Mining Large-scale Mobility Patterns Using Mobile Phone Network Data Amnir Hadachi, Mozhgan Pourmoradnasseri, Kaveh Khoshkhah To cite this version: Amnir Hadachi, Mozhgan Pourmoradnasseri, Kaveh Khoshkhah. Mining Large-scale Mobility Pat- terns Using Mobile Phone Network Data. 2020. hal-02974853 HAL Id: hal-02974853 https://hal.archives-ouvertes.fr/hal-02974853 Preprint submitted on 22 Oct 2020 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. Mining Large-scale Mobility Patterns Using Mobile Phone Network Data a < a a Amnir Hadachi , ,1, Mozhgan Pourmoradnasseri and Kaveh Khoshkhah aITS Lab, University of Tartu, Ülikooli 17, 51014 Tartu, Estonia ARTICLEINFO ABSTRACT Keywords: In this study, with Estonia as an example, we established to which extent and how we can use a mas- Commuting patterns sive amount of mobile data of the cellular networks, which is referred to as Call detail record (CDR), CDR data to extract large-scale commuting patterns at different geographical levels. We constructed a model OD-Matrix based on Hidden Markov Model for reconstructing and transforming the trajectories extracted from Large-scale mobility the CDR data. This step allowed us to perform origin-destination Matrix extraction among different Hidden Markov model geographical levels, which helped in depicting the commuting patterns. Besides, we introduced dif- Mobile Cellular Network data ferent techniques for analyzing the commuting at the urban level. Our results unveiled that there is great potential behind mobile data of the cellular networks after transforming it into meaningful mo- bility patterns that can easily be used for understanding urban dynamics, large-scale daily commuting and mobility. The aggressive development and growth of ubiquitous mobile sensing have generated valuable data that can be used with our approach for providing answers and solutions to the growing problems of transportation, urbanization and sustainability. 1. Introduction physics as well as geography, to exploit the data for mod- elling and analyzing different factors of human mobility. Spatiotemporal population movement has a significant GPS data provides the most accurate spatial trajectories impact on the environment, people’s lifestyle and economy. of individuals, but it is not usually available on a larger scale. Therefore, understanding human mobility patterns over time, A common source of GPS data are vehicles equipped with both in short and long periods, is in the heart of sustain- GPS transmitter (Bazzani et al., 2010; Liu et al., 2012). Al- able urban and transportation planning and resolving envi- though high accuracy in positioning makes this type of data ronmental problems. a rich source for mapping human mobility patterns, the low To this end, many different data sources are used for ex- degree of diversity and amount of the data remains a chal- tracting information on human mobility. Census data has lenge. Besides, collecting the data requires GPS equipment been the main source for a long time and it is usually col- and maintenance which impose extra costs. lected periodically by governments. The regularity and the Mobile data is game-changing for monitoring both macro types of census data vary widely in different countries and and micro levels of human mobility behaviour (Järv et al., regions. 2014). Unlike traditional methods, it allows to track a large Another source of data commonly used for deriving in- number of individuals frequently and for a long time inter- formation on trips and the flows of population between cer- val. Cellular networks record spatiotemporal trajectories of tain locations are travel surveys (Zhai et al., 2019). Travel a considerable portion of the population for billing purposes. surveys are usually carried out by local governments and This already available data has originated the possibility of contain more detailed information in comparison to census larger-scale studies on human mobility that could be more data, such as trip purpose and mode of travel. However, frequent and executed at lower cost. As another advantage, the data is collected on a smaller scale and may be biased the cellular date is influenced less by the traditional methods due to self-reporting errors. In addition to the traditional inconsistency, caused by intrinsic differences in collection ways of collecting data for extracting mobility patterns, there methods in different regions (Tolouei et al., 2017). are some research papers reflecting the usage of less trivial Nevertheless, the cellular network data is sparse in time sources of data. Bank notes (Brockmann et al., 2006), transit and with low spatial resolution. These characteristics are smart cards (Ma et al., 2017), online publicly shared data in due to cellular events records which are representing the lo- social