
Traffic engineering with traffic prediction is one approach to accommodate time-varying traffic without frequent route changes. In this approach, the routes are calculated so as to avoid congestion based on the predicted traffic. The accuracy of the traffic prediction however has large impacts on this approach. Especially, if the predicted traffic amount is significantly less than the actual traffic, the congestion may occur. In this paper, we propose the traffic prediction methods suitable to the traffic engineering. In our method, we perform preprocessing before the prediction in order to predict the periodical variation accurately. Moreover, we consider the confidence interval for the prediction error and the variation excluded by the preprocessing to avoid the congestion caused by the temporal traffic variation. In this paper, we discuss three preprocessing approaches; the trend component, the lowpass filter, and the envelope. Through simulation, we clarify that the preprocessing by the trend component or the lowpass filter increases the accuracy of the prediction. In addition, considering the confidence interval achieves the lower link utilization within a fixed control period.
| citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 9 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Top 10% | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
