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Efficient deep learning based method for multi‐lane speed forecasting: a case study in Beijing
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DOI：10.1049/iet-its.2020.0410.png)
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Real‐time and accurate multi‐lane traffic condition forecasting is of great importance to the connected and automated vehicle highway system. However, the majority of existing deep learning based traffic prediction methods focus on pursuing the precision of the methods while neglect to improve the efficiency of the methods. To achieve the high accuracy and high efficiency of multi‐lane traffic flow prediction simultaneously, this study proposes a novel combination method via the integration of the clockwork recurrent neural network (CWRNN) and random forest (RF) method, which is RF‐CWRNN. To the best of the authors’ knowledge, this is the first time that the CWRNN is introduced to capture the temporal feature of lane‐level traffic flow and make traffic speed prediction. Meanwhile, the RF method is employed to measure the temporal relevance of the traffic flow and determine the optimal input time window. To verify the performance of the RF‐CWRNN method, the ground‐truth data of the expressways in Beijing were utilised to carry out experiments. The results indicate that the RF‐CWRNN method is superior to the baseline models in terms of accuracy and robustness. Besides, the proposed method can save plenty of training time compared with the classical long short‐term memory neural network.
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