Zhiyong Cui

TL;DR: A novel deep learning framework, Traffic Graph Convolutional Long Short-Term Memory Neural Network (TGC-LSTM), to learn the interactions between roadways in the traffic network and forecast the network-wide traffic state and shows that the proposed model outperforms baseline methods on two real-world traffic state datasets.

TL;DR: Comparisons with other classical and state-of-the-art models indicate that the proposed SBU-LSTM neural network achieves superior prediction performance for the whole traffic network in both accuracy and robustness.

TL;DR: Experimental results indicate that the proposed SBU-LSTM architecture, especially the two-layer BDLSTM network, can achieve superior performance for the network-wide traffic prediction in both accuracy and robustness and comprehensive comparison results show that the suggested data imputation mechanism in the RNN-based models can achieve outstanding prediction performance.

TL;DR: A novel framework for real-time traffic flow parameter estimation from aerial videos is proposed that achieves about 96% and 87% accuracy in estimating average traffic stream speed and vehicle count, respectively and achieves a fast processing speed that enables real- time traffic information estimation.

TL;DR: A comparison of the prediction precisions obtained for time granularities of 10, 15, and 30 min indicates that prediction precision increases with increasing time granularity, and this study can provide subway operators with insight into short-term passenger flow forecasting by leveraging deep learning models.