Human-like autonomous car-following model with deep reinforcement learning M Zhu, X Wang, Y Wang Transportation research part C: emerging technologies 97, 348-368, 2018 | 456 | 2018 |
Safe, efficient, and comfortable velocity control based on reinforcement learning for autonomous driving M Zhu, Y Wang, Z Pu, J Hu, X Wang, R Ke Transportation Research Part C: Emerging Technologies 117, 102662, 2020 | 311 | 2020 |
Modeling car-following behavior on urban expressways in Shanghai: A naturalistic driving study M Zhu, X Wang, A Tarko Transportation research part C: emerging technologies 93, 425-445, 2018 | 233 | 2018 |
Drivers’ rear end collision avoidance behaviors under different levels of situational urgency X Wang, M Zhu, M Chen, P Tremont Transportation research part C: emerging technologies 71, 419-433, 2016 | 106 | 2016 |
Development of a kinematic-based forward collision warning algorithm using an advanced driving simulator X Wang, M Chen, M Zhu, P Tremont IEEE Transactions on Intelligent Transportation Systems 17 (9), 2583-2591, 2016 | 78 | 2016 |
Impact on car following behavior of a forward collision warning system with headway monitoring M Zhu, X Wang, J Hu Transportation research part C: emerging technologies 111, 226-244, 2020 | 67 | 2020 |
Optimizing signal timing control for large urban traffic networks using an adaptive linear quadratic regulator control strategy H Wang, M Zhu, W Hong, C Wang, G Tao, Y Wang IEEE Transactions on Intelligent Transportation Systems 23 (1), 333-343, 2020 | 51 | 2020 |
Monitoring public transit ridership flow by passively sensing Wi-Fi and Bluetooth mobile devices Z Pu, M Zhu, W Li, Z Cui, X Guo, Y Wang IEEE Internet of Things Journal 8 (1), 474-486, 2020 | 45 | 2020 |
A hierarchical framework for improving ride comfort of autonomous vehicles via deep reinforcement learning with external knowledge Y Du, J Chen, C Zhao, F Liao, M Zhu Computer‐Aided Civil and Infrastructure Engineering 38 (8), 1059-1078, 2023 | 38 | 2023 |
How fast you will drive? predicting speed of customized paths by deep neural network H Yang, C Liu, M Zhu, X Ban, Y Wang IEEE transactions on intelligent transportation systems 23 (3), 2045-2055, 2021 | 34 | 2021 |
Traffic performance score for measuring the impact of COVID-19 on urban mobility Z Cui, M Zhu, S Wang, P Wang, Y Zhou, Q Cao, C Kopca, Y Wang arXiv preprint arXiv:2007.00648, 2020 | 32 | 2020 |
Modeling car-following behavior on freeways considering driving style P Sun, X Wang, M Zhu Journal of transportation engineering, Part A: Systems 147 (12), 04021083, 2021 | 28 | 2021 |
Traffic-informed multi-camera sensing (TIMS) system based on vehicle re-identification H Yang, J Cai, M Zhu, C Liu, Y Wang IEEE Transactions on Intelligent Transportation Systems 23 (10), 17189-17200, 2022 | 17 | 2022 |
Calibrating and Validating Car-following Models on Urban Expressways for Chinese Drivers Using Naturalistic Driving Data X Wang, M Zhu China Journal of Highway and Transport 31 (9), 129-138, 2017 | 14 | 2017 |
Real-time crash identification using connected electric vehicle operation data M Zhu, HF Yang, C Liu, Z Pu, Y Wang Accident Analysis & Prevention 173, 106708, 2022 | 13 | 2022 |
Transfollower: Long-sequence car-following trajectory prediction through transformer M Zhu, SS Du, X Wang, Z Pu, Y Wang arXiv preprint arXiv:2202.03183, 2022 | 13 | 2022 |
Follownet: A comprehensive benchmark for car-following behavior modeling X Chen, M Zhu, K Chen, P Wang, H Lu, H Zhong, X Han, X Wang, Y Wang Scientific data 10 (1), 828, 2023 | 12 | 2023 |
基于自然驾驶数据的避撞预警对跟车行为影响 王雪松, 朱美新, 邢祎伦 同济大学学报: 自然科学版 44 (7), 1045-1051, 2016 | 10 | 2016 |
Edge computing for real-time near-crash detection for smart transportation applications R Ke, Z Cui, Y Chen, M Zhu, H Yang, Y Wang arXiv preprint arXiv:2008.00549, 2020 | 9 | 2020 |
Impacts of Collision Warning System on Car following Behavior Based on Naturalistic Driving Data 王雪松, 朱美新, 邢祎伦 同济大学学报 (自然科学版)(英文版) 44 (7), 1045-1051, 2016 | 9 | 2016 |