Data-Driven Solutions To Transportation Problems

Understanding data-driven transportation science is essential for addressing the newest challenges in the quickly developing intelligent transportation systems. Data-Driven Transportation Science explores the fundamental principle of analysing different types of transportation-related data using methodologies such as data fusion model, 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. The book helps readers solve such transportation systems problems as energy efficiency under connected vehicle environment, urban travel behavior, trajectory data based travel pattern identification, public transportation analysis, traffic signal control efficiency, optimizing traffic networks network, and much more Synthesizes the newest developments in data-driven transportation science Includes case studies and examples in each chapter that illustrate the application of methodologies and technologies employed Useful for both theoretical and technical oriented researchers

• | Author: Ziqiang Zeng, Yinhai Wang
• | Publisher: Elsevier
• | Publication Date: Dec 05, 2018
• | Number of Pages: 299 pages
• | Language: English
• | Binding: Paperback
• | ISBN-10: 0128170263
• | ISBN-13: 9780128170267