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Unveiling large-scale commuting patterns based on mobile phone cellular network data

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  • Hadachi, Amnir
  • Pourmoradnasseri, Mozhgan
  • Khoshkhah, Kaveh

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.

Suggested Citation

  • Hadachi, Amnir & Pourmoradnasseri, Mozhgan & Khoshkhah, Kaveh, 2020. "Unveiling large-scale commuting patterns based on mobile phone cellular network data," Journal of Transport Geography, Elsevier, vol. 89(C).
    • Handle: RePEc:eee:jotrge:v:89:y:2020:i:c:s0966692320309480
    DOI: 10.1016/j.jtrangeo.2020.102871
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    References listed on IDEAS

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    1. Zahnow, Renee & Abewickrema, Wanuji, 2023. "Examining regularity in vehicular traffic through Bluetooth scanner data: Is the daily commuter the regular road user?," Journal of Transport Geography, Elsevier, vol. 109(C).

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