Data-driven solutions to transportation problems / / edited by Yinhai Wang, Ziqiang Zeng

• Record Nr.: UNINA9910583349703321
• Pubbl/distr/stampa: Amsterdam, Netherlands : , : Elsevier, , [2019]; ©2019
• ISBN: 0-12-817027-1
• Descrizione fisica: 1 online resource (302 pages) : illustrations
• Disciplina: 388.015118
• Soggetti: Transportation - Mathematical models
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Nota di contenuto: 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.
• Sommario/riassunto: 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.