Machine learning methods for transportation under uncertainty
Motivated by the prevalence of uncertainty and the widespread use of modeling in Transportation, we develop and study effective methods for modeling Transportation under uncertainty. These methods are Machine Learning-based, i.e., they extract patterns from data and leverage them for better modeling...
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| Format: | Thesis-Doctor of Philosophy |
| Language: | English |
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Nanyang Technological University
2022
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| Online Access: | https://hdl.handle.net/10356/153581 |
| _version_ | 1811693663007277056 |
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| author | Peled, Inon |
| author2 | - |
| author_facet | - Peled, Inon |
| author_sort | Peled, Inon |
| collection | NTU |
| description | Motivated by the prevalence of uncertainty and the widespread use of modeling in Transportation, we develop and study effective methods for modeling Transportation under uncertainty. These methods are Machine Learning-based, i.e., they extract patterns from data and leverage them for better modeling. We study them through several case studies, including: quick adaptation of traffic models upon road incidents; estimation of mobility demand from limited observations; and predictive optimization of dynamic Public Transport. Our results yield several positive conclusions about the effectiveness of the studied methods for current and future Transportation. |
| first_indexed | 2024-10-01T06:55:15Z |
| format | Thesis-Doctor of Philosophy |
| id | ntu-10356/153581 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T06:55:15Z |
| publishDate | 2022 |
| publisher | Nanyang Technological University |
| record_format | dspace |
| spelling | ntu-10356/1535812023-03-05T16:36:45Z Machine learning methods for transportation under uncertainty Peled, Inon - Interdisciplinary Graduate School (IGS) Technical University of Denmark Justin Dauwels J.H.G.Dauwels@tudelft.nl Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence Engineering::Civil engineering::Transportation Motivated by the prevalence of uncertainty and the widespread use of modeling in Transportation, we develop and study effective methods for modeling Transportation under uncertainty. These methods are Machine Learning-based, i.e., they extract patterns from data and leverage them for better modeling. We study them through several case studies, including: quick adaptation of traffic models upon road incidents; estimation of mobility demand from limited observations; and predictive optimization of dynamic Public Transport. Our results yield several positive conclusions about the effectiveness of the studied methods for current and future Transportation. Doctor of Philosophy 2022-01-24T23:54:41Z 2022-01-24T23:54:41Z 2021 Thesis-Doctor of Philosophy Peled, I. (2021). Machine learning methods for transportation under uncertainty. Doctoral thesis, Nanyang Technological University, Singapore. https://hdl.handle.net/10356/153581 https://hdl.handle.net/10356/153581 10.32657/10356/153581 en This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0). application/pdf Nanyang Technological University |
| spellingShingle | Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence Engineering::Civil engineering::Transportation Peled, Inon Machine learning methods for transportation under uncertainty |
| title | Machine learning methods for transportation under uncertainty |
| title_full | Machine learning methods for transportation under uncertainty |
| title_fullStr | Machine learning methods for transportation under uncertainty |
| title_full_unstemmed | Machine learning methods for transportation under uncertainty |
| title_short | Machine learning methods for transportation under uncertainty |
| title_sort | machine learning methods for transportation under uncertainty |
| topic | Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence Engineering::Civil engineering::Transportation |
| url | https://hdl.handle.net/10356/153581 |
| work_keys_str_mv | AT peledinon machinelearningmethodsfortransportationunderuncertainty |