Weighted dynamic time warping for traffic flow clustering.

• Providing a more effective Weighed DTW for traffic flow data analysis. • Systematically analysing and labelling a large real traffic dataset from the City of Melbourne. • GWDTW outperforms Euclidean and DTW using k-medoid for clustering traffic flow data. This paper presents a novel similarity measure to identify interesting traffic patterns on a large traffic flow time series data for the central suburbs of Melbourne city in Australia. This new measure is a weighted Dynamic Time Warping (DTW) method based on Gaussian probability function, named GWDTW, that reflects the relative importance of peak hours. We have shown its superior performance over two benchmark similarity measures, the Euclidean distance and conventional DTW measure, on the intersection clustering task using k -medoids clustering algorithm, with respect to both internal and external evaluation measures. With intensive evaluation, the results show that GWDTW is a very effective similarity measure for modelling traffic behaviours, which can provide policy makers with more valuable information for infrastructure design, and smart city development. [ABSTRACT FROM AUTHOR]