Improving Urban Traffic Speed Prediction Using Data Source Fusion and Deep Learning

Autor: Aniekan Essien, Sandra Sampaio, Pedro Sampaio, Ilias Petrounias

Publikováno v: BigComp
Essien, A, Petrounias, I, Sampaio, P & Sampaio, S 2019, Improving Urban Traffic Speed Prediction Using Data Source Fusion and Deep Learning . in 2019 IEEE International Conference on Big Data and Smart Computing, BigComp 2019-Proceedings ., 8679231, 2019 IEEE International Conference on Big Data and Smart Computing, BigComp 2019-Proceedings, IEEE Computer Society, Kyoto, Japan . https://doi.org/10.1109/bigcomp.2019.8679231

Traffic parameter forecasting is critical to effective traffic management but is a challenging task due to the stochasticity of traffic flow characteristics, especially in urban road networks. Traffic networks can be affected by external factors, suc

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::04ab4d3d91aecda1c9d752c7dcbc6971
https://doi.org/10.1109/bigcomp.2019.8679231

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