Connected Vehicle Infrastructure University Transportation Center
Current adaptive traffic control systems rely heavily on predictions to gain proper control strategies. The majority of such predictions are the anticipation of arrival patterns, turning designations and approaching demands at downstream intersections. However, it is generally understood that prediction inaccuracy is often cause for less desirable performance of the adaptive control. This is because the stochastic nature of vehicles’ movements would not be properly captured by the prediction models used in the adaptive control systems. Nevertheless, such predictions are utilized because existing sensing technologies, including inductive loops or video cameras, used in the current adaptive system are incapable of directly measuring individual vehicles’ driving information.
As connected vehicle (CV) technology is expected to provide individual vehicular information in real-time, it would be unnecessary to maintain the current prediction-based framework of the adaptive signal control systems. This research will conduct a field deployment study to demonstrate previously developed cumulative travel-time responsive (CTR) intersection control algorithm that relies on real-time vehicular information. The objectives of this research are 1) to improve the CTR algorithm under low CV market penetration by utilizing Bluetooth technology, and 2) to assess potential benefits of the algorithm by examining the mobility, energy and greenhouse emissions measures through field operational test at the Northern Virginia Connected Vehicle Test Bed. Before conducting the proposed field deployment study, the project team will develop and evaluate a hardware-in-the-loop simulation to ensure the algorithm developed will work with traffic controller in the Northern Virginia Connected Vehicle Test Bed.
This research project will assess the impacts of the CV technology enabled traffic signal control with the support of Bluetooth device that collects cumulative travel time data. As reported in an earlier simulation based study showing 34% total delay savings, significant benefits in travel time, fuel consumption and emissions is expected. This project will provide CV stakeholders, such as government agencies and/or private companies, with the information of the marginal market penetration rate of CVs ensuring the benefits. In addition, the hardware in the loop simulation setup can be used in the development and evaluation of future CV applications related to traffic signal operations including transit signal priority, preemption, advisory to red light running vehicles, etc.
Park, B.B., Choi, S., Lee, J. & Son, S. H. (2016, October). Assessment of Cumulative Travel-Time Responsive Traffic Signal Control Algorithm using Vissim Presented at the PTV Group Annual Traffic User Group Meeting, Arlington, VA
Choi, S., S. Hong, J. Lee and B. B. Park. Evaluation of Cumulative Travel Time Response Control Algorithm using Advanced Prediction Method. Presented at the US Korea Conference 2015, Atlanta, GA, July 28 – August 1, 2015.
Saerona Choi and Brian Park. Cumulative Travel-time Responsive Algorithm for Traffic Signal Control. International Cyber Physical System Workshop at DGIST, Daegu, Korea, June 2, 2015.
Research and Innovative Technology Administration
University Transportation Centers Program
Department of Transportation
1200 New Jersey Avenue, SE
Washington, DC 20590
USA
Harwood, Leslie
Phone: 540-231-9530
Email: lharwood@vtti.vt.edu
University of Virginia, Charlottesville
Center for Transportation Studies
P.O. Box 400742, Thornton Hall, D228
Charlottesville, VA 22903
USA
Park, Brian
Hong, Seongah
RiP URL
Project Poster
TriD Format