Title: Uncertainty Estimation of the Connected Vehicle Penetration Rate: Modeling Complex Residual-vehicle Effects
Speaker: Mr. Shaocheng Jia (Department of Civil Engineering)
Date: September 27, 2024 (Friday)
Time: 10:00 am - 11:00 am
Venue: Room 612B, 6/F, Haking Wong Building, The University of Hong Kong
About the talk: In the transition to full deployment of connected vehicles (CVs), the CV penetration rate plays a key role in bridging the gap between partial and complete traffic information. Several innovative methods have been proposed to estimate the CV penetration rate using only CV data. However, these methods, as point estimators, may lead to biased estimations or suboptimal solutions when applied directly in modeling or system optimization. To avoid these problems, the uncertainty and variability in the CV penetration rate must be considered. Recently, a probabilistic penetration rate (PPR) model was developed for estimating such uncertainties. The key model input is a constrained queue length distribution composed exclusively of queues formed by red signals in undersaturation conditions with no residual vehicles. However, in real-world scenarios, due to random arrivals, residual vehicles are commonly carried over from one cycle to another in temporary overflow cycles in undersaturation conditions, which seriously restricts the applicability of the PPR model. To address this limitation, this talk will introduce a Markov-constrained queue length (MCQL) model that can model the complex effects of residual vehicles on the CV penetration rate uncertainty. Comprehensive VISSIM simulations and applications to real-world datasets demonstrate that the proposed MCQL model can accurately model the residual-vehicle effect and estimate the uncertainty. Thus, the applicability of the PPR model is truly extended to real-world settings, regardless of the presence of residual vehicles. A simple stochastic CV-based adaptive signal control example illustrates the potential of the proposed model in real-world applications.
About the speaker: Shaocheng Jia is currently a Ph.D. candidate at The University of Hong Kong and a (student) member of IEEE, IEEE ITSS, INFORMS, INFORMS TSL, CHTS, IACIP, and ITS of HKU. He received his B.E. degree from the Department of Electronic Information Engineering, China University of Petroleum (Beijing), and M.E. degree from the Department of Automation, Tsinghua University, in 2014 and 2018, respectively. His research interests include intelligent perception and control, connected and automated transportation, stochastic modeling and optimization, and machine learning. Shaocheng has published over 10 papers in prestigious journals and conferences, including Transportation Science, IEEE TITS, TR-Part C, the International Symposium on Transportation and Traffic Theory (ISTTT), etc., as well as 6 national invention patents. Shaocheng is also a recipient of the Outstanding Master’s Dissertation Awards from CHTS and Tsinghua University. He has chaired a regular session in the 26th IEEE ITSC and a Young Scholar Tech Talk at HKU, co-organized two workshops in IEEE ITSC, and been a reviewer for IEEE TITS, IEEE TNNLS, IEEE TMC, TRR, IEEE ITSC, TRB, etc.
