Speaker：Shan Jiang，Lead Data Scientist, Johnson & Johnson
Time：9:00, May 23, 2024 (Beijing Time)
Location：https://vc.feishu.cn/j/367790682
Abstract：
Issues surrounding traffic safety and mobility have been a focal point of transportation research for decades, continuing to impact us all. Data-driven approaches are transforming our understanding of these issues, leading to effective solutions and paving the way for improved traffic safety and mobility. In this talk, three essential aspects of this domain will be explored. First, an innovative model 'Safe Route Mapping' will be introduced. This model combines crash estimates with conflict probabilities to evaluate real-time risk scores on roadways. Next, a data-driven optimization algorithm for the dynamic shortest path problem, which considers time-varying traffic safety and travel time, will be examined. Lastly, a revolutionary approach using distributed multi-agent reinforcement learning with graph decomposition for large-scale adaptive traffic signal control will be discussed. These solutions are not just theoretical but also practical tools for enhancing traffic safety and mobility.
Biography：
Shan Jiang received the Ph.D. from Industrial and Systems Engineering, Rutgers University—New Brunswick, New Jersey, USA. His research lies in modeling, optimization and operation management of complex systems, with a focus on intelligent transportation systems, healthcare systems, and production systems. He has published 18 papers, including 3 in IEEE T-ITS, 3 in IEEE T-ASE, and 1 on IFORMS Journal on Computing. He is currently a Lead Data Scientist with Johnson & Johnson Supply Chain in New Jersey, USA. In this role, he has played a pivotal part in deploying five funded projects aimed at enhancing and optimizing the company's digital supply chain.
