Print Email Facebook Twitter Network partitioning on time-dependent origin-destination electronic trace data Title Network partitioning on time-dependent origin-destination electronic trace data Author van Leeuwen, D. Bosman, J.W. Dugundji, E.R. Publication year 2019 Abstract In this study, we identify spatial regions based on an empirical data set consisting of time-dependent origin-destination (OD) pairs. These OD pairs consist of electronic traces collected from smartphone data by Google in the Amsterdam metropolitan region and is aggregated by the volume of trips per hour at neighbourhood level. By means of community detection, we examine the structure of this empirical data set in terms of connectedness. We show that we can distinguish spatially connected regions when we use a performance metric called modularity and the trip directionality is incorporated. From this, we proceed to analyse variations in the partitions that arise due to the non-optimal greedy optimisation method. We use a method known as ensemble learning to combine these variations by means of the overlap in community partitions. Ultimately, the combined partition leads to a more consistent result when evaluated again, compared to the individual partitions. Analysis of the partitions gives insights with respect to connectivity and spatial travel patterns, thereby supporting policy makers in their decisions for future infra structural adjustments. © 2019, The Author(s). Subject Community detectionEnsemble learningMetropolitan regionsNetwork partitioningOptimisation methodOrigin destinationPerformance metricesSupporting policiesComputer applications To reference this document use: http://resolver.tudelft.nl/uuid:fbe4c68c-e634-41eb-8c61-36a3be7940ea TNO identifier 866883 Publisher Springer London ISSN 1617-4909 Source Personal and Ubiquitous Computing Document type article Files To receive the publication files, please send an e-mail request to TNO Library.