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University of South Florida

Theses/Dissertations

Mixed Traffic

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Full-Text Articles in Urban Studies and Planning

Trajectory Based Traffic Analysis And Control Utilizing Connected Autonomous Vehicles, Yu Wang Nov 2019

Trajectory Based Traffic Analysis And Control Utilizing Connected Autonomous Vehicles, Yu Wang

USF Tampa Graduate Theses and Dissertations

Recent scholars have developed a number of stochastic car-following models that have successfully captured driver behavior uncertainties and reproduced stochastic traffic oscillation propagation. While elegant frequency domain analytical methods are available for stability analysis of classic deterministic linear car-following models, there lacks an analytical method for quantifying the stability performance of their peer stochastic models and theoretically proving oscillation features observed in the real world. To fill this methodological gap, this study proposes a novel analytical method that measures traffic oscillation magnitudes and reveals oscillation characteristics of stochastic linear car-following models. We investigate a general class of stochastic linear car-following …


Connected Autonomous Vehicles: Capacity Analysis, Trajectory Optimization, And Speed Harmonization, Amir Ghiasi Jul 2018

Connected Autonomous Vehicles: Capacity Analysis, Trajectory Optimization, And Speed Harmonization, Amir Ghiasi

USF Tampa Graduate Theses and Dissertations

Emerging connected and autonomous vehicle technologies (CAV) provide an opportunity to improve highway capacity and reduce adverse impacts of stop-and-go traffic. To realize the potential benefits of CAV technologies, this study provides insightful methodological and managerial tools in microscopic and macroscopic traffic scales. In the macroscopic scale, this dissertation proposes an analytical method to formulate highway capacity for a mixed traffic environment where a portion of vehicles are CAVs and the remaining are human-driven vehicles (HVs). The proposed analytical mixed traffic highway capacity model is based on a Markov chain representation of spatial distribution of heterogeneous and stochastic headways. This …