Transportation Big Data

Theory and Methods

Häftad, Engelska, 2024

1 432 kr

Beställningsvara. Skickas inom 7-10 vardagar. Fri frakt över 249 kr.

Beskrivning

Transportation Big Data: Theory and Methods is centered on the big data theory and methods. Big data is now a key topic in transportation, simply because the volume of data has increased exponentially due to the growth in the amount of traffic (all modes) and detectors. This book provides a structured analysis of the commonly used methods for handling transportation big data; it is supported by a wealth of transportation engineering examples, together with codes. It offers a concise, yet comprehensive, description of the key techniques and important tools in transportation big data analysis.

Covers big data applications in transportation engineering in real-world scenarios
Shows how to select different machine learning algorithms for processing, analyzing, and modeling transportation data
Provides an overview of the fundamental concepts of machine learning and how classical algorithms can be applied to transportation-related problems
Provides an overview of Python’s basic syntax and commonly used modules, enabling practical data analysis and modeling tasks using Python

Produktinformation

Utgivningsdatum:2024-12-05
Mått:152 x 229 x 24 mm
Vikt:740 g
Format:Häftad
Språk:Engelska
Antal sidor:454
Förlag:Elsevier Science
ISBN:9780443338915

Utforska kategorier

Fordon inom Sport, fritid och hobby

Mer om författaren

Dr. Zhiyuan (Terry) Liu is a Professor at the School of Transportation at Southeast University, China. He obtained his PhD degree from the National University of Singapore, Singapore. His research interests lie in the intersection and integration of transportation system analysis, big data analytics, and machine learning methods. He has published more than 100 papers in these areas. Dr. Ziyuan (Frank) Gu is an Associate Professor at the School of Transportation at Southeast University, China. He obtained his PhD degree from the University of New South Wales Sydney, Australia. His research interests include data-driven transportation system analysis and machine learning-assisted traffic simulation and optimization. He has published over 40 papers in these areas. Dr. Pan Liu is a Professor at the School of Transportation at Southeast University, China. He obtained his PhD degree from the University of South Florida, Tampa, USA. He has authored or co-authored over 100 papers in prestigious transportation journals. His research interests include transportation big data analysis, traffic operations and safety, and intelligent transportation systems

Innehållsförteckning

1. Introduction2. Data analysis in Python3.Data preprocessing and exploratory data analysis4. Data visualization5. Machine learning basics6. Linear model7. Support vector machine8. Decision tree9. Clustering analysis10. Ensemble learning11. Artificial neural network12. Deep learning