Connected Autonomous Vehicle Control
Optimization at Intersections
Guohui Zhang
Abstract Connected and Autonomous Vehicle (CAV)-enabled traffic system has
demonstrated great potential to mitigate congestion, reduce travel delay, and enhance
safety performance. According to the U.S. Based on seamless Vehicle-To-Vehicle
(V2V) and Vehicle-To-Infrastructure communication as well as autonomous driving
technologies, traffic management and control will be revolutionized. The existing
studies indicate that traffic lights will be eliminated and 75 % of vehicles will be
autonomous vehicles by 2040. National Highway Traffic Safety Administration
(NHTSA) plans to mandate inter-vehicle communication technologies on every single vehicle by 2016. However, one should note that the current research regarding
CAV system management and control is still in its early stage. The presented study
concentrates on the VISSIM-based simulation platform development to enable an
innovative autonomous intersection control mechanism and optimize CAV operations at intersections without signal lights. Simulation-based investigation on traffic
system operations provides a cost-effective, risk-free means of exploring optimal
management strategies, identifying potential problems, and evaluating various alternatives. In the study, a VISSIM-based simulation platform is developed for simulating individual-CAV-conflict-based traffic control optimization at intersections.
A novel external module will be developed via VISSIM Component Object Model
(COM) interfaces. A new CAV-based control algorithm entitled a Discrete ForwardRolling Optimal Control (DFROC) model, is developed and implemented through the
VISSIM COM server. This external module can provide sufficient flexibility to satisfy
any specific demands from particular researchers and practitioners for CAV control
operations. Research efforts will be made to calibrate driving behavior parameters
in the simulation model using drivers’ characteristic data to further strengthen the
simulation creditability. Furthermore, a method for statistically analyzing simulation
outputs and examining simulation reliability is developed. The methodology developed is applicable for quantitatively evaluating the impacts of various CAV control
strategies on urban arterials.
G. Zhang (B)
University of New Mexico, Albuquerque, NM 87131, USA
e-mail: [email protected]
© Springer International Publishing Switzerland 2017
V.E. Balas et al. (eds.), Information Technology and Intelligent
Transportation Systems, Advances in Intelligent Systems and Computing 454,
DOI 10.1007/978-3-319-38789-5_2
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G. Zhang
Guohui Zhang
1 Short Biography
Dr. Guohui Zhang is an Assistant Professor in the Department of Civil Engineering
at the University of New Mexico (UNM). Dr. Zhang received his Ph.D. from the
University of Washington in 2008. Dr. Zhang’s research focuses on large-scale transportation systems modeling, customized traffic simulation, travel delay estimation,
traffic safety and accident modeling, congestion pricing, traffic detection and sensor
data analysis, and sustainable transportation infrastructure design and maintenance.
Dr. Zhang has published nearly 50 peer-reviewed journal articles, conference papers,
and technical reports and presented his research contributions numerous times at
prestigious international and national conferences.
http://www.springer.com/978-3-319-38787-1