Các bài viết có thể truy cập công khai - Bin RanTìm hiểu thêm
Không có ở bất kỳ nơi nào: 124
A hybrid deep learning based traffic flow prediction method and its understanding
Y Wu, H Tan, L Qin, B Ran, Z Jiang
Transportation Research Part C: Emerging Technologies 90, 166-180, 2018
Các cơ quan ủy nhiệm: National Natural Science Foundation of China
Energy-efficient metro train rescheduling with uncertain time-variant passenger demands: An approximate dynamic programming approach
J Yin, T Tang, L Yang, Z Gao, B Ran
Transportation Research Part B: Methodological 91, 178-210, 2016
Các cơ quan ủy nhiệm: National Natural Science Foundation of China
A dynamic lane-changing trajectory planning model for automated vehicles
D Yang, S Zheng, C Wen, PJ Jin, B Ran
Transportation Research Part C: Emerging Technologies 95, 228-247, 2018
Các cơ quan ủy nhiệm: National Natural Science Foundation of China
Day-ahead traffic flow forecasting based on a deep belief network optimized by the multi-objective particle swarm algorithm
L Li, L Qin, X Qu, J Zhang, Y Wang, B Ran
Knowledge-Based Systems 172, 1-14, 2019
Các cơ quan ủy nhiệm: National Natural Science Foundation of China
Tensor based missing traffic data completion with spatial–temporal correlation
B Ran, H Tan, Y Wu, PJ Jin
Physica A: Statistical Mechanics and its Applications 446, 54-63, 2016
Các cơ quan ủy nhiệm: National Natural Science Foundation of China
Dynamic driving risk potential field model under the connected and automated vehicles environment and its application in car-following modeling
L Li, J Gan, X Ji, X Qu, B Ran
IEEE transactions on intelligent transportation systems 23 (1), 122-141, 2020
Các cơ quan ủy nhiệm: National Natural Science Foundation of China
Exploring AIS data for intelligent maritime routes extraction
Z Yan, Y Xiao, L Cheng, R He, X Ruan, X Zhou, M Li, R Bin
Applied Ocean Research 101, 102271, 2020
Các cơ quan ủy nhiệm: National Natural Science Foundation of China
A novel car-following control model combining machine learning and kinematics models for automated vehicles
D Yang, L Zhu, Y Liu, D Wu, B Ran
IEEE Transactions on Intelligent Transportation Systems 20 (6), 1991-2000, 2018
Các cơ quan ủy nhiệm: National Natural Science Foundation of China
A novel lane-changing model of connected and automated vehicles: Using the safety potential field theory
L Li, J Gan, K Zhou, X Qu, B Ran
Physica A: Statistical Mechanics and its Applications 559, 125039, 2020
Các cơ quan ủy nhiệm: National Natural Science Foundation of China
Drivers’ visual comfort at highway tunnel portals: A quantitative analysis based on visual oscillation
D Zhigang, Z Zheng, M Zheng, B Ran, X Zhao
Transportation Research Part D: Transport and Environment 31, 37-47, 2014
Các cơ quan ủy nhiệm: National Natural Science Foundation of China
Understanding individualization driving states via latent Dirichlet allocation model
Z Chen, Y Zhang, C Wu, B Ran
IEEE Intelligent Transportation Systems Magazine 11 (2), 41-53, 2019
Các cơ quan ủy nhiệm: National Natural Science Foundation of China
Risk perception and the warning strategy based on safety potential field theory
L Li, J Gan, Z Yi, X Qu, B Ran
Accident Analysis & Prevention 148, 105805, 2020
Các cơ quan ủy nhiệm: National Natural Science Foundation of China
Traffic speed prediction for intelligent transportation system based on a deep feature fusion model
L Li, X Qu, J Zhang, Y Wang, B Ran
Journal of Intelligent Transportation Systems 23 (6), 605-616, 2019
Các cơ quan ủy nhiệm: National Natural Science Foundation of China
Coupled application of generative adversarial networks and conventional neural networks for travel mode detection using GPS data
L Li, J Zhu, H Zhang, H Tan, B Du, B Ran
Transportation Research Part A: Policy and Practice 136, 282-292, 2020
Các cơ quan ủy nhiệm: National Natural Science Foundation of China
Analysis of global marine oil trade based on automatic identification system (AIS) data
Z Yan, Y Xiao, L Cheng, S Chen, X Zhou, X Ruan, M Li, R He, B Ran
Journal of Transport Geography 83, 102637, 2020
Các cơ quan ủy nhiệm: National Natural Science Foundation of China
A comparison of traffic flow prediction methods based on DBN
H Tan, X Xuan, Y Wu, Z Zhong, B Ran
CICTP 2016, 273-283, 2016
Các cơ quan ủy nhiệm: National Natural Science Foundation of China
A deep fusion model based on restricted Boltzmann machines for traffic accident duration prediction
L Li, X Sheng, B Du, Y Wang, B Ran
Engineering Applications of Artificial Intelligence 93, 103686, 2020
Các cơ quan ủy nhiệm: National Natural Science Foundation of China
Modeling and analysis of the lane-changing execution in longitudinal direction
D Yang, L Zhu, B Ran, Y Pu, P Hui
IEEE transactions on intelligent transportation systems 17 (10), 2984-2992, 2016
Các cơ quan ủy nhiệm: National Natural Science Foundation of China
Cascading failure analysis and robustness optimization of metro networks based on coupled map lattices: A case study of Nanjing, China
Y Shen, G Ren, B Ran
Transportation 48 (2), 537-553, 2021
Các cơ quan ủy nhiệm: National Natural Science Foundation of China
A deep reinforcement learning‐based distributed connected automated vehicle control under communication failure
H Shi, Y Zhou, X Wang, S Fu, S Gong, B Ran
Computer‐Aided Civil and Infrastructure Engineering 37 (15), 2033-2051, 2022
Các cơ quan ủy nhiệm: National Natural Science Foundation of China
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