Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/153581
Title: Machine learning methods for transportation under uncertainty
Authors: Peled, Inon
Keywords: Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence
Engineering::Civil engineering::Transportation
Issue Date: 2021
Publisher: Nanyang Technological University
Source: Peled, I. (2021). Machine learning methods for transportation under uncertainty. Doctoral thesis, Nanyang Technological University, Singapore. https://hdl.handle.net/10356/153581
Abstract: 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.
URI: https://hdl.handle.net/10356/153581
DOI: 10.32657/10356/153581
Rights: This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0).
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:IGS Theses

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