Short‐term traffic congestion prediction with Conv–BiLSTM considering spatio‐temporal features
Convolutional LSTM based transportation mode learning from raw GPS trajectories
Spatio‐temporal expand‐and‐squeeze networks for crowd flow prediction in metropolis
Multi‐graph convolutional network for short‐term passenger flow forecasting in urban rail transit
Multi‐task deep learning with optical flow features for self‐driving cars
Pedestrian motion recognition via Conv‐VLAD integrated spatial‐temporal‐relational network
Prediction of vehicle energy consumption on a planned route based on speed features forecasting
Hybrid strategy for traffic light detection by combining classical and self‐learning detectors
LightGBM‐based model for metro passenger volume forecasting
Deep learning enabled vehicle trajectory map‐matching method with advanced spatial–temporal analysis
Fatigue driving detection model based on multi‐feature fusion and semi‐supervised active learning
Urban rail transit passenger flow forecast based on LSTM with enhanced long‐term features
Using GLCM features in Haar wavelet transformed space for moving object classification