Z
Zhengbing He
Researcher at Beijing University of Technology
Publications - 91
Citations - 3829
Zhengbing He is an academic researcher from Beijing University of Technology. The author has contributed to research in topics: Computer science & Traffic flow. The author has an hindex of 19, co-authored 68 publications receiving 2237 citations. Previous affiliations of Zhengbing He include Beijing Jiaotong University.
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Journal ArticleDOI
Learning Traffic as Images: A Deep Convolutional Neural Network for Large-Scale Transportation Network Speed Prediction.
TL;DR: Wang et al. as mentioned in this paper proposed a convolutional neural network (CNN)-based method that learns traffic as images and predicts large-scale, network-wide traffic speed with a high accuracy.
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Learning Traffic as Images: A Deep Convolutional Neural Network for Large-Scale Transportation Network Speed Prediction
TL;DR: The CNN can train the model in a reasonable time and, thus, is suitable for large-scale transportation networks and outperforms other algorithms by an average accuracy improvement of 42.91% within an acceptable execution time.
Journal ArticleDOI
Predicting station-level hourly demand in a large-scale bike-sharing network: A graph convolutional neural network approach
TL;DR: A novel Graph Convolutional Neural Network with Data-driven Graph Filter (GCNN-DDGF) model that can learn hidden heterogeneous pairwise correlations between stations to predict station-level hourly demand in a large-scale bike-sharing network is proposed.
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A data-driven lane-changing model based on deep learning
TL;DR: This may be the first work that comprehensively models LC using deep learning approaches and the results indicate that the proposed data-driven model is able to accurately predict the LC process of a vehicle.
Journal ArticleDOI
Trajectory data-based traffic flow studies: A revisit
TL;DR: The critical role of trajectory data (especially the next generation simulation (NGSIM) trajectory dataset) in the recent history of traffic flow studies is highlighted and the critical role at the microscopic/mesoscopic/macroscopic levels is highlighted.