Unveiling large-scale commuting patterns based on mobile phone cellular network data. (December 2020)

• Record Type: Journal Article
• Authors: Hadachi, Amnir; Pourmoradnasseri, Mozhgan; Khoshkhah, Kaveh
• Is Part Of: Journal of transport geography. Volume 89(2020)
• Publication Date: 2020-12
• Subjects: Commuting patterns -- CDR data -- OD-Matrix -- Large-scale mobility -- Hidden Markov model -- Mobile Cellular Network data; Transportation -- Periodicals; Telecommunication -- Periodicals; Transport -- Périodiques; Télécommunications -- Périodiques; Telecommunication; Transportation; Periodicals; 388
• DOI: 10.1016/j.jtrangeo.2020.102871
• Languages: English
• ISSNs: 0966-6923

Abstract

Abstract: In this study, with Estonia as an example, we established an approach based on Hidden Markov Model to extract large-scale commuting patterns at different geographical levels using a massive amount of mobile phone cellular network data, which is referred to as Call detail record (CDR). The proposed model is designed for reconstructing and transforming the trajectories extracted from the CDR data. This step allowed us to perform origin-destination matrix extraction among different geographical levels, which helped in depicting the commuting patterns. Besides, we introduced different techniques for analyzing the commuting at the urban level. Our results unveiled that there is great potential behind mobile data of the cellular networks after transforming it into meaningful mobility patterns. That can easily be used for understanding urban dynamics, large-scale daily commuting and mobility. The aggressive development and growth of ubiquitous mobile sensing have generated valuable data that can be used with our approach for providing answers and solutions to the growing problems of transportation, urbanization and sustainability.