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

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. 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.

On the requirements on spatial accuracy and sampling rate for transport mode detection in view of a shift to passive signalling data

“…The splitting of the raw data into trips is not part of this work, as the stays were for the most part very clearly discernible. This assumes long stays with short times of movement in between, as was already observed in other studies (Burkhard et al, 2017).…”

A Mobility Model for Synthetic Travel Demand From Sparse Traces

“…Their study suggests that filling the gaps in the sparse individual traces results in better representation of travel demand, e.g., the truncated power-law distribution of trip distance distributions. Burkhard et al (2017) have reconstructed regular mobility patterns from users with sparse CDRs using idiosyncratic daily patterns from clustered daily activities [25].…”

Exploring the Role of Mobility and Personality for Healthy Aging

“…Often 1 week of GPS data are recorded to assess an individual's habitual movement, assuming that the majority of the movement patterns are repeated on a weekly basis (Cornwell & Cagney, 2017;Giannouli et al, 2016;Kestens et al, 2016;Schmidt, Kerr, Kestens, & Schipperijn, 2018). Some GPS studies have shown, however, that a minimum of 14 days of GPS data are needed to obtain a stable measure of an individual's activity space (Stanley, Yoo, Paul, & Bell, 2018;Zenk, Matthews, Kraft, & Jones, 2018) and people's movement habits may change to a greater extent than expected (Burkhard, Ahas, Saluveer, & Weibel, 2018).…”