Dissertation/ Thesis
Geospatial probabilistic machine learning for analyzing urban vehicular mobility patterns with decision-making application
| Τίτλος: | Geospatial probabilistic machine learning for analyzing urban vehicular mobility patterns with decision-making application |
|---|---|
| Συγγραφείς: | Mohammadi, Sevin |
| Έτος έκδοσης: | 2024 |
| Συλλογή: | Columbia University: Academic Commons |
| Θεματικοί όροι: | Civil engineering, Urban transportation--Decision making, Transportation--Planning--Decision making, Machine learning, Decision making, Stochastic processes, Geospatial data--Computer processing |
| Περιγραφή: | The advent of Intelligent Transportation Systems (ITS) and smart cities, powered by sensors and cyber-physical systems, has transformed urban mobility planning through data-driven approaches. Advances in communication technologies enable the collection of large-scale mobility data, offering valuable insights into mobility patterns within urban road networks. Key sources of this data, such as vehicular travel durations and driver trajectory behaviors, are crucial for understanding the dynamics of traffic flow and drivers' interactions with urban road systems. This dissertation presents geospatial probabilistic machine learning models designed to capture spatiotemporal and contextual properties of vehicular mobility patterns in urban environments. It further emphasizes how these insights can address operational challenges, particularly in emergency response systems, where ambulances interact closely with urban road networks. Optimizing decision-making in such systems is intricately linked to efficient navigation, accurate travel duration prediction, and effective routing, all of which are deeply tied to understanding mobility patterns and dynamics. By its stochastic nature, mobility data is inherently uncertain, dynamic, sparse, and noisy, influenced by diverse spatiotemporal and exogenous factors. Probabilistic models are particularly well-suited for addressing these challenges, as they effectively handle uncertainty, variability, and noise, and their extensions, in the right way, are capable of handling sparse information and dynamic conditions. This dissertation focuses on probabilistic models, emphasizing their robustness and ability to generalize to new scenarios and cities, making them a powerful tool for effectively learning urban mobility dynamics and enhancing transportation systems' resilience and sustainability. |
| Τύπος εγγράφου: | thesis |
| Γλώσσα: | English |
| DOI: | 10.7916/91hm-wz75 |
| Διαθεσιμότητα: | https://doi.org/10.7916/91hm-wz75 |
| Αριθμός Καταχώρησης: | edsbas.F74DD299 |
| Βάση Δεδομένων: | BASE |
| FullText | Text: Availability: 0 CustomLinks: – Url: https://doi.org/10.7916/91hm-wz75# Name: EDS - BASE (ns324271) Category: fullText Text: View record from BASE |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.7916/91hm-wz75 Languages: – Text: English Subjects: – SubjectFull: Civil engineering Type: general – SubjectFull: Urban transportation--Decision making Type: general – SubjectFull: Transportation--Planning--Decision making Type: general – SubjectFull: Machine learning Type: general – SubjectFull: Decision making Type: general – SubjectFull: Stochastic processes Type: general – SubjectFull: Geospatial data--Computer processing Type: general Titles: – TitleFull: Geospatial probabilistic machine learning for analyzing urban vehicular mobility patterns with decision-making application Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Mohammadi, Sevin IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 01 Type: published Y: 2024 Identifiers: – Type: issn-locals Value: edsbas – Type: issn-locals Value: edsbas.oa |
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