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Clustering of transactional traffic data to analyse mobility patterns
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This work develops a framework to analyse the development of mobility patterns over time. Based on flow data of individual vehicles on Portuguese motorways the proposed methodology defines a set of features that describe each individual’s movement characteristics. K-means was identified as most suitable algorithm to cluster the set of features allowing for a meaningful interpretation of mobility patterns. The analysis showed a conflict of objectives between cluster quality, and the interpretability of the defined features. Therefore, for an optimal outcome of the analysis the number of clusters should be manually aligned with the goal of the analysis.
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Data science Business analytics Traffic clustering Traffic flow modeling Mobility pattern analysis