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.
1. Introduction
2. Python in data science
3. Exploratory data analysis
4. Data visualization
5. Machine learning basics
6. Linear model
7. Support vector machine
8. Decision tree
9. Clustering analysis
10. Ensemble learning
11. Artificial neural network
12. Deep learning
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
