Day-ahead traffic flow forecasting based on a deep belief network optimized by the multi-objective particle swarm algorithm

@article{Li2019DayaheadTF,
  title={Day-ahead traffic flow forecasting based on a deep belief network optimized by the multi-objective particle swarm algorithm},
  author={Linchao Li and Lingqiao Qin and Xu Qu and Jian Zhang and Yonggang Wang and B. Ran},
  journal={Knowl. Based Syst.},
  year={2019},
  volume={172},
  pages={1-14}
}
Abstract Traffic flow forecasting is a necessary part in the intelligent transportation systems in supporting dynamic and proactive traffic control and making traffic management plan. However, most of the previous studies attempting to build traffic flow forecasting models focus on short-term forecasting as the next step. In this paper, a deep feature leaning approach is proposed to predict short-term traffic flow in the following multiple steps using supervised learning techniques. To achieve… Expand
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