Thursday, August 16, 2018, 12:00PM (ET)

Operations and Planning for Connected Autonomous Vehicles: From Trajectory Control to Capacity Analysis

Increasing congestion, excessive fuel consumption and emissions, and unacceptable safety risks remain the major challenges to next-generation highway systems that are meant to be smart and sustainable. Advanced connected and automated vehicle (CAV) technologies offer unprecedented opportunities to smooth highway traffic and increase highway capacity. These technologies render the high possibility of precise control of intelligent vehicle trajectories in addition to passive accommodation of defective human drivers. We propose a shooting heuristic (SH) approach that constructs smooth trajectories at the microscopic scale for a large number of interactive vehicles under realistic constraints (e.g., vehicle kinematic limits, traffic arrival conditions, car-following safety, and signal timing). SH has a very parsimonious structure (e.g., only four acceleration variables) and a very small computational complexity suitable for real-time applications. This SH technique has been applied to operations of both pure CAV traffic and mixed traffic in both freeway bottleneck and signalized arterial operations. Trajectory control is further integrated with signal optimization to further enhance the system performance. Numerical results show these trajectory control strategies can significantly reduce fuel consumption, travel delay and safety risks.

Microscopic management will eventually impact macroscopic highway throughput. To facilitate infrastructure planning decisions, we propose an analytical capacity model for highway mixed traffic considering heterogeneous and stochastic headways in mixed traffic. This model captures not only the full spectrum of CAV market penetration rates but also all possible values of CAV platooning intensities that largely affect the spatial distribution of different headway types. Numerical experiments verify that this analytical model accurately quantifies the corresponding mixed traffic capacity at various settings. This analytical model allows for examination of the impact of different CAV technology scenarios on mixed traffic capacity. This analytical framework further enables us to build a compact lane management model to efficiently determine the optimal number of dedicated CAV lanes to maximize mixed traffic throughput of a multi-lane highway segment. This optimization model addresses varying demand levels, market penetration rates, platooning intensities and technology scenarios. Numerical analyses illustrate the application of this lane management model and draw insights into how the key parameters affect the optimal CAV lane solution and the corresponding optimal capacity. This model can serve as a useful and simple decision tool for near future CAV lane management and related planning decisions. Download Handout

Presenter: Xiaopeng Li, Ph.D., Assistant Professor, Department of Civil and Environmental Engineering, University of South Florida

Dr. Xiaopeng Li is currently an assistant professor and the Susan A. Bracken Faculty Fellow in the Department of Civil and Environmental Engineering at the University of South Florida (USF). His major research interests include connected vehicles, autonomous vehicles, sensor networks and resilient interdependent infrastructure systems, with a focus of understanding and mitigating oscillations and disruptions rising in these systems across various temporal and spatial scales. Prior to joining USF, he worked at Mississippi State University as an assistant professor of transportation engineering. He is a recipient of a National Science Foundation (NSF) CAREER award. He has published 38 peer-reviewed journal papers. He has been the PI or a co-PI for multiple federal and state research projects, including four sponsored by NSF. He has served as members on the Transportation Network Modeling Committee (ADB30) and the Traffic Flow Theory and Characteristics (AHB45) of the Transportation Research Board (TRB). He has served as a departmental associate editor for Institute of Industrial and Systems Engineers Transactions and guest editors for Journal of Advanced Transportation and IEEE ITS Magazine.


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