Learning Spatiotemporal Latent Factors of Traffic via Regularized Tensor Factorization: Imputing Missing Values and Forecasting

Learning Spatiotemporal Latent Factors of Traffic via Regularized Tensor Factorization: Imputing Missing Values and Forecasting

<p>Intelligent transportation systems are a key component in smart cities, and the estimation and prediction of the spatiotemporal traffic state is critical to capture the dynamics of traffic congestion, i.e., its generation, propagation and mitigation, in order to increase operational efficie...

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Bibliographic Details
Main Author: Abdelkader Baggag (16864140) (author)
Other Authors: Sofiane Abbar (14153043) (author), Ankit Sharma (578833) (author), Tahar Zanouda (14153046) (author), Abdulaziz Al-Homaid (16864143) (author), Abhiraj Mohan (16864146) (author), Jaideep Srivastava (455466) (author)
Published: 2019
Subjects:
Built environment and design
Urban and regional planning
Information and computing sciences
Data management and data science
Distributed computing and systems software
Forecasting
Junctions
Roads
Real-time systems
Regularization
Sensors
Tensile stress
Traffic forecasting
Tensor decomposition
Traffic monitoring
Urban areas
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