Date on Master's Thesis/Doctoral Dissertation

5-2024

Document Type

Doctoral Dissertation

Degree Name

Ph. D.

Department

Civil and Environmental Engineering

Degree Program

Civil Engineering, PhD

Committee Chair

Kluger, Robert

Committee Member

Sun, Zhihui

Committee Member

Li, Richard

Committee Member

Bai, Lihui

Author's Keywords

Emergency vehicle preemption; connected and autonomous vehicles; cooperative behavior framework; vehicle trajectory optimization; microscopic traffic simulation; queue estimation model

Abstract

Reducing the response time of emergency vehicles (EV) is highly important due to the relationship between response time and fatality rate. Emerging technologies can help reduce response time by enhancing EV prioritization. On the other hand, EV prioritization can negatively impact general traffic especially in cross streets of signalized intersections. In this research, three main objectives were followed. Firstly, creation and dissipation of EVP disruption on arterial cross streets was studied to recommend solutions to prevent this disruption. Secondly, lane-changing and stopping behavior of CAVs were studied in EV prioritization to identify the best lane-changing behavior through minimizing delay. Thirdly, a cooperative behavior framework was developed for CAVs to optimize their trajectories when prioritizing EV to minimize lane-changing conflict. The results shows that shockwave theory can quantify the disruption created by EVP and investigating solutions to counteract the disruption. Using the developed queue estimation model, the strategies to prevent the disruption can be designed, and if the disruption is not preventable, the solutions to aid the approaches recover quickly after EVP can be investigated. The results of cooperative behavior framework indicate that if EVs move on the rightmost road lane, adjacent to shoulder, the cooperative algorithm can clear EV’s lane and have minimal adverse impact on CAV delay as it can wisely open shoulder to CAVs when required.

Available for download on Sunday, November 10, 2024

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