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

Abstract

Passively-generated mobile phone data is emerging as a potential data source for transportation research and applications. Despite the large amount of studies based on the mobile phone data, only a few have reported the properties of such data, and documented how they have processed the data. In this paper, we describe two types of common mobile phone data: Call Details Record (CDR) data and sightings data, and propose a data processing framework and the associated algorithms to address two key issues associated with the sightings data: locational uncertainty and oscillation. We show the effectiveness of our proposed methods in addressing these two issues compared to the state of art algorithms in the field. We also demonstrate that without proper processing applied to the data, the statistical regularity of human mobility patterns—a key, significant trait identified for human mobility—is over-estimated. We hope this study will stimulate more studies in examining the properties of such data and developing methods to address them. Though not as glamorous as those directly deriving insights on mobility patterns (such as statistical regularity), understanding properties of such data and developing methods to address them is a fundamental research topic on which important insights are derived on mobility patterns.

Extracting trips from multi-sourced data for mobility pattern analysis: An app-based data example

“…Since it is less likely for any user to make a tour within a short time window, the noise sequences detected may contain observations generated from signaling noises and therefore are removed. Readers are referred to Wang and Chen (2018) for more details on the timewindow-based method. It is found that false trips in the app-based data lead to overestimation on users' daily trip rate.…”

“…Contrast to actively solicited data such as household travel survey data, the passively-generated data are usually location-and time-stamped and are the by-product of some nontransportation related purposes (Chen et al, 2016). Among them two kinds of data are widely used: cellular data generated from cellular networks for mobile phone billing purposes (Alexander et al, 2015;Wang and Chen, 2018) and vehicular GPS data generated from the processes of, for example, freight/taxi/buses management and operation (Yang et al, 2014). Besides data that are generated from single positioning technology (e.g., cellular tower based or GPS), data are also emerging from the combined use of multiple positioning technologies, including GPS-, WiFi-, Bluetooth-, and cellulartowers-based technologies.…”

A Random Forest Model for Travel Mode Identification Based on Mobile Phone Signaling Data

“…Also, background information of traffic routes and travel information of travelers are included in their model [22]. Wang and Chen (2018) used speed, acceleration, direction change rate, and individual traveler characteristics (age and disability) as input features combined with GIS information [23]. In the study of Dabiri (2018), the speed, acceleration, speed curve smoothness, and direction change rate were used to identify the mode of travel, and the GPS trajectory data collected by the test was used to test and establish different classifications [24][25][26].…”

Characterizing and Removing Oscillations in Mobile Phone Location Data

“…Oscillation phenomenon. Recent works in literature have attempted to firstly define the oscillation phenomenon and then suggest methods to detect and correct it [1], [5], [11], [14], [23], [24]. Existing definitions are not formal, but they all converge in context.…”

“…Closer to our approach are methods that define particular patterns and take into account the time information. [23] proposes two methods, namely circular and pattern-based. In the circular case, an oscillation is defined as an AXA pattern, where X = A and |X| >= 1, and the temporal window is set at 5 minutes, after trial and error, arguing that longer time windows might consider actual mobile trips.…”