Modeling and simulation of the car-truck heterogeneous traffic flow based on a nonlinear car-following model. - Lan Liu 002; Liling Zhu; Da Yang 004 (Appl. Math. Comput., 2016)

Analysis of Different Graph Convolutional Network Prediction Models with Spatial Dependence Evaluation. - Yuting Chen; Jiannan Mao; Hao Huang 004; Qipeng Yan; Weike Lu; Lan Liu 002 (ITSC, 2021)

Mining the Graph Representation of Traffic Speed Data for Graph Convolutional Neural Network. - Jiannan Mao; Hao Huang 004; Yuting Chen; Weike Lu; Guoqiang Chen; Lan Liu 002 (ITSC, 2021)

Multi-precision traffic speed predictions via modified sequence to sequence model and spatial dependency evaluation method. - Jiannan Mao; Hao Huang 004; Weike Lu; Yuting Chen; Lan Liu 002 (Appl. Soft Comput., 2022)

A Dueling Deep Q-Network method for low-carbon traffic signal control. - Leilei Kang; Hao Huang 004; Weike Lu; Lan Liu 002 (Appl. Soft Comput., 2023)

Research on route hierarchical control strategy from the perspective of macroscopic traffic network. - Leilei Kang; Weike Lu; Lan Liu 002 (J. Intell. Transp. Syst., 2023)

Optimizing gate control coordination signal for urban traffic network boundaries using multi-agent deep reinforcement learning. - Leilei Kang; Hao Huang 004; Weike Lu; Lan Liu 002 (Expert Syst. Appl., 2024)

A dynamic graph deep learning model with multivariate empirical mode decomposition for network-wide metro passenger flow prediction. - Hao Huang 004; Jiannan Mao; Leilei Kang; Weike Lu; Sijia Zhang; Lan Liu 002 (Comput. Aided Civ. Infrastructure Eng., 2024)

MTLMetro: A Deep Multi-Task Learning Model for Metro Passenger Demands Prediction. - Hao Huang 004; Jiannan Mao; Ronghui Liu; Weike Lu; Tianli Tang; Lan Liu 002 (IEEE Trans. Intell. Transp. Syst., 2024)