Discovering urban activity patterns in cell phone data

Abstract

Massive and passive data such as cell phone traces provide samples of the whereabouts and movements of individuals. These are a potential source of information for models of daily activities in a city. The main challenge is that phone traces have low spatial precision and are sparsely sampled in time, which requires a precise set of techniques for mining hidden valuable information they contain. Here we propose a method to reveal activity patterns that emerge from cell phone data by analyzing relational signatures of activity time, duration, and land use. First, we present a method of how to detect stays and extract a robust set of geolocated time stamps that represent trip chains. Second, we show how to cluster activities by combining the detected trip chains with land use data. This is accomplished by modeling the dependencies between activity type, trip scheduling, and land use types via a Relational Markov Network. We apply the method to two different kinds of mobile phone datasets from the metropolitan areas of Vienna, Austria and Boston, USA. The former data includes information from mobility management signals, while the latter are usual Call Detail Records. The resulting trip sequence patterns and activity scheduling from both datasets agree well with their respective city surveys, and we show that the inferred activity clusters are stable across different days and both cities. This method to infer activity patterns from cell phone data allows us to use these as a novel and cheaper data source for activity-based modeling and travel behavior studies.

Estimating activity patterns using spatio-temporal data of cell phone networks

“…Therefore, some preprocessing needs to be done in order to convert the data into meaningful trips and stay locations that show movements and activity participation. Peter Widhelm et al [6] used a low-pass filter to eliminate the outliers and smooth the movements with a velocity higher than an acceptable range. Then they use an incremental clustering algorithm to detect stays and to convert raw cellphone track into a sequence of visited places.…”

“…Later they used a Markov chains with conditional random fields to find the relations between individual's socio-economic attributes and activity sequencing and spatio-temporal trajectory of activities. Again in the same year Peter Widhalm et al made an effort to discover urban activity patterns in cell phone data [6]. In order to do so they developed a twostaged method.…”

On data processing required to derive mobility patterns from passively-generated mobile phone data

“…Indeed, a search in the literature using keyword combinations of mobile phone data, mobility, and travel behavior resulted in more than 1000 articles published in journals across different disciplines (Ahas et al, 2010a; Becker et al, 2013; Calabrese et al, 2013; Candia et al, 2008; Chen et al, 2014, 2016; Gao et al, 2013; Song et al, 2010b; Wang et al, 2014). These articles cover a wide range of topics including, for example, estimating mobility patterns (Csáji et al, 2013; González et al, 2008; Song et al, 2010a), inferring OD matrix (Calabrese et al, 2011b; Iqbal et al, 2014), finding anchor locations (Dong et al, 2015; Isaacman et al, 2011), inferring activity types (Jiang et al, 2017; Widhalm et al, 2015) and travel modes (Qu et al, 2015; Wang et al, 2010). …”

Evaluation and Planning of Urban Green Space Distribution Based on Mobile Phone Data and Two-Step Floating Catchment Area Method

“…These studies advanced not only in terms of data size but also in visualization and representation. In recent years, mobile data have been used to identify the commuting of residents, and further the functional zones of the city [23][24][25][26]. By comparing them to actual census data, the results of analysis have been proven relatively accurate with evidently improved data size and precision.…”