Abstract
Travel behavior plays a crucial role in urban planning and epidemic control. Extensive researches concerning mobile phone data have been performed with the increasing widespread use of mobile phones. However, data sparsity and localization noise pose a great challenge to further studies. In this research, mobile phone call record data (CRD) of 60 days obtained from Shenzhen, China has been put into use. By identifying the home and work location for users, location information for every timeslot was labeled. First, mobility topic was extracted by latent Dirichlet allocation (LDA) model. Then, affinity propagation (AP) was used to analyze users’ mobility patterns from mobility topic distribution. The results revealed that clustering on the level of mobility topic outperformed directly clustering location sequence information. This method could effectively mitigate the adverse impact brought by missing information. Finally, 25 and 17 patterns are found in weekday and weekend, respectively. Representative features were captured from each pattern. By measuring the accuracy of the representative feature, it can be concluded that mobility feature after clustering is capable of describing the main mobility patterns.
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Acknowledgments
This research was sponsored by National Natural Science Foundation of China (71171147) and Fundamental Research Funds for the Central Universities.
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Yang, C., Zhang, Y., Ukkusuri, S.V., Zhu, R. (2019). Mobility Pattern Identification Based on Mobile Phone Data. In: Ukkusuri, S., Yang, C. (eds) Transportation Analytics in the Era of Big Data. Complex Networks and Dynamic Systems, vol 4. Springer, Cham. https://doi.org/10.1007/978-3-319-75862-6_9
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DOI: https://doi.org/10.1007/978-3-319-75862-6_9
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