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Data-driven solutions to transportation problems /
Data-Driven Solutions to Transportation Problems explores the fundamental principle of analyzing different types of transportation-related data using methodologies such as the data fusion model, the big data mining approach, computer vision-enabled traffic sensing data analysis, and machine learning...
| Clasificación: | Libro Electrónico |
|---|---|
| Otros Autores: | , |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Amsterdam :
Elsevier,
[2019]
|
| Edición: | First edition. |
| Temas: | |
| Acceso en línea: | Texto completo |
MARC
| LEADER | 00000cam a2200000 i 4500 | ||
|---|---|---|---|
| 001 | SCIDIR_on1076878084 | ||
| 003 | OCoLC | ||
| 005 | 20231120010333.0 | ||
| 006 | m o d | ||
| 007 | cr cnu---unuuu | ||
| 008 | 181203s2019 ne a o 000 0 eng d | ||
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| 020 | |a 9780128170274 |q (electronic bk.) | ||
| 020 | |a 0128170271 |q (electronic bk.) | ||
| 020 | |z 9780128170267 | ||
| 020 | |z 0128170263 | ||
| 035 | |a (OCoLC)1076878084 | ||
| 050 | 4 | |a HE192.5 | |
| 072 | 7 | |a BUS |x 070100 |2 bisacsh | |
| 072 | 7 | |a TRA |x 009000 |2 bisacsh | |
| 082 | 0 | 4 | |a 388.015118 |2 23 |
| 245 | 0 | 0 | |a Data-driven solutions to transportation problems / |c [edited by] Yinhai Wang, Ziqiang Zeng. |
| 250 | |a First edition. | ||
| 264 | 1 | |a Amsterdam : |b Elsevier, |c [2019] | |
| 300 | |a 1 online resource (1 volume) : |b illustrations | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 588 | |a Online resource; title from PDF title page (EBSCO, viewed December 4, 2018). | ||
| 520 | |a Data-Driven Solutions to Transportation Problems explores the fundamental principle of analyzing different types of transportation-related data using methodologies such as the data fusion model, the big data mining approach, computer vision-enabled traffic sensing data analysis, and machine learning. The book examines the state-of-the-art in data-enabled methodologies, technologies and applications in transportation. Readers will learn how to solve problems relating to energy efficiency under connected vehicle environments, urban travel behavior, trajectory data-based travel pattern identification, public transportation analysis, traffic signal control efficiency, optimizing traffic networks network, and much more. | ||
| 650 | 0 | |a Transportation |x Mathematical models. | |
| 650 | 7 | |a BUSINESS & ECONOMICS |x Industries |x Transportation. |2 bisacsh | |
| 650 | 7 | |a TRANSPORTATION |x Public Transportation. |2 bisacsh | |
| 650 | 7 | |a Transportation |x Mathematical models. |2 fast |0 (OCoLC)fst01155115 | |
| 700 | 1 | |a Wang, Yinhai, |e editor. | |
| 700 | 1 | |a Zeng, Ziqiang |c (Assistant professor), |e editor. | |
| 776 | 0 | 8 | |i Print version: |a Wang, Yinhai. |t Data-driven solutions to transportation problems. |d Amsterdam : Elsevier, 2018 |z 9780128170267 |w (OCoLC)1063565232 |
| 856 | 4 | 0 | |u https://sciencedirect.uam.elogim.com/science/book/9780128170267 |z Texto completo |