Network‐wide traffic speed forecasting: 3D convolutional neural network with ensemble empirical mode decomposition,Computer-Aided Civil and Infrastructure Engineering

当前位置： X-MOL 学术 › Comput. Aided Civ. Infrastruct. Eng. › 论文详情

Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)

Network‐wide traffic speed forecasting: 3D convolutional neural network with ensemble empirical mode decomposition
Computer-Aided Civil and Infrastructure Engineering ( IF 9.6 ) Pub Date : 2020-05-30 , DOI: 10.1111/mice.12575
Shuaichao Zhang ₁ , Lingxiao Zhou ₁ , Xiqun (Michael) Chen ₁ , Lei Zhang ₂ , Li Li _{3,

4} , Meng Li ₅

Affiliation

Accurate traffic speed forecasting is one of the most critical tasks in proactive traffic management and the deployment of advanced traveler information systems. This paper proposes a hybrid forecasting approach named DeepEnsemble by integrating the three‐dimensional convolutional neural network (3D CNN) with ensemble empirical mode decomposition (EEMD). There are four steps in this hybrid approach. First, EEMD is adopted to decompose the complex traffic speed time series data with noise into several intrinsic mode functions (IMFs) and a residue. Second, a three‐dimensional tensor is established and fed into 3D CNN for prediction. Third, the output of 3D CNN prediction is obtained by a linear combination of the results of all components. Finally, the 3D CNN prediction output, external features, and historical features are fused to predict the network‐wide traffic speed simultaneously. The proposed DeepEnsemble approach is tested on the three‐month traffic speed series data of a real‐world large‐scale urban expressway network with 308 traffic flow detectors in Beijing, China. The experimental results indicate that DeepEnsemble outperforms the state‐of‐the‐art network‐wide traffic speed forecasting models. 3D CNN learns temporal, spatial, and depth information better than 2D CNN. Moreover, forecasting accuracy can be improved by employing EEMD. DeepEnsemble is a promising model with scalability and portability for network‐wide traffic speed prediction and can be further extended to conduct traffic status monitoring and congestion mitigation strategies.

更新日期：2020-05-30

点击分享查看原文

点击收藏

阅读更多本刊最新论文本刊介绍/投稿指南

全部期刊列表>>