Network partitioning on time-dependent origin-destination electronic trace data

Daphne van Leeuwen*, Joost W. Bosman, Elenna R. Dugundji

*Corresponding author for this work

Research output: Contribution to JournalArticleAcademicpeer-review


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.

Original languageEnglish
Pages (from-to)687-706
Number of pages20
JournalPersonal and Ubiquitous Computing
Issue number5-6
Early online date22 Apr 2019
Publication statusPublished - 1 Nov 2019


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