Transportation Seminar Series: Dr. Lili Du, Associate Professor, University of Florida

This study seeks to develop a cooperative platoon control for a mixed traffic flow consisting of human-drive vehicles (HDVs) sandwiched by connected and autonomous vehicles (CAVs), aiming to ensure entire platoon smoothness and stability as well as individual vehicles’ mobility and safety. Specifically, our study contributes to the following integrated approaches. First, using real-time trajectory data, this study employs online curve matching algorithm to learn the aggregated response delay of the HDVs, whose car-following behavior is captured by the well accepted Newell car-following models. Built upon that, we develop constrained One- or P-step MPC models to cooperatively control the movement of the CAV platoon upstream or downstream of the HDV platoon so that we can ensure both transient traffic smoothness and asymptotic stability of this mixed flow platoon. Considering the lack of the centralized computation facilities and the platoon subject to frequent topology variation, this study develops a novel distributed algorithm to solve the MPCs, leveraging the communication and computation technologies equipped on CAVs. The development of the distributed algorithm takes advantage of the properties of the optimizers in the MPCs, such as solution uniqueness, sequentially feasibility, and nonempty interior point of the solution space. The convergence of the distributed algorithm as well as the stability of the MPC control are proved by both theoretical analysis and the experimental study. Extensive numerical experiments based on the field data indicate that the distributed algorithm works efficiently. The cooperative MPC can dampen traffic oscillation and stabilize the traffic flow more efficiently. It outperforms the other three control strategies, including the non-cooperative control strategy and a latest CACC strategy in literature.

 
Dr. Du is an associate professor in the Department of Civil and Coastal Engineering, University of Florida. Before joining UF, she worked as an assistant and then an associate professor at Illinois Institute of Technology from 2012-2017. She also worked as a Post-doctoral Research Associate for NEXTRANS, the USDOT Region V Regional University Transportation Center at Purdue University from 2008 to 2012. Dr. Du received her Ph.D. degree in Decision Sciences and Engineering Systems with a minor in Operations Research and Statistics from Rensselaer Polytechnic Institute in 2008. Dr. Du received her MS degree in Industrial Engineering from Tsinghua University in 2003 and her BS degree in Mechanical Engineering from Xi’an Jiaotong University in 1998. Du’s research is characterized by applying operations research, network modeling, and statistical methods into transportation system analysis and network modeling. Her current research covers several interdisciplinary research areas in Transportation Engineering, such as Connected and Autonomous Vehicle Systems, Interdependent Infrastructure Network Modeling, Sustainable Multimodal Transportation Systems, Optimization, and Data Fusion Applications in Traffic Flow Analysis. Dr. Du’s studies have been published in several major transportation journals, including Transportation Research Part B, Part C, IEEE Transactions on ITS, Networks and Spatial Economics, etc. Her research has been well funded by National Science Foundation, Illinois Department of Transpiration, and University Transportation Research Center. Dr. Du is a recipient of the NSF CAREER award in 2016. Her recent project “Driverless City” won the First Nayar Prize at IIT in 2015. Dr. Du currently serves on the editorial advisory board of Transportation Research Part B. She is a member of INFORMS and the Transportation Research Board Committee on Transportation Network Modeling (ADB30), for which she also serves on the committee’s editorial board, and the chair of the subcommittee on Emerging Technologies in Network Modeling (ADB30(5)).