DDFM: A Novel Perspective on Urban Travel Demand Forecasting Based on the Ensemble Empirical Mode Decomposition and Deep Learning. - Rongkun Ye; Zhihao Xu 002; Junjie Pang (ICBDT, 2022)

Prediction of Crowd Flow in Key Areas Based on a Spatial-temporal Convolutional Model. - Xue Liu; Haokai Sun; Rongkun Ye (ITSC, 2022)

Socially Acceptable Trajectory Prediction for Scene Pedestrian Gathering Area. - Rongkun Ye; Zhiqiang Lv; Aite Zhao; Jianbo Li (WASA (1), 2022)

Taxi travel time prediction based on fusion of traffic condition features. - Zhaoyu Sheng; Zhiqiang Lv; Jianbo Li; Zhihao Xu 002; Haokai Sun 002; Xue Liu; Rongkun Ye (Comput. Electr. Eng., 2023)

MT-CNN: A Lightweight Spatial-Temporal Convolutional Neural Network for Deep Learning of Complex Trajectory Distributions based on Area Partitioning. - Rongkun Ye; Zhiqiang Lv; Zhihao Xu 002; Jianbo Li (IJCNN, 2024)