Use of Mobile Phone Data to Estimate Visitors Mobility Flows

Proceedings of the ACM SIGKDD International Workshop on Urban Computing - UrbComp '12, 2012

Commuting matrices are key for a variety of fields, including transportation engineering and urban planning. Up to now, these matrices have been typically generated from data obtained from surveys. Nevertheless, such approaches typically involve high costs which limits the frequency of the studies. Cell phones can be considered one of the main sensors of human behavior due to its ubiquity, and as a such, a pervasive source of mobility information at a large scale. In this paper we propose a new technique for the estimation of commuting matrices using the data collected from the pervasive infrastructure of a cell phone network. Our goal is to show that we can construct cell-phone generated matrices that capture the same patterns as traditional commuting matrices. In order to do so we use optimization techniques in combination with a variation of Temporal Association Rules. Our validation results show that it is possible to construct commuting matrices from call detail records with a high degree of accuracy, and as a result our technique is a cost-effective solution to complement traditional approaches.

Computer Networks, 2014

PLoS ONE, 2014

The pervasive use of new mobile devices has allowed a better characterization in space and time of human concentrations and mobility in general. Besides its theoretical interest, describing mobility is of great importance for a number of practical applications ranging from the forecast of disease spreading to the design of new spaces in urban environments. While classical data sources, such as surveys or census, have a limited level of geographical resolution (e.g., districts, municipalities, counties are typically used) or are restricted to generic workdays or weekends, the data coming from mobile devices can be precisely located both in time and space. Most previous works have used a single data source to study human mobility patterns. Here we perform instead a cross-check analysis by comparing results obtained with data collected from three different sources: Twitter, census and cell phones. The analysis is focused on the urban areas of Barcelona and Madrid, for which data of the three types is available. We assess the correlation between the datasets on different aspects: the spatial distribution of people concentration, the temporal evolution of people density and the mobility patterns of individuals. Our results show that the three data sources are providing comparable information. Even though the representativeness of Twitter geolocated data is lower than that of mobile phone and census data, the correlations between the population density profiles and mobility patterns detected by the three datasets are close to one in a grid with cells of 2 × 2 and 1 × 1 square kilometers. This level of correlation supports the feasibility of interchanging the three data sources at the spatio-temporal scales considered.

NETMOB conference, Oct. 10-11, 2011, MIT, Cambridge, MA

Transportation Research Part C: Emerging Technologies, 2015

In this work, we present methods to estimate average daily origin-destination trips from triangulated mobile phone records of millions of anonymized users. These records are first converted into clustered locations at which users engage in activities for an observed duration. These locations are inferred to be home, work, or other depending on observation frequency , day of week, and time of day, and represent a user's origins and destinations. Since the arrival time and duration at these locations reflect the observed (based on phone usage) rather than true arrival time and duration of a user, we probabilistically infer departure time using survey data on trips in major US cities. Trips are then constructed for each user between two consecutive observations in a day. These trips are multiplied by expansion factors based on the population of a user's home Census Tract and divided by the number of days on which we observed the user, distilling average daily trips. Aggregating indi-viduals' daily trips by Census Tract pair, hour of the day, and trip purpose results in trip matrices that form the basis for much of the analysis and modeling that inform transportation planning and investments. The applicability of the proposed methodology is supported by validation against the temporal and spatial distributions of trips reported in local and national surveys.

Transportmetrica A: Transport Science, 2020

Exploring strengths and weaknesses of mobility inference from mobile phone data vs. travel surveys Origin-destination (OD) matrices serve as a basis for travel demand modelling. Traditionally, they are derived from travel surveys that collect detailed trip information but with several shortcomings. Mobile phones are regarded as a useful source of information on people's daily mobility. This work explores the use of mobile data in the context of mobility studies by comparing matrices derived from both types of sources over the same region. The results reveal many common features in the trip information. Moreover, although the use of mobile technology may raise questions for short trips, the huge representativeness of this technology captures the mobility in OD connections extensively regardless the area. This is crucial for non-populated areas (e.g. industrial parks or educational campuses), which constitute important mobility hotspots. Based on these findings, an applicable data fusion approach to obtain the optimum accuracy from these heterogeneous sources is presented and applied.

