How the quality of call detail records influences the detection of commuting trips

• Main Authors: Joel Pires, Aldina Piedade, Marco Veloso, Santi Phithakkitnukoon, Zbigniew Smoreda, Carlos Bento
• Format: Book Series
• Published: 2020
• Subjects: Computer Science; Mathematics
• Online Access: https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85072886814&origin=inward; http://cmuir.cmu.ac.th/jspui/handle/6653943832/67760
• Institution: Chiang Mai University

© Springer Nature Switzerland AG 2019. Call Detail Records provide information on the origin and destination of voice calls at the level of the base stations in a cellular network. The low spatial resolution and sparsity of these data constitutes challenges in using them for mobility characterization. In this paper we analyze the impact on the detection of commuting patterns of four parameters: density of base stations per square kilometer, average number of calls made and received per day per user, regularity of these calls, and the number of active days per user. In this study, we use CDRs collected from Portugal over a period of fourteen months. Based on the result of our study, we are able to infer the commuting patterns of 10.42% of the users in our data set by considering users with at least 7.5 calls per day. Accounting users with over 7.5 calls per day, on average, does not result in a significant improvement on the result. Concerning the inference of routes in the home-to-work direction and vice versa, we examined users who connect to the cellular network, on average, every 17 days to everyday, which results in a 0.27% to 11.1% of trips detected, respectively. Finally, we found that with 208 days of data we are able to infer 5.67% of commuting trips and this percentage does not improve significantly by considering more data.