Description
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
Table of Contents
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
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