Network partitioning on time-dependent origin-destination electronic trace data
| Autor: | Daphne van Leeuwen, J. W. Bosman, Elenna R. Dugundji |
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| Přispěvatelé: | Mathematics, Centrum Wiskunde & Informatica, Amsterdam (CWI), The Netherlands |
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
Empirical data
Origin destination Social connectedness Computer science Mobile computing 02 engineering and technology Management Science and Operations Research computer.software_genre Neighbourhood level Supporting policies 020204 information systems Ensemble learning 0202 electrical engineering electronic engineering information engineering Community detection Computer Applications 020206 networking & telecommunications Partition (database) Network partitioning Computer Science Applications Hardware and Architecture Performance metrices Data mining Optimisation method Computer applications Performance metric computer Metropolitan regions |
| Zdroj: | van Leeuwen, D, Bosman, J W & Dugundji, E R 2019, ' Network partitioning on time-dependent origin-destination electronic trace data ', Personal and Ubiquitous Computing, vol. 23, no. 5-6, pp. 687-706 . https://doi.org/10.1007/s00779-019-01208-1 Personal and Ubiquitous Computing, 23(5-6), 687-706. Springer London Personal and Ubiquitous Computing, 23, 687-706 Personal and Ubiquitous Computing |
| ISSN: | 1617-4909 |
| DOI: | 10.1007/s00779-019-01208-1 |
| Popis: | 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). |
| Databáze: | OpenAIRE |
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