NDLI: Efficient deep learning based method for multi-lane speed forecasting: a case study in Beijing

Content Provider	IET Digital Library
Author	Lu, Wenqi Yi, Ziwei Liu, Wan Gu, Yuanli Rui, Yikang Ran, Bin
Abstract	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.
Starting Page	2073
Ending Page	2082
Page Count	10
ISSN	1751956X
Volume Number	14
e-ISSN	17519578
Issue Number	Issue 14, Dec (2020)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-its.2020.0410
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-its/14/14
Journal	IET Intelligent Transport Systems
Publisher Date	2021-01-18
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Automated Vehicle Highway System Beijing Clockwork Recurrent Neural Network Combination Method Combinatorial Mathematics Connected Vehicle Highway System Deep Learning (artificial Intelligence) Deep Learning Based Traffic Prediction Method Lane-level Traffic Flow Multilane Speed Forecasting Multilane Traffic Flow Prediction Optimal Input Time Window Random Forest Real Time System Real-time Multilane Traffic Condition Forecasting Recurrent Neural Nets RF-CWRNN Method Road Traffic Temporal Feature Traffic Engineering Computing Traffic Speed Prediction Training Time
Content Type	Text
Resource Type	Article
Subject	Law Transportation Environmental Science Mechanical Engineering

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