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Extracting regular mobility patterns from sparse CDR data without a priori assumptions


Burkhard, Oliver; Ahas, Rein; Saluveer, Erki; Weibel, Robert (2017). Extracting regular mobility patterns from sparse CDR data without a priori assumptions. Journal of Location Based Services, 11(2):78-97.

Abstract

In this work we present two methods that can extract habitual movement patterns and reconstruct the underlying movement of users from their call detail records (CDR) in a way that works for users with only moderate numbers of CDRs and that does not make any prior assumptions on the behaviour of the users. The methods allow for a more comprehensive user base in large-scale studies due to the fact that users that might otherwise have to be discarded can also be analysed. The first one is computationally not overly intense and is based on association mining. The second one, which we named DAMOCLES, is based on extracting idiosyncratic daily patterns from clustered daily activities. The methods are evaluated on real data of 140 users over an average of 200 days against benchmarks using assumptions commonly found in the literature such as a work week from Monday to Friday on GPS ground truth. Both methods clearly outperform the benchmarks and for many users retrieve similar regularities. Additionally a simulation study is performed that allows to evaluate the methods in a more controlled environment.

Abstract

In this work we present two methods that can extract habitual movement patterns and reconstruct the underlying movement of users from their call detail records (CDR) in a way that works for users with only moderate numbers of CDRs and that does not make any prior assumptions on the behaviour of the users. The methods allow for a more comprehensive user base in large-scale studies due to the fact that users that might otherwise have to be discarded can also be analysed. The first one is computationally not overly intense and is based on association mining. The second one, which we named DAMOCLES, is based on extracting idiosyncratic daily patterns from clustered daily activities. The methods are evaluated on real data of 140 users over an average of 200 days against benchmarks using assumptions commonly found in the literature such as a work week from Monday to Friday on GPS ground truth. Both methods clearly outperform the benchmarks and for many users retrieve similar regularities. Additionally a simulation study is performed that allows to evaluate the methods in a more controlled environment.

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Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:07 Faculty of Science > Institute of Geography
Dewey Decimal Classification:910 Geography & travel
Language:English
Date:2017
Deposited On:15 Nov 2017 16:37
Last Modified:19 Feb 2018 09:18
Publisher:Taylor & Francis
ISSN:1748-9725
Additional Information:This is an Accepted Manuscript of an article published by Taylor & Francis in Journal of Location Based Services on 2017, available online: http://wwww.tandfonline.com/10.1080/17489725.2017.1333638
OA Status:Green
Publisher DOI:https://doi.org/10.1080/17489725.2017.1333638

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