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dc.contributor.authorAljuaydi, Fahad Mesfer M
dc.contributor.supervisorBenchawan Wiwatanapatapheeen_US
dc.contributor.supervisorYong Wuen_US
dc.date.accessioned2023-04-03T05:10:10Z
dc.date.available2023-04-03T05:10:10Z
dc.date.issued2022en_US
dc.identifier.urihttp://hdl.handle.net/20.500.11937/91305
dc.description.abstract

Optimal traffic control under incident-driven congestion is crucial for road safety and maintaining network performance. Over the last decade, prediction and simulation of road traffic play important roles in network operation. This dissertation focuses on development of a machine learning-based prediction model, a stochastic cell transmission model (CTM), and an optimisation model. Numerical studies were performed to evaluate the proposed models. The results indicate that proposed models are helpful for road management during road incidents.

en_US
dc.publisherCurtin Universityen_US
dc.titleMathematical Model and Cloud Computing of Road Network Operations under Non-Recurrent Eventsen_US
dc.typeThesisen_US
dcterms.educationLevelPhDen_US
curtin.departmentSchool of Electrical Engineering, Computing and Mathematical Sciencesen_US
curtin.accessStatusOpen accessen_US
curtin.facultyScience and Engineeringen_US


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