[Time] 10:00-11:00, March 22(Sunday), 2020
 
[Location] Online
Lotus Pond Rain Classroom: W141WB
ZOOM ID: 572 208 225
Tecent ID：220 744 898
 
[Speaker]  Dr. Ziqi Song

[Host] Dr. Fang He
 
[Title] Strategic Planning of Dedicated Autonomous Vehicle Lanes and Autonomous Vehicle/Toll Lanes in Transportation Networks

Note: We will register the seminar for the attendees by the record in Lotus Pond Rain Classroom.

[Abstract] Employing vehicle communication and automated control technologies, autonomous vehicles (AVs) can safely drive closer together than human-driven vehicles (HVs), thereby potentially improving traffic efficiency. Separation between AV and HV traffic through the deployment of dedicated AV lanes is foreseen as an effective method of amplifying the benefits of AVs and promoting their adoption. However, it is important to consider mixed AV and HV traffic in a transportation network. On the one hand, it may be impractical to deploy dedicated AV lanes throughout the network, while on the other hand, dedicated AV lanes may even reduce the total traffic efficiency of a road segment when the AV flow rate is low. In this study, we considered a new form of managed lanes for AVs, designated as autonomous vehicle/toll (AVT) lanes, which grant free access to AVs while allowing HVs to access the lanes by paying a toll. We investigated the optimal deployment of dedicated AV lanes and AVT lanes in transportation networks with mixed AV and HV flows. The user equilibrium (UE) problem in a transportation network with mixed flows of AVs and HVs is first explored. We formulated the UE problem as a link-based variational inequality (VI) and identified that, with different impacts of AVs on road capacity, the UE problem can have unique or non-unique flow patterns. Considering that the UE problem may have non-unique flow distributions, we proposed a robust optimal deployment model, which is a generalized semi-infinite min-max program, to deploy the dedicated AV lanes and AVT lanes so that the system performance under the worst-case flow distributions is optimized. We proposed effective solution algorithms to solve these models and presented numerical studies to demonstrate the models and the solution algorithms. The results show that the system performance can be significantly improved through the deployment of dedicated AV lanes and AVT lanes.


[Bio] Dr. Ziqi Song is an Assistant Professor in the Department of Civil and Environmental Engineering (CEE) at Utah State University (USU). He is also the Transportation Division Head of the CEE Department and a member of the engineering college research council at USU. Further, he serves as the faculty advisor of USU Institute of Transportation Engineers (ITE) student chapter. He is a committee member of the Transportation Research Board (TRB) Transportation of Hazardous Materials (AT040) and a subcommittee member of the Emerging Technologies in Network Modeling Subcommittee (ADB30-5). His research interests include transportation network modeling, transportation electrification, intelligence transportation systems, and traffic operations. Dr. Song received his M.S. in Operations Research and a Ph.D. in Civil and Coastal Engineering from the University of Florida. He also graduated with an M.Phil. in Civil Engineering from the University of Hong Kong and a B.E. in Transportation Engineering from Southeast University, China. He worked as a research fellow at Technical University of Munich, Germany, and later a postdoctoral researcher at the University of Florida.


All interested are welcome!