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You searched for id:"oai:tigerprints.clemson.edu:all_dissertations-3479". One record found.

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Clemson University

1. Khan, Sakib Mahmud. Connected and Automated Vehicles in Urban Transportation Cyber-Physical Systems.

Degree: PhD, Civil Engineering, 2019, Clemson University

Understanding the components of Transportation Cyber-Physical Systems (TCPS), and inter-relation and interactions among these components are key factors to leverage the full potentials of Connected and Automated Vehicles (CAVs). In a connected environment, CAVs can communicate with other components of TCPS, which include other CAVs, other connected road users, and digital infrastructure. Deploying supporting infrastructure for TCPS, and developing and testing CAV-specific applications in a TCPS environment are mandatory to achieve the CAV potentials. This dissertation specifically focuses on the study of current TCPS infrastructure (Part 1), and the development and verification of CAV applications for an urban TCPS environment (Part 2). Among the TCPS components, digital infrastructure bears sheer importance as without connected infrastructure, the Vehicle-to-Infrastructure (V2I) applications cannot be implemented. While focusing on the V2I applications in Part 1, this dissertation evaluates the current digital roadway infrastructure status. The dissertation presents a set of recommendations, based on a review of current practices and future needs. In Part 2, To synergize the digital infrastructure deployment with CAV deployments, two V2I applications are developed for CAVs for an urban TCPS environment. At first, a real-time adaptive traffic signal control algorithm is developed, which utilizes CAV data to compute the signal timing parameters for an urban arterial in the near-congested traffic condition. The analysis reveals that the CAV-based adaptive signal control provides operational benefits to both CVs and non-CVs with limited data from 5% CVs, with 5.6% average speed increase, and 66.7% and 32.4% average maximum queue length and stopped delay reduction, respectively, on a corridor compared to the actuated coordinated scenario. The second application includes the development of a situation-aware left-turning CAV controller module, which optimizes CAV speed based on the follower driver's aggressiveness. Existing autonomous vehicle controllers do not consider the surrounding driver's behavior, which may lead to road rage, and rear-end crashes. The analysis shows that the average travel time reduction for the scenarios with 600, 800 and 1000 veh/hr/lane opposite traffic stream are 61%, 23%, and 41%, respectively, for the follower vehicles, if the follower driver's behavior is considered by CAVs. Advisors/Committee Members: Mashrur Chowdhury, Committee Chair, Amy Apon, Feng Luo, Weichiang Pang.

Subjects/Keywords: Adaptive traffic signal; Autonomous car; Connected car; Cyber physical systems; Digital infrastructure; Situation-awareness

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Khan, S. M. (2019). Connected and Automated Vehicles in Urban Transportation Cyber-Physical Systems. (Doctoral Dissertation). Clemson University. Retrieved from https://tigerprints.clemson.edu/all_dissertations/2475

Chicago Manual of Style (16th Edition):

Khan, Sakib Mahmud. “Connected and Automated Vehicles in Urban Transportation Cyber-Physical Systems.” 2019. Doctoral Dissertation, Clemson University. Accessed September 21, 2019. https://tigerprints.clemson.edu/all_dissertations/2475.

MLA Handbook (7th Edition):

Khan, Sakib Mahmud. “Connected and Automated Vehicles in Urban Transportation Cyber-Physical Systems.” 2019. Web. 21 Sep 2019.

Vancouver:

Khan SM. Connected and Automated Vehicles in Urban Transportation Cyber-Physical Systems. [Internet] [Doctoral dissertation]. Clemson University; 2019. [cited 2019 Sep 21]. Available from: https://tigerprints.clemson.edu/all_dissertations/2475.

Council of Science Editors:

Khan SM. Connected and Automated Vehicles in Urban Transportation Cyber-Physical Systems. [Doctoral Dissertation]. Clemson University; 2019. Available from: https://tigerprints.clemson.edu/all_dissertations/2475

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