Meixin Zhu
Cited by
Cited by
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
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
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
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
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
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
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
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
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
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
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
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
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, 2022
王雪松, 朱美新, 陈铭
同济大学学报: 自然科学版 44 (6), 876-883, 2016
Car-following headways in different driving situations: A naturalistic driving study
M Zhu, X Wang, X Wang
CICTP 2016, 1419-1428, 2016
Impacts of collision warning system on car following behavior based on naturalistic driving data
X Wang, M Zhu, Y Xing
Journal of Tongji University (Natural Science) 44 (7), 1045-1051, 2016
Globalized modeling and signal timing control for large-scale networked intersections
H Wang, CR Wang, M Zhu, W Hong
Proceedings of the 2nd ACM/EIGSCC Symposium on Smart Cities and Communities, 1-7, 2019
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
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
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
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