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1. |
Record Nr. |
UNINA9910583349703321 |
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Titolo |
Data-driven solutions to transportation problems / / edited by Yinhai Wang, Ziqiang Zeng |
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Pubbl/distr/stampa |
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Amsterdam, Netherlands : , : Elsevier, , [2019] |
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©2019 |
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ISBN |
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Descrizione fisica |
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1 online resource (302 pages) : illustrations |
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Disciplina |
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Soggetti |
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Transportation - Mathematical models |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di contenuto |
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1. Overview of data-driven solutions -- 2. Data-driven energy efficient driving control in connected vehicle environment -- 3. Machine learning and computer vision-enabled traffic sensing data analysis and quality enhancement -- 4. Data-driven approaches for estimating travel time reliability -- 5. Urban travel behavior study based on data fusion model -- 6. Urban travel mobility exploring with large-scale trajectory data -- 7. Public transportation big data mining and analysis -- 8. Simulation-based optimization for network modeling with heterogeneous data -- 9. Network modelling and resilience analysis of air transportation : a data-driven, open-source approach -- 10. Health assessment of electric multiple units. |
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Sommario/riassunto |
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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. |
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2. |
Record Nr. |
UNINA9910557285303321 |
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Autore |
Mondal Pinki |
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Titolo |
Global Vegetation and Land Surface Dynamics in a Changing Climate |
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Pubbl/distr/stampa |
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Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2021 |
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Descrizione fisica |
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1 online resource (108 p.) |
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Soggetti |
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Research & information: general |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Sommario/riassunto |
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Global ecosystem changes are influenced by a combination of natural and anthropogenic factors. Ongoing changes in rainfall, temperature, and carbon dioxide in the atmosphere can affect natural or managed vegetation, such as forest, grassland, or farmland. Moreover, anthropogenic pressures, such as forest clearing, cattle grazing, increasing infrastructural development, intensive management, and expansion of cropland, can contribute to ecosystem degradation. This collection presents a wide range of studies examining natural and anthropogenic drivers in diverse ecosystems in Africa, Asia, and North America. |
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