Hong Zhu
Orcid: 0000-0003-0358-6724Affiliations:
- Tongji University, College of Transportation Engineering, Key Laboratory of Road and Traffic Engineering of the Ministry of Education, Shanghai, China
According to our database1,
Hong Zhu authored at least 12 papers
between 2024 and 2026.
Collaborative distances:
Collaborative distances:
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Bibliography
2026
Leveraging Trajectory Continuity to Enhance Real-Time Traffsic Signal Control With Limited Connected Vehicle Provision.
IEEE Intell. Transp. Syst. Mag., 2026
2025
Connected Vehicle Data-Driven Robust Optimization for Traffic Signal Timing: Modeling Traffic Flow Variability and Errors.
IEEE Trans. Intell. Transp. Syst., December, 2025
Robust Estimation of Traffic Arrival Rates at Signalized Intersections With Sparse Internet of Vehicles.
IEEE Internet Things J., October, 2025
Predictive and Multigranularity Resilience Assessment of Urban Transportation Based on Neural Controlled Differential Equation.
IEEE Trans. Reliab., September, 2025
Sharing Control Knowledge Among Heterogeneous Intersections: A Distributed Arterial Traffic Signal Coordination Method Using Multi-Agent Reinforcement Learning.
IEEE Trans. Intell. Transp. Syst., February, 2025
An adversarial diverse deep ensemble approach for surrogate-based traffic signal optimization.
Comput. Aided Civ. Infrastructure Eng., February, 2025
Quantifying Autonomy Levels of Traffic Signal Control Within Autonomous Traffic Systems Based on AHP-TOPSIS.
Syst., 2025
A distributed model predictive approach for network traffic signal control using multi-objective dynamic programming.
Comput. Aided Civ. Infrastructure Eng., 2025
Proceedings of the 28th IEEE International Conference on Intelligent Transportation Systems, 2025
2024
IEEE Trans. Intell. Transp. Syst., October, 2024
Integrating Multi-Graph Convolutional Networks and Temporal-Aware Multi-Head Attention for Lane-Level Traffic Flow Prediction in Urban Networks.
Proceedings of the 27th IEEE International Conference on Intelligent Transportation Systems, 2024
Intersection Dynamics-Aware Continuous Learning in Adaptive Traffic Signal Control Featuring Fast Startup and Adaptation.
Proceedings of the 27th IEEE International Conference on Intelligent Transportation Systems, 2024