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Missing Value Imputation for Traffic-Related Time Series Data Based on a Multi-View Learning Method
2 references·1 citing·14 second hop
Missing Value Imputation for Traffic-Related Time Series Data Based on a Multi-View Learning Method (2018)Missing Value Imputation for Traffic-Rela…Understanding urban mobility patterns with a probabilistic tensor factorization framework (2016)Understanding urban mobility …On the First Law of Geography: A Reply (2004)On the First Law of Geography…A variational autoencoder solution for road traffic forecasting systems: Missing data imputation, dimension reduction, model selection and anomaly detection (2020)A variational autoencoder sol…Human behavior and the principle of least effort (1949)Field Theory in Social Science (1953)The Constitution of Society. (1988)Photocopiers and Water-coolers: The Affordances of Informal Interaction (2007)Migration influenced by environmental change in Africa: A systematic review of empirical evidence (2019)How does digital finance affect industrial structure upgrading? Evidence from Chinese prefecture-level cities (2023)Activity‐based approaches to travel analysis: conceptual frameworks, models, and research problems (1992)Understanding individual human mobility patterns (2008)Discovering regions of different functions in a city using human mobility and POIs (2012)Understanding Urban Dynamics via Context-Aware Tensor Factorization with Neighboring Regularization (2019)Urban Human Mobility (2019)Spatial-temporal potential exposure risk analytics and urban sustainability impacts related to COVID-19 mitigation: A perspective from car mobility behaviour (2020)Missing traffic data: comparison of imputation methods (2013)2017 IEEE International Conference on Big Data (Big Data) (2017)
this work references cited by second hop

References

  • 2004On the First Law of Geography: A Reply500 cited
  • 2016Understanding urban mobility patterns with a probabilistic tensor factorization framework175 cited

Cited by

  • 2020A variational autoencoder solution for road traffic forecasting systems: Missing data imputation, dimension reduction, model selection and anomaly detection166 cited

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