On the impact of cooperative autonomous vehicles in improving freeway merging: a modified intelligent driver model-based approach M Zhou, X Qu, S Jin IEEE Transactions on Intelligent Transportation Systems 18 (6), 1422-1428, 2016 | 345 | 2016 |
A recurrent neural network based microscopic car following model to predict traffic oscillation M Zhou, X Qu, X Li Transportation research part C: emerging technologies 84, 245-264, 2017 | 322 | 2017 |
Development of an Efficient Driving Strategy for Connected and Automated Vehicles at Signalized Intersections: A Reinforcement Learning Approach M Zhou, Y Yu, X Qu IEEE Transactions on Intelligent Transportation Systems 21 (1), 433-443, 2020 | 281 | 2020 |
Jointly Dampening Traffic Oscillations and Improving Energy Consumption with Electric, Connected and Automated Vehicles: A Reinforcement Learning Based Approach X Qu, Y Yu, M Zhou, CT Lin, X Wang Applied Energy 257, 114030, 2020 | 271 | 2020 |
Improving efficiency at highway T-junctions with connected and automated vehicles M Zhou, X Qu, W Qi Transportmetrica A: transport science 17 (1), 107-123, 2021 | 11 | 2021 |
Microscopic car-following model for autonomous vehicles using reinforcement learning M Zhou, X Qu Symposium on Innovations in Traffic Flow Theory and Characteristics and TFT …, 2016 | 7 | 2016 |
Modeling the Impact of Connected and Automated Vehicles on Highway Operations M Zhou Griffith University, 2018 | 2 | 2018 |
面向应用的深度神经网络图说 孙澄宇, 周沫凡, 胡苇 时代建筑, 50-55, 2018 | | 2018 |