Human Mobility Behavior and Predicting Amount of Trips Based on Mobile Data Records

This paper reveals the human mobility behavior in the metropolitan area of Rio de Janeiro, Brazil. The base for this study is the mobile phone data provided by one of the largest mobile carriers in Brazil. Mobile phone data comprises a reasonable variety of information, including data about time and location for call activity throughout urban areas. This information might be used to build users’ trajectories over the time, describing the major characteristics of the urban mobility within the city. A variety of distribution analyses is presented in this paper aiming clearly describes the most relevant characteristics of the overall mobility in the metropolitan area of Rio de Janeiro. In addition to that, methods from physics to describe trends in trips such as gravity and radiation models were computed and compared in terms of granularity of the geographic scales and also in relation to traditional data mining approach such as linear regressions. A brief comparison in terms of performance in predicting the amount of trips between pairs of locations is presented at the end. In analytics terms, this human mobility study is a new approach to better understand consumers’ behavior. The most often approach to perform such analysis is by using several profile information. How customers use products and services, when, in which frequency, recency and amount, how they pay; if they pay; how long they have been using and so forth. This set of information is quite individual and definitely describes the consumer’s behavior in the company’s relationship perspective. But, how customers relate to each other, how they affect to each other when using products and services? The first set of information describes who the customers are. The second one describes “how” they are. That question can be answered by using a social network analysis approach, describing how customers relate to each other. INTRODUCTION AND THE PROBLEM STATEMENT Human mobility analysis reveals relevant knowledge about subscribers behavior as well as urban planning, traffic forecasting or spread diseases (González et al 2008). Human mobility studies based on call detail records may also disclosure an approximation of the human motion within particular geographic areas such as metropolitan and urban areas, big cities, entire states and even countries, including mobility behavior and migration trends (Simini et al 2012). This particular human mobility study was conducted by using mobile phone data from one of the largest telecommunications company in Brazil. The overall analyses were performed upon six months of call detail records, revealing the average pattern of behavior for users travelling throughout the city over the time. The total amount of records handled in this study is about 3.1 billion records, comprising 2.1 million subscribers, handed out through 5,332 towers cells. The analysis of the human mobility in urban areas may arises relevant knowledge about the population’s behavior in terms of motion and displacements (Rubio et al 2013), allowing companies and government organizations in better planning traffic and public transportation, optimizing telecommunications’ networks, deeper understanding the vectors of spread diseases, accurately defining procedures in case of unexpected scenarios such as disasters and catastrophes, and preparing big events such as World Cup and Olympic Games, as is the case of the city of Rio de Janeiro. Many researches have been carried on human mobility studies in developed countries (Liu et al 2013). However, much less is known about the urban mobility and human motion in developing countries such as in Brazil. In this study we aim to cover a set of distribution analyses in order to describe the mobility patterns in a big metropolitan area like the city of Rio de Janeiro. By describing the overall human mobility pattern in Rio de Janeiro, it is possible to compare the outcomes from developed and developing cities and therefore to analyze possible differences in trends of human displacements. Another goal in this paper is to apply approaches from physics such as gravitation and radiation models to better understand mobility trends and predict trips between locations. In addition to the physics’ models, a set of data mining models based on regressions were also applied and the outcomes were compared in terms of trips prediction between locations. Human Mobility based on Call Detail Records 2 Understanding human mobility behavior, trends in amount of trips between locations, and overall displacements within big cities, such as Rio de Janeiro, is a quite good foundation to plan traffic routes, improve public transportation systems and network communications, particular toward to big events forthcoming such as World Cup in 2014 and the Olympic Games in 2016. METHODOLOGY AND DATA PREPARATION Mobile phone data provides a set of information about caller and called numbers, assigning them to tower cells spread out through urban areas. These tower cells hand out the calls made by the subscribers, and give us some useful information about their geographic locations in specific points of time. The range of information mapped for the tower cells is particular for each mobile carrier, providing basically data about latitude and longitude, radius, and address description such as neighborhood, district, city, county, and state. In this study, we are taking into account just the information about the subscribers for the mobile carrier which has provided the mobile phone data. Upon this data, we have the tower cell when the subscribers make or receive calls, indicating the geographic position they were at the particular time of the call. Even though it is a sort of approximation, recent studies (Candia et al, 2008) shows that by using the appropriate techniques, mobile phone data may offer the possibility of characterizing statistically the human trajectories and travels in a urban area scale. Over the day, subscribers can make and receive calls in different periods of time, but not always when they are moving. Also, the distribution of the call activity follows a sort of a power law, where most of the subscribers make and receive a few amount of calls over the time and a few subscribers make and receive a significant number of calls. Also, mobile phone records are sparse over the day and hold indefinite gaps in space and time. For this reason, we decide to monthly aggregate the call activity in order to assembly the travel history for the subscribers. By doing this, calls received and made in different days, keep just the call time information, building therefore a possible trajectory over the day. There is a differentiation between weekday and weekend in doing this aggregation. Thus, calls made and received from Monday to Friday are aggregated by time period slots of one hour, as well as calls made and received on Saturday and Sunday. Several studies (Schneider, 2013) (Jiang, 2013) show that most of the individuals have a recurrent travel activity, visiting in most of the times the same subset of locations, and in very few times different locations than the usual ones. Therefore, we believe that the method to reduce the sparsity of the calls and the spatio gaps by merging days keeping the time period still reveals the average subscribers’ behavior in terms of urban mobility. Upon this approach, if a particular subscriber makes a call on Tuesday at 1 pm at location a, then another call on Wednesday at 10 am at location b, and receives a call on Thursday at 4pm at location c and finally receives another call on Friday at 6 pm at location d, then a possible trajectory for this subscriber would be the path b – a – c – d. Considering the assumption of the recurrent trajectories and frequent displacements, we may assume that this particular subscriber is most likely to be at location b around 10 am, at location a around 1 pm, at location c around 4 pm and at location d around 6 pm. This assumption is important because as a matter of fact, subscribers don’t make and receive calls anytime they move. We believe that this approach to aggregate mobile phone data consists in a reasonable and accurate method to diminish the sparsity and the spatio gaps within the data in building frequent trajectories and performing human mobility analysis over the time. Hence, all six months were aggregated considering this aggregation approach, discarding the day and keeping the time period, splitting then the data aggregation process into two distinct datasets, containing data for weekdays and weekends. For further evaluation, we kept the weekday allowing us to perform distinct behavioral analyses for weekdays and weekends in terms of urban mobility. In building the trajectories for all subscribers, some cleansing constraints were put in place in order to make the mobility data even more realistic, even though increasing a bit its sparsity. All calls were fitted into slots of one hour. A call at 1:32 pm then is flagged as a call in the time period 1 (between 1 and 2 pm), and so on. Locations visited just one time over the timeframe were discarded from the analysis. These locations might represent an unusual behavior of the subscribers in traveling throughout urban areas. Also, once the information about call activity was summed, the most frequent visited location was considered for each time slot. For instance, a particular subscriber s may visit location a 10 times during the timeframe analyzed at the time period 1, location b 5 times at the same time period, and location c just 2 times. Once location a is the most visited at the time period 1, we assume that this location is the most likely position to find subscriber s between 1 and 2 pm. Finally, once we are concern about the subscribers’ motion, if a particular subscriber has the same most frequent location in consecutive time slots we keep just the first one. For example, if subscriber s has location a as