ORCID

https://orcid.org/0000-0002-6856-8503

Keywords

traffic safety, connected autonomous vehicles, intelligent transportation systems, work zones

Abstract

Work zones play a vital role in the maintenance and upgrading of roads and highways; however, they also pose substantial risks to both drivers and workers. Utilizing technology, such as Smart Work Zones (SWZs), Connected Autonomous Vehicles (CAVs), and data dissemination algorithms, is crucial in improving safety in work zones by providing decision-makers with timely and accurate information. Ensuring safety in these environments is essential for reducing accidents and fatalities and enhancing drivers' sense of security, and protecting the well-being of workers operating in hazardous conditions. In this dissertation, we proposed a methodology based on three complementary approaches to enhance work zone safety. First, the study develops a cost-effective, low-power communication network for SWZ barrel-mounted sensors, proposing a novel relay node selection algorithm utilizing Bluetooth Low Energy mesh technology to enhance network reliability in linear, semi-static configurations. Second, the impact of vehicle-to-vehicle communication on lane change safety is assessed through a calibrated co-simulation framework, modeling real-world connectivity issues such as packet loss and latency to analyze their effect on CAV behavior during merging maneuvers. Third, a Signal Temporal Logic-based optimization framework is introduced to dynamically enforce safety constraints and refine lane change parameters, addressing the limitations of traditional car-following models in complex traffic environments. Finally, the research integrates vehicle-to-infrastructure communication into a comprehensive SWZ framework, evaluating the combined effects of CAV market penetration rates, roadside unit placement, and communication ranges on crash risk reduction. Together, these contributions create an interconnected framework for improving traffic safety in dynamic and complex work zone environments. The integration of these methodologies not only bridges gaps between traffic behavior modeling and communication network analysis but also provides scalable, real-world solutions for enhancing safety and efficiency in smart transportation systems.

Completion Date

2025

Semester

Spring

Committee Chair

Abdel-aty, Mohamed

Degree

Doctor of Philosophy (Ph.D.)

College

College of Engineering and Computer Science

Department

Civil, Environmental, and Construction Engineering

Identifier

DP0029358

Document Type

Dissertation/Thesis

Campus Location

Orlando (Main) Campus

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