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198224 https://hdl.handle.net/2286/R.2.N.198224 http://rightsstatements.org/vocab/InC/1.0/ All Rights Reserved 2024 446 pages Doctoral Dissertation Academic theses Text eng Doll, Alisa Zhao, Ming Zhou, Xuesong Pendyala, Ram M. Mirchandani, Pitu Polzin, Steven Jenq, Jeff Belezamo, Baloka Arizona State University Partial requirement for: Ph.D., Arizona State University, 2024 Field of study: Civil, Environmental and Sustainable Engineering ABSTRACT The challenge of optimizing traffic signal coordination in urban environments, particularly during peak periods, has long posed significant difficulties for traffic engineers. Traditional methods, which often rely on static models or fixed-time signal plans, struggle to adapt to the dynamic and stochastic nature of real-world traffic flows. This dissertation addresses these challenges through a two-part research approach that enhances both the understanding and management of traffic at signalized intersections. The first part of this research extends Newell's polynomial fluid-based queuing model by employing time-dependent polynomial functions to approximate stochastic arrival flow rates. These functions aim to improve the estimation of queue profiles, including time-dependent queue lengths and total delays. Validation of this approach is conducted through real-world experiments using high-resolution video detection systems to capture queue lengths and traffic patterns during peak periods. By analyzing cumulative cycles and segmenting data based on residual queue values, this study seeks to provide a more accurate understanding of peak period congestion dynamics under over-capacity conditions. The second part introduces the GreenSync Survival Function (GSF) to enhance Newell’s Time-Space Diagram. The GSF Model integrates Weibull distribution functions to introduce a probabilistic layer that improves the assessment of green wave synchronization and better accounts for variability in vehicle arrivals along congested signalized roadways. Validation of the GSF Model is conducted through the application of real-world traffic data, providing a sophisticated framework for optimizing signal coordination by predicting the likelihood of vehicles encountering green or red lights within a coordinated segment of a peak-period congested arterial. Overall, this research contributes to the field of traffic engineering by developing two innovative frameworks designed to improve the functionality of traditional traffic models. These models offer robust and practical tools for optimizing traffic signal coordination, enhancing the efficiency and reliability of urban traffic management. Civil Engineering Transportation Statistics Green Wave Optimization Queuing theory Traffic congestion Traffic Flow Modeling Traffic Signal Coordination Weibull distribution Optimizing Traffic Signals During Peak-Period Congestion: A Polynomial Fluid and Survival-Based Approach