Browsing by Author "Heshami, Seiran"

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    Freeway control under stochastic capacity in a connected vehicle environment based on a dynamic bargaining game approach
    (2020-12) Heshami, Seiran; Kattan, Lina; Wirasinghe, S. Chandra; Sabouri, Alireza; De Barros, Alexandre Gomes; Walters, Nigel M.; Abbas, Montasir M.
    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 of 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 controllers to communicate their state and decision information, and find the control solution with a compromise between local and global performance. This unique property allows local equity considerations, in regard to a fair distribution of occurrence of breakdown events, while seeking system-wide efficiency. The results showed that the proposed model outperformed the deterministic capacity-based models in terms of the effectiveness and equity of the ramp metering solutions.
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    Real-time Queue Length Estimation on Freeway Off-ramps Using Case Based Reasoning Combined with Kalman Filter
    (2015-09-18) Heshami, Seiran; Kattan, Lina
    Real-time queue length estimation and prediction provides useful information for proactively managing transportation networks. Queue spillback from off-ramps onto main freeway lanes is a serious traffic issue that can be efficiently managed using dynamic queue information. In this thesis, a case-based reasoning algorithm combined with a Kalman filter is developed to provide real-time queue length measurements and predictions on long freeway off-ramps. Estimations are based on occupancy readings from three loop detectors installed on the ramp. The proposed method is examined using a micro-simulation model in a Quadstone Paramics package on an off-ramp‏ with a length of 650 meters. The simulation results demonstrate that the model is capable of estimating and predicting the queue length on long off-ramps in 60 second time intervals. The performance of the algorithm is examined under various demand loading scenarios, estimation time intervals and number of detectors through several experiments.
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    The Transition to Net-Zero of Heavy-Duty Road Freight in Alberta: A Scenario Model
    (2024-11-18) Redick, Zachary Campo; Layzell, David B.; de Barros, Alexandre; Heshami, Seiran; Kattan, Lina
    The global climate crisis has prompted Canada’s commitment to achieving net-zero greenhouse-gas (GHG) emissions by 2050. The transportation sector, responsible for ~25% of Canada's GHG emissions, faces challenges in decarbonizing heavy-duty vehicles (HDVs), which make up ~20% of transportation emissions. Alberta’s heavy-duty trucking industry, a significant emissions contributor, encounters challenging conditions with strict range and vehicle weight requirements, complicating efforts to decarbonize. This thesis models the transition of Alberta’s heavy-duty trucking sector to net-zero GHG emissions, evaluating the feasibility of meeting Canada’s federal targets of 35% zero-emission vehicle (ZEV) sales by 2030 and nearly 100% by 2040. A comprehensive stock and flow model for hydrogen fuel-cell electric vehicles (FCEVs) and battery electric vehicles is developed, integrating vehicle projections, kilometers traveled, energy use, and GHG emissions under different decarbonization scenarios. The study also explores the development of a hydrogen-based value chain for Alberta's long-haul trucking industry, addressing the economic, logistical, and technical challenges of building infrastructure to support FCEVs. The economic analysis compares the total cost of ownership (TCO) for FCEVs and internal combustion engine vehicles (ICEVs) and examines the role of government policies, particularly the carbon tax, in supporting the transition. Key findings indicate that meeting the 2030 sales target is unlikely due to infrastructure and deployment challenges, while the 2040 target, though challenging, remains feasible. The extended timeline allows for the development of zero-emission vehicle technologies and hydrogen infrastructure, providing substantial GHG emission reduction benefits of at least 87% across all scenarios. FCEVs initially have a higher TCO than ICEVs, but as production scales and technology improves, the TCO is projected to fall below ICEVs by 2045. Incremental costs are projected to peak at CAD 500 million annually by 2035, achieving cost parity by 2040, and resulting in total costs of CAD 4 billion, with potential savings of up to CAD 2.5 billion annually by 2050. The projected carbon tax revenue covers the incremental costs, and even if doubled, would require only 75% of the revenue, demonstrating the strong economic feasibility of this beneficial and essential transition.