Understanding Human Mobility Flows from Aggregated Mobile Phone Data

Abstract

In this paper we deal with the study of travel flows and patterns of people in large populated areas. Information about the movements of people is extracted from coarse-grained aggregated cellular network data without tracking mobile devices individually. Mobile phone data are provided by the Italian telecommunication company TIM and consist of density profiles (i.e. the spatial distribution) of people in a given area at various instants of time. By computing a suitable approximation of the Wasserstein distance between two consecutive density profiles, we are able to extract the main directions followed by people, i.e. to understand how the mass of people distribute in space and time. The main applications of the proposed technique are the monitoring of daily flows of commuters, the organization of large events, and, more in general, the traffic management and control.

Mobile phone data statistics as a dynamic proxy indicator in assessing regional economic activity and human commuting patterns

“…Analytics and visualizations can provide true values and real‐world contributions to big data computing that map well with geographic information systems (Chang, ). To discover the population's commuting patterns and directions, having only aggregated CDR data from mobile cellular base stations, the Monge–Kantorovich mass movement model (Balzotti et al, ) was used. Wasserstein distance calculations provide approximate indications on human commuting patterns across a certain territory in different time periods, allowing an analysis of different times of the day and days of the week and enabling the discovery of seasonality effects on commuting throughout the year.…”

“…Mobile data analysis is an authoritative source of information for problem solving in the fields of human activity recognition, population dynamics, tourism, transport planning, traffic measurement, and public administration. Previously, mobile positioning data have been analysed in the context of residents' movements (Ahas, Aasa, Silm, & Tiru, ; Zonghao, Dongyuan, & Zhengyu, ), human home‐work commuting (Kung, Greco, Sobolevsky, & Ratti, ), automatic recognition of population activities (Chetty, White, & Akther, ; Lee & Cho, ), estimation of human trajectories (Hoteit, Secci, Sobolevsky, Ratti, & Pujolle, ; Larijani, Olteanu‐Raimond, Perret, Bredif, & Ziemlicki, ; Liu, Janssens, Wets, & Cools, ; Zilske & Nagel, ) and flows (Balzotti, Bragagnini, Briani, & Cristiani, ), as well as patterns of population dynamics (Deville et al, ; Trasarti et al, ).…”

Sensing and Forecasting Crowd Distribution in Smart Cities: Potentials and Approaches

“…Yet, building the OD matrix from aggregated mobility data is much more challenging than doing it from single user trajectory data. However, there are some promising approaches, such as in [4,5] based on the identification of the most likely set of movements explaining changes in the crowd density. Finally, knowing what are the patterns of the crowd flows with respect to a given moment of the day can be helpful for predicting the travel demand: in [6], for example, the authors propose a location recommendation system that infers personal preferences while at the same time taking in consideration the constraints imposed by traffic and road capacity.…”

Putting (Big) Data in Action: Saving Lives with Countrywide Population Movement Monitoring Using Mobile Devices during the COVID‐19 Crisis

“…The use of data from mobile phones to analyse physical mobility in a population is not new, [16][17][18][19] and the location of mobile devices, as a proxy for the users' geographical presence, has long been used for commercial purposes, such as for tailored advertising, as well as to inform subscribers about their daily activities, such as numbers of steps taken. 20 The opportunity that mobile phones and associated apps offer in combating the pandemic has already been recognised, 21 with applications such as the Chinese coronavirus "close contact detector" app, which brings together routine data on travel, case reports, and users' mobile phone location information to detect possible contacts with infected people, 22 or the South Korean official mobile phone apps, which are used similarly for agile contact tracing, but also as a means to monitor people in home quarantine.…”