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Modeling Methodology for Cooperative Adaptive Traffic Control Using Connected Vehicle Data

Kashyap, Gaurav
http://orcid.org/0000-0002-2659-3823
http://rave.ohiolink.edu/etdc/view?acc_num=ucin15921353756158

Abstract Details

2020, MS, University of Cincinnati, Engineering and Applied Science: Civil Engineering.
With the increasing demand for real-time control logic, infrastructure-enabled vehicle detector data is being considered for state of art traffic signal control strategies. The conventional detection methods are usually point detection that cannot directly measure vehicle speed and location. This has been the biggest challenge to design a robust traffic control system. Connected Vehicles (CVs) due to the advancements in wireless communication technology are a potential solution to overcome this challenge. The emerging CV technology provides an opportunity to formulate an ambulant data platform that allows the actual data transfer among multiple vehicles as well as with the infrastructure. More significantly, the CV’s capability of serving as the mobile trajectory sensors could help us to reduce the dependencies on conventional infrastructure-based vehicle detectors. The connected vehicles can provide increased opportunities and enforce more challenges for the signal control of urban traffic. These include vehicle to infrastructure (V2I), vehicle to vehicle (V2V), and vehicle to something else’s(V2X). The core objective of this study is to create a framework in which algorithms, modeling methods, and testing schemes for the optimization of urban traffic signal under mixed traffic conditions are included (coexistence of conventional vehicles and CVs). For isolated intersections or multiple intersections along a corridor, this framework can improve traffic signal timing. Precisely, the major assignments of this research include: 1.Thorough testing in traffic simulation to reinforce the proposed methods. This research evaluated the CCACSTO algorithm at four different penetration rates of CAVs for three different traffic conditions (light traffic, mild traffic, and heavy traffic). The simulation test results show that average vehicle delay and queue length with CCACSTO algorithm reduced by 46.04% and 56.15% respectively under 50% penetration rate of CAVs.
Heng Wei, Ph.D. (Committee Chair)
Jiaqi Ma, Ph.D. (Committee Member)
Nick Yeretzian, MS Civil Engineering (Committee Member)
100 p.
Transportation
Cooperative Adaptive Traffic Control; Connected Vehicle; Connected Automated Vehicle; Signal Optimization; Wei-Zone; CCACSTO

Recommended Citations

Citations

  • Kashyap, G. (2020). Modeling Methodology for Cooperative Adaptive Traffic Control Using Connected Vehicle Data [Master's thesis, University of Cincinnati]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=ucin15921353756158

    APA Style (7th edition)

  • Kashyap, Gaurav. Modeling Methodology for Cooperative Adaptive Traffic Control Using Connected Vehicle Data. 2020. University of Cincinnati, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=ucin15921353756158.

    MLA Style (8th edition)

  • Kashyap, Gaurav. "Modeling Methodology for Cooperative Adaptive Traffic Control Using Connected Vehicle Data." Master's thesis, University of Cincinnati, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=ucin15921353756158

    Chicago Manual of Style (17th edition)

ucin15921353756158
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© 2020, all rights reserved.
This open access ETD is published by University of Cincinnati and OhioLINK.