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Clustering analysis of traffic flow time series can improve the accuracy of traffic flow prediction, which is the basis of traffic planning. The correlations between traffic flow series imply that there are some potential patterns of traffic flow movements. In this work, a clustering framework based on multi-scale analysis of traffic flow time series is proposed to seek these potential patterns. The framework includes the selection of the optimum algorithm, the construction of quantitative indicators to evaluate the clustering effect, and the visual inspection. The experimental results of 24-hour long-term traffic flow time series clustering on large-scale road network in Shenzhen indicate that the model can clearly distinguish different classes of traffic flow time series.

Clustering framework based on multi-scale analysis of traffic flow time series