Jingbo Zhan
Papers
1
Total Citations
2
H-Index
1
About
Jingbo Zhan is a rising researcher in intelligent transportation systems, with a focus on leveraging artificial intelligence to enhance urban traffic safety. Their work centers on developing advanced computational methods for real-time crash risk inference, combining state-of-the-art deep learning architectures with optimization algorithms. Zhan’s most-cited paper, "Online Traffic Crash Risk Inference Method Using Detection Transformer and Support Vector Machine Optimized by Biomimetic Algorithm" (2024), introduces a novel framework that integrates Detection Transformers with biomimetic optimization—such as swarm intelligence—to improve the accuracy and timeliness of crash risk estimation. This approach addresses the persistent challenge of predicting life-threatening and economically costly traffic incidents, offering a scalable solution for smart city applications. With 2 citations in its first year, the work signals growing interest in Zhan’s interdisciplinary methodology, which bridges computer vision, machine learning, and transportation engineering. Their contributions are particularly notable for advancing online inference capabilities, enabling proactive rather than reactive traffic safety measures. Zhan’s research holds promise for reducing urban crash risks through data-driven, real-time decision support systems.
Research Focus
Key Achievements
Top Papers
- 1