CS Events

Qualifying Exam Cyber-Physical Systems for Urban Traffic Management
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Speaker: Qinchen Yang

Location : CoRE 305

Committee: 

Associate Professor Desheng Zhang

Associate Professor Hao Wang

Assistant Professor Dong Deng

Assistant Professor Kangning Wang

Event Type: Qualifying Exam

Abstract: My research focuses on the Cyber-Physical System(CPS) for urban traffic management, especially when abnormal situations happen. CPS is a new information paradigm that connects the physical world and the cyber world. In the physical world, we have different systems, such as agriculture systems, smart home systems, and transportation systems. Given these bodily systems, we use various sensing technologies to collect large-scale data, which will be uploaded to the cyber world for analysis, modeling, prediction, and scheduling. CPS is prone to anomalies because of the unstable interaction between the physical world and the cyber world, such as sensing noise, sensing failure, and controller failure. I'm interested in the resilient Cyber-Physical System, where we solve anomalies in the cyber world and minimize their impact on the physical world. I have developed a resilient system that solves traffic signal malfunctions, a common real-world occurrence with significant repercussions. The primary objective of this research is to mitigate the adverse effects of traffic signal malfunction, such as traffic congestion and collision, by optimizing the control of neighboring functioning signals. Additionally, I designed a Large Language Model(LLM) based framework for correcting abnormal addresses, whose occurrence leads to a significant impact on modern navigation systems. The research idea is to rewrite abnormal addresses using the strong reasoning ability of LLM and further boost LLM’s performance through objective alignment and address-centric retrieval augmented generation.Papers:MalLight: Influence-Aware Coordinated Traffic Signal Control for Traffic Signal Malfunctions, Accepted CIKM 2024AddrLLM: Address Rewriting via Large Language Model on Nationwide Logistics Data, Accpeted KDD 2024

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Contact  Associate Professor Desheng Zhang

Link: https://rutgers.zoom.us/j/5034279181?pwd=U2FFOGIwVkJJV1dHbzYxK2VTVDBiZz09