IFAC-PapersOnLine, 2018

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.

ISPRS International Journal of Geo-Information, 2018

The mobility patterns and trip behavior of people are usually extracted from data collected by traditional survey methods. However, these methods are generally costly and difficult to implement, especially in developing cities with limited resources. The massive amounts of call detail record (CDR) data passively generated by ubiquitous mobile phone usage provide researchers with the opportunity to innovate alternative methods that are inexpensive and easier and faster to implement than traditional methods. This paper proposes a method based on proven techniques to extract the origin-destination (OD) trips from the raw CDR data of mobile phone users and process the data to capture the mobility of those users. The proposed method was applied to 3.4 million mobile phone users over a 12-day period in Mozambique, and the data processed to capture the mobility of people living in the Greater Maputo metropolitan area in different time frames (weekdays and weekends). Subsequently, trip generation maps, attraction maps, and the OD matrix of the study area, which are all practically usable for urban and transportation planning, were generated. Furthermore, spatiotemporal interpolation was applied to all OD trips to reconstruct the population distribution in the study area on an average weekday and weekend. Comparison of the results obtained with actual survey results from the Japan International Cooperation Agency (JICA) indicate that the proposed method achieves acceptable accuracy. The proposed method and study demonstrate the efficacy of mining big data sources, particularly mobile phone CDR data, to infer the spatiotemporal human mobility of people in a city and understand their flow pattern, which is valuable information for city planning.

International Journal of Intelligent Transportation Systems Research, 2013

This study develops cell phone location tracking algorithms from a large cell phone network database to estimate the dynamic origin-destination (O-D) traffic flow and travel demand data as well as commuting traffic. A case study was conducted in the Kansas Metro Corridor to analyze the feasibility of using cell phone data to track crossregion (cities) traffic activities, and to derive the O-D traffic, travel demand by time-of-day and commuting traffic data along the traffic corridor based on a 6 week observation period. The results found that the available cell phone network data detected about 17.6% of the daily traffic data compared to the AADT data along the Kansas Metro Corridor. Approximately 58% of the total traffic was determined to be O-D traffic through the study corridor. This indicates that most of the traffic is from three major regions (the Kansas City metropolitan area, the City of Topeka, KS and the City of Lawrence, KS) and the estimated dynamic travel demand can be used for public transportation system planning and schedule arrangements. Due to the low location resolution using the network-based cell phone network, the use of cell phone network in collecting traffic data would be more feasible for long distance or inter-city trips. A longer observation period is also needed to increase the cell phone sample size and could be useful to obtain stable cell phone traffic, reducing the bias of the data. be used in both vehicle and traveler tracking. Previous studies [1-4] have evaluated the application of cell phones in travel speed and travel time estimation, as well as identifying congested sections. Several researchers have examined different types of cell phone network data in deriving O-D matrices. White and Wells [5] proposed a pilot study which investigated the feasibility of using phone billing data to obtain O-D information in the Kent area of the United Kingdom. The billing data included the location of signal towers indicating where and when the calls were made. It demonstrated that it is possible to obtain O-D information from cell phone data. Caceres et al. [6] also assessed the feasibility of using cell phone location databases to infer O-D matrices via cell phones that were switched on all the time. A cell phone communication network simulator was used to simulate and extract the O-D matrix from the phone network. Zhang et al.

2019

Big cities show a wide public transportation network, allowing people to travel within cities. However, with the overpopulation of urban areas, the demand for new mobility strategies has increasing. Every day, citizens need to commute fast, easily and comfortable, which is not always easy due to the complexity of the transport network. The main objective of this paper, is to explore the ability of Big Data technologies to cope with data collected from public transportation, by inferring automatically and continuously, complex mobility patterns about human mobility, in the form of insightful indicators (such as connections, transshipments or pendular movements), creating a new perspective in public transports data analytics. The presented work, focus on the Lisbon metropolitan area, aiming to analyze the demand and supply side of transportation network of Lisbon, considering ticketing data transactions provided by the different transportation