Mathematical Model and Cloud Computing of Road Network Operations under Non-Recurrent Events
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 pre...
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| Format: | Thesis |
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Curtin University
2022
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| Online Access: | http://hdl.handle.net/20.500.11937/91305 |
| _version_ | 1848765502412816384 |
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| author | Aljuaydi, Fahad Mesfer M |
| author_facet | Aljuaydi, Fahad Mesfer M |
| author_sort | Aljuaydi, Fahad Mesfer M |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-14T11:36:16Z |
| format | Thesis |
| id | curtin-20.500.11937-91305 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T11:36:16Z |
| publishDate | 2022 |
| publisher | Curtin University |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-913052023-04-03T05:10:10Z Mathematical Model and Cloud Computing of Road Network Operations under Non-Recurrent Events Aljuaydi, Fahad Mesfer M 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. 2022 Thesis http://hdl.handle.net/20.500.11937/91305 Curtin University fulltext |
| spellingShingle | Aljuaydi, Fahad Mesfer M Mathematical Model and Cloud Computing of Road Network Operations under Non-Recurrent Events |
| title | Mathematical Model and Cloud Computing of Road Network Operations under Non-Recurrent Events |
| title_full | Mathematical Model and Cloud Computing of Road Network Operations under Non-Recurrent Events |
| title_fullStr | Mathematical Model and Cloud Computing of Road Network Operations under Non-Recurrent Events |
| title_full_unstemmed | Mathematical Model and Cloud Computing of Road Network Operations under Non-Recurrent Events |
| title_short | Mathematical Model and Cloud Computing of Road Network Operations under Non-Recurrent Events |
| title_sort | mathematical model and cloud computing of road network operations under non-recurrent events |
| url | http://hdl.handle.net/20.500.11937/91305 |