes the associated analytical process. (c) 2004 Elsevier B.V. All rights reserved.
network traffic timescale cross-correlation spatial-temporal pattern eigenvalue eigenvector