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Title Weighted Dynamic Time Warping For Traffic Flow Clustering
ID_Doc 61619
Authors Li M.; Zhu Y.; Zhao T.; Angelova M.
Year 2022
Published Neurocomputing, 472
DOI http://dx.doi.org/10.1016/j.neucom.2020.12.138
Abstract 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. © 2021 Elsevier B.V.
Author Keywords Cluster validation; K-medoids clustering; Traffic flow analysis; Weighted Dynamic Time Warping


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