Freeway control under stochastic capacity in a connected vehicle environment based on a dynamic bargaining game approach

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dc.contributor.advisorKattan, Lina
dc.contributor.authorHeshami, Seiran
dc.contributor.committeememberWirasinghe, S. Chandra
dc.contributor.committeememberSabouri, Alireza
dc.contributor.committeememberDe Barros, Alexandre Gomes
dc.contributor.committeememberWalters, Nigel M.
dc.contributor.committeememberAbbas, Montasir M.
dc.date2021-02
dc.date.accessioned2020-12-22T22:51:13Z
dc.date.available2020-12-22T22:51:13Z
dc.date.issued2020-12
dc.description.abstractTraffic 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.en_US
dc.identifier.citationHeshami, S. (2020). Freeway control under stochastic capacity in a connected vehicle environment based on a dynamic bargaining game approach (Doctoral thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca.en_US
dc.identifier.doihttp://dx.doi.org/10.11575/PRISM/38495
dc.identifier.urihttp://hdl.handle.net/1880/112901
dc.language.isoengen_US
dc.publisher.facultySchulich School of Engineeringen_US
dc.publisher.institutionUniversity of Calgaryen
dc.rightsUniversity of Calgary graduate students retain copyright ownership and moral rights for their thesis. You may use this material in any way that is permitted by the Copyright Act or through licensing that has been assigned to the document. For uses that are not allowable under copyright legislation or licensing, you are required to seek permission.en_US
dc.subjectfreeway controlen_US
dc.subjectstochastic capacityen_US
dc.subjectbargaining gameen_US
dc.subjectconnected vehiclesen_US
dc.subject.classificationSociology--Transportationen_US
dc.subject.classificationEngineeringen_US
dc.subject.classificationEngineering--Civilen_US
dc.titleFreeway control under stochastic capacity in a connected vehicle environment based on a dynamic bargaining game approachen_US
dc.typedoctoral thesisen_US
thesis.degree.disciplineEngineering – Civilen_US
thesis.degree.grantorUniversity of Calgaryen_US
thesis.degree.nameDoctor of Philosophy (PhD)en_US
ucalgary.item.requestcopytrueen_US
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