Call detail records to characterize usages and mobility events of phone users

Abstract

International audienceCellular technologies are evolving quickly to constantly adapt to new usage and tolerate the load induced by the increasing number of phone applications. Understanding the mobile traffic is thus crucial to refine models and improve experiments. In this context, one has to understand the temporal activity of a user and the user movements. At the user scale, the usage is not only defined by the amount of calls but also by the user’s mobility. At a higher level, the base stations have a key role on the quality of service. In this paper, we analyze a very large Call Detail Records (CDR) over 12 months in Mexico. It contains 8 millions users and 5 billions of call events. Our first contribution is the study call duration and inter-arrival time parameters. Then, we assess user movements between consecutive calls (switching from a station to another one). Our study suggests that user mobility is pretty dependent on user activity. Furthermore, we show properties of the inter-call mobility by making an analysis of the call distribution

Approaches, technologies and importance of analysis of the number of train travellers

“…However, most studies have not focused on the rail in particular, but typically addressing travel in general. Mobile phone datasets allow deriving a statistical analysis of human activities at a fine level of details (Leo, Busson, Sarraute, & Fleury, 2016). It has been shown that cell phone data can be used to derive good estimates of dynamic quantities, such as travel times, train occupancy levels and origin-destination flows, for transportation studies (Aguilera et al, 2014).…”

Use of mobile phone data for analysis of number of train travellers

“…CDRs are generated by phone communication activities and contain relevant information about the activity (e.g., caller/callee, time, duration) and the location of the cell phone tower that handles the communication (Zhao et al, 2016). Studies have shown that CDR data can be used to study habits and mobility patterns of mobile users (Bianchi et al, 2016;Zhao et al, 2016), to study user movements (Leo et al, 2016), and to calculate commuting matrices with a very high level of accuracy (Frias-Martinez, et al, 2012). Studies have also looked at utilizing mobile data to estimate intra-city travel time (Kujala, Aledavood, & Saramäki, 2016) and have shown that mobile data could be employed as a real-time traffic monitoring tool (Järv et al, 2012).…”

“…Higuchi et al (2015) identify a number of innovative uses based on mobile devices, including several technologies that typically are found in smartphones, such as GPS, Wi-Fi, and Bluetooth. Mobile phone data sets allow for a statistical analysis of human activities at a fine level of detail (Leo et al, 2016).…”

Impact of Resource Blocks Allocation Strategies on Downlink Interference and SIR Distributions in LTE Networks: A Stochastic Geometry Approach

“…It models the number of communications in progress when both the interarrival of the communications and their duration follow an exponential distribution. Such assumptions are pertinent in cellular networks as it has been recently shown in [30]. We associate to these traffic demands several resource allocation strategies.…”