networks, such as Twitter and Flicker (Jurdak et al., cation of the user within a tower coverage area or Location 2015; Yang et al., 2019; Barchiesi et al., 2015), are some Area (LA) and the user’s location is lost when the mobile examples of less-common data sources. phone is not in use or no keep-alive signal is received from Recently, we witnessed an increase in the availability of the network. However, by refining the known limitations of massive data sources such as GPS and mobile data and also mobile phone data and through a careful choice of analyzing the means for handling them, which provides a new con- methods such as map matching (Hadachi & Lind, 2019), it text for scientists in several fields, such as computer science, is possible to control these barriers to some extent. Never- < Corresponding author theless, there are other obstacles such as privacy concerns [email protected] (A. Hadachi) or providing a large scale of a new type of mobile sensors 0000-0001-9257-3858 ORCID(s): (A. Hadachi) datasets such as in the “Data for Development” (D4D) chal- : Preprint submitted to Elsevier Page 1 of 15 lenge (Blondel et al., 2012) by Orange or the “Big Data Chal- individual usually follow a repetitive pattern, but that these lenge” (Barlacchi et al., 2015) by Telecom Italia. These can patterns are also very common between different people when easily initiate several new problems related to human mobil- a large amount of population is observed, even in different ity based on mobile data. countries. Schneider et al. (Schneider et al., 2013) study In general, cellular network data can be very useful in the behaviour of mobile users in different cities of different the study of broadening areas related to human studies, such countries and surprisingly discover that with 17 unique sim- as estimating the population density (Ricciato et al., 2017), ple networks alone it is possible to describe the daily com- community detection (Lind et al., 2017), segregation (Järv muting patterns of up to 90~ of the population. The authors et al., 2015), Carbon footprint detection (Becker et al., 2013) explain that each individual has a preferred list of locations and many more. Understanding the different aspects of hu- for daily visits and that the characteristics of his or her mo- man mobility is among the promising use-cases of mobile bility remain stable over several months. Jiang et al. (Jiang data. A wide range of factors are studied in this area, such as et al., 2017) extract the daily mobility patterns of inhabitants predicting human mobility based on users’ history (Hadachi of the city-state Singapore using CDR data. By labelling the et al., 2014), travel time estimation (Kujala et al., 2016), most frequently visited locations for each individual, mean- relative traffic volumes in metropolitan areas (Becker et al., ingful stay locations are identified. It is observed that a few 2013) and traffic monitoring (Janecek et al., 2015). extracted network motifs are sufficient to describe the mobil- From this perspective, our paper focused on unveiling ity patterns of the whole population. Next, for each motif, the potential behind Mobile data of the cellular networks to the areas with the highest density are detected and the re- extract movement and commuting patterns of the population sults are analyzed and compared with official survey data. on a large scale. The adopted approach has two major levels. The authors conclude that Big Data, if properly treated, can One level is based on the Hidden Markov Model (HMM) and provide further insights beyond traditional methods with a OD-matrix for reconstructing and building complete mobil- robust outcome and in wider study areas. ity patterns and flows. The second level is about estimating In addition to the existence of common regular patterns the departure and arrival time of the commuting journeys in in human mobility, common behaviours in mobility are also the OD-matrix and the movement status classification (Stay investigated. Oliveira et al. (Oliveira et al., 2016) study the or Move). Therefore, the paper is organized in such a manner individual mobility patterns of people in eight major world to lay emphasise on describing the used data in this research cities: Beijing, Tokyo, New York, Paris, San Francisco, Lon- work and all the challenges faced due to its nature. The sec- don, Moscow, and Mexico City by using different types of ond section is about related work and similar projects. The datasets, including mobile network data. Their result reflects third section is detailing a view of our proposed approach. a clear repetitively in human mobility patterns. Additionally, Finally, the last section gives a clear overview of the results they deduce that people tend to use the shortest path when trailed with a thorough discussion.