Using CDR Data to Understand Post-pandemic Mobility Patterns
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-49011-8_35
Reference22 articles.
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2. Marra, A., Sun, L., Corman, F.: The impact of COVID-19 pandemic on public transport usage and route choice: evidences from a long-term tracking study in urban area. Transp. Policy 16, 258–268 (2022). https://doi.org/10.1016/j.tra.2022.03.024
3. Ferreira, G., Alves, A., Veloso, M., Bento, C.: Identification and classification of routine locations using anonymized mobile communication data. ISPRS Int. J. Geo-In 11(4), 228 (2022). https://doi.org/10.3390/ijgi11040228
4. Montero, L., Ros-Roca, X., Herranzc, R., Barcelób, J.: Fusing mobile phone data with other data sources to generate input Fusing mobile phone data with other data sources to generate input OD matrices for transport models. Transp. Res. Procedia 417–424 (2019). https://doi.org/10.1016/j.trpro.2018.12.211
5. Ranjan, G., Zang, H., Zhang, Z., Bolot, J.: Are call detail records biased for sampling human mobility? SIGMOBILE Mob. Comput. Commun. Rev. 16(3), 33–44 (2012). https://doi.org/10.1145/2412096.2412101
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