Traffic congestion on urban freeways has become a serious problem in major metropolitan areas, causing delays, pollution, reduced road safety and degradation of infrastructure. Predictive freeway control measures are shown to be effective in reducing traffic congestion on urban freeways. Each predictive freeway control measure includes three major components: 1) freeway capacity constraints 2) a traffic prediction model, and 3) an optimization problem formulation with respective solution. Most

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... f the freeway control models considered deterministic values of capacity, occupancy or density as the physical constraints. However, previous research confirmed that the observed freeway capacity follows a probabilistic behavior. In terms of the traffic prediction models, the majority of control approaches used deterministic macroscopic traffic flow models to predict the traffic parameters. These models are not suitable in capturing lane by lane and stochastic traffic behavior caused by uncertainties in driving behaviors of road users and network conditions. Finally, the current optimization approaches mainly try to achieve system-wide benefits while overlooking the impact of local stochastic constraints and equity issues of such systems. In this thesis, I initially investigated and modeled the probabilistic behavior of freeway capacity based on real-world traffic data. The results not only confirmed probabilistic capacity but also indicated that different weather conditions result in the distinct parameters of the probability distribution functions. Thereafter, I developed a traffic state prediction approach based on a stochastic microscopic three-phase model. The rigorous analysis carried out showed that the proposed method predicts traffic parameters with an accuracy comparable to that of data-driven models without the same intensive data requirements. Finally, I developed a predictive ramp metering approach that facilitates cooperative control using a bargaining game theory approach. This configuration allows the contro [...]