主讲人：Dr. Wai Wong

邀请人：苏子诚 研究员

时间：2025年12月26日下午15：30-16：30（Beijing Time）

地点：通达馆102会议室

主讲人简介：

Dr. Wai Wong is a senior lecturer in the Department of Civil and Environmental Engineering at the University of Canterbury, New Zealand. He holds a Ph.D. in Transportation and Traffic Engineering (supervised by Professor S.C. Wong) and a Bachelor’s degree in Civil Engineering with first-class honours, both from the Department of Civil Engineering at The University of Hong Kong. After completing his doctorate, Dr. Wong served as a postdoctoral research fellow (supervised by Professor Henry X. Liu) in the Department of Civil and Environmental Engineering at the University of Michigan, USA. His research focuses on big data analytics, traffic flow theory, connected and autonomous vehicles, traffic control and signal optimisation, evacuation, and intelligent transportation systems. His work has been published in top-tier international journals, including Transportation Science, Transportation Research Part B, Transportation Research Part C and IEEE Transactions on Intelligent Transportation Systems, and presented at prestigious international conferences, such as ISTTT, TRB and IEEE ITSC. His research has been supported by multiple funding agencies, including New Zealand’s Ministry of Business, Innovation and Employment (MBIE), Royal Society of New Zealand and the University Grants Committee (UGC) of Hong Kong. Dr. Wong also serves as a guest editor for Transportation Research Part A.

主讲内容简介：

The advent of big data and the Internet of Things has created unprecedented opportunities to enhance the efficiency and safety of future transportation systems. Connected vehicles (CVs), functioning as mobile sensors or probe vehicles, are expected to play a central role in this connected ecosystem. However, during the extended transition toward full CV deployment, the CV penetration rate remains a critical input for reconstructing complete traffic information from partial observations and supporting a wide range of intelligent transportation system (ITS) applications. This seminar introduces two innovative analytical methods: the Single-Source Data Penetration Rate Estimation (SSDPRE) method and the Probabilistic Penetration Rate (PPR) model. The SSDPRE method estimates the average CV penetration rate, while the PPR model quantifies its uncertainty. Both methods are fully analytical, yield unbiased estimates, and rely solely on CV data, effectively transforming CVs into pseudo-detectors and extending their applicability across transportation networks. To demonstrate their practical utility, a CV-based stochastic adaptive signal control framework applicable to detector-free junctions across the full range of CV penetration rates is presented. In this framework, the estimated average CV penetration rate is used to infer expected vehicle arrival rates, and the associated uncertainty is translated into uncertain arrival rates. The proposed control strategy minimises the expected total junction delay by accounting for these uncertainties on a cycle-by-cycle basis, illustrating the potential for efficient and reliable traffic control even at detector-free intersections under real-world conditions.

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