Machine learning for transportation research and applications:
Transportation issues are often too complicated to be addressed by conventional parametric methods. Increasing data availability and recent advancements in machine learning provide new methods to tackle the challenging transportation problems. Readers will learn how to develop and apply different ty...
Gespeichert in:
| Hauptverfasser: | , , |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Philadelphia
Elsevier
[2023]
|
| Schlagworte: | |
| Online-Zugang: | UEI03 |
| Zusammenfassung: | Transportation issues are often too complicated to be addressed by conventional parametric methods. Increasing data availability and recent advancements in machine learning provide new methods to tackle the challenging transportation problems. Readers will learn how to develop and apply different types of machine learning models to transportation related problems. Example applications include transportation data generations, traffic sensing, transportation mode recognition, transportation system management and control, traffic flow prediction, and traffic safety analysis. |
| Beschreibung: | Part One: Overview 1. General Introduction and Overview 2. Fundamental Mathematics 3. Machine Learning Basics Part Two: Methodologies and Applications 4. Classical ML Methods 5. Convolutional Neural Network 6. Graph Neural Network 7. Sequence Modeling 8. Probabilistic Models 9. Reinforcement Learning 10. Generative Models 11. Meta/Transfer Learning Part Three: Future Research and Applications The Future of Transportation and AI |
| Beschreibung: | 1 Online-Ressource (xi, 239 Seiten) |
| ISBN: | 9780323996808 0323961266 |
Internformat
MARC
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| 520 | 3 | |a Transportation issues are often too complicated to be addressed by conventional parametric methods. Increasing data availability and recent advancements in machine learning provide new methods to tackle the challenging transportation problems. Readers will learn how to develop and apply different types of machine learning models to transportation related problems. Example applications include transportation data generations, traffic sensing, transportation mode recognition, transportation system management and control, traffic flow prediction, and traffic safety analysis. | |
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Datensatz im Suchindex
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| author | Wang, Yinhai Cui, Zhiyong Ke, Ruimin |
| author_GND | (DE-588)1052895808 (DE-588)1214185525 |
| author_facet | Wang, Yinhai Cui, Zhiyong Ke, Ruimin |
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| author_sort | Wang, Yinhai |
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| discipline | Informatik Verkehr / Transport |
| discipline_str_mv | Informatik Verkehr / Transport |
| format | Electronic eBook |
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| id | DE-604.BV048983668 |
| illustrated | Not Illustrated |
| index_date | 2024-07-03T22:05:29Z |
| indexdate | 2024-07-10T09:51:59Z |
| institution | BVB |
| isbn | 9780323996808 0323961266 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-034247123 |
| oclc_num | 1385291015 |
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| physical | 1 Online-Ressource (xi, 239 Seiten) |
| psigel | ZDB-4-NLEBK |
| publishDate | 2023 |
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| publisher | Elsevier |
| record_format | marc |
| spelling | Wang, Yinhai Verfasser (DE-588)1052895808 aut Machine learning for transportation research and applications Yinhai Wang, Zhiyong Cui, Ruimin Ke Philadelphia Elsevier [2023] © 2023 1 Online-Ressource (xi, 239 Seiten) txt rdacontent c rdamedia cr rdacarrier Part One: Overview 1. General Introduction and Overview 2. Fundamental Mathematics 3. Machine Learning Basics Part Two: Methodologies and Applications 4. Classical ML Methods 5. Convolutional Neural Network 6. Graph Neural Network 7. Sequence Modeling 8. Probabilistic Models 9. Reinforcement Learning 10. Generative Models 11. Meta/Transfer Learning Part Three: Future Research and Applications The Future of Transportation and AI Transportation issues are often too complicated to be addressed by conventional parametric methods. Increasing data availability and recent advancements in machine learning provide new methods to tackle the challenging transportation problems. Readers will learn how to develop and apply different types of machine learning models to transportation related problems. Example applications include transportation data generations, traffic sensing, transportation mode recognition, transportation system management and control, traffic flow prediction, and traffic safety analysis. Big Data (DE-588)4802620-7 gnd rswk-swf Neuronales Netz (DE-588)4226127-2 gnd rswk-swf Verkehr (DE-588)4062901-6 gnd rswk-swf Maschinelles Lernen (DE-588)4193754-5 gnd rswk-swf Datenanalyse (DE-588)4123037-1 gnd rswk-swf Mobilität (DE-588)4039785-3 gnd rswk-swf Transport (DE-588)4060680-6 gnd rswk-swf Das Selbst, das Ich, Identität und Persönlichkeit Environmental economics Social, group or collective psychology Sozialpsychologie environment The self, ego, identity, personality Umweltwissenschaften, Umwelttechnik Umweltökonomie Big Data (DE-588)4802620-7 s Mobilität (DE-588)4039785-3 s Transport (DE-588)4060680-6 s Verkehr (DE-588)4062901-6 s Datenanalyse (DE-588)4123037-1 s Maschinelles Lernen (DE-588)4193754-5 s Neuronales Netz (DE-588)4226127-2 s DE-604 Cui, Zhiyong Verfasser aut Ke, Ruimin Verfasser (DE-588)1214185525 aut Erscheint auch als Druck-Ausgabe 978-0-323-96126-4 |
| spellingShingle | Wang, Yinhai Cui, Zhiyong Ke, Ruimin Machine learning for transportation research and applications Big Data (DE-588)4802620-7 gnd Neuronales Netz (DE-588)4226127-2 gnd Verkehr (DE-588)4062901-6 gnd Maschinelles Lernen (DE-588)4193754-5 gnd Datenanalyse (DE-588)4123037-1 gnd Mobilität (DE-588)4039785-3 gnd Transport (DE-588)4060680-6 gnd |
| subject_GND | (DE-588)4802620-7 (DE-588)4226127-2 (DE-588)4062901-6 (DE-588)4193754-5 (DE-588)4123037-1 (DE-588)4039785-3 (DE-588)4060680-6 |
| title | Machine learning for transportation research and applications |
| title_auth | Machine learning for transportation research and applications |
| title_exact_search | Machine learning for transportation research and applications |
| title_exact_search_txtP | Machine learning for transportation research and applications |
| title_full | Machine learning for transportation research and applications Yinhai Wang, Zhiyong Cui, Ruimin Ke |
| title_fullStr | Machine learning for transportation research and applications Yinhai Wang, Zhiyong Cui, Ruimin Ke |
| title_full_unstemmed | Machine learning for transportation research and applications Yinhai Wang, Zhiyong Cui, Ruimin Ke |
| title_short | Machine learning for transportation research and applications |
| title_sort | machine learning for transportation research and applications |
| topic | Big Data (DE-588)4802620-7 gnd Neuronales Netz (DE-588)4226127-2 gnd Verkehr (DE-588)4062901-6 gnd Maschinelles Lernen (DE-588)4193754-5 gnd Datenanalyse (DE-588)4123037-1 gnd Mobilität (DE-588)4039785-3 gnd Transport (DE-588)4060680-6 gnd |
| topic_facet | Big Data Neuronales Netz Verkehr Maschinelles Lernen Datenanalyse Mobilität Transport |
| work_keys_str_mv | AT wangyinhai machinelearningfortransportationresearchandapplications AT cuizhiyong machinelearningfortransportationresearchandapplications AT keruimin machinelearningfortransportationresearchandapplications |