Transportation Big Data
Theory and Methods
- 1st Edition - November 29, 2024
- Latest edition
- Authors: Zhiyuan Liu, Ziyuan Gu, Pan Liu
- Language: English
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 in… Read more
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Description
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.
Key features
Key features
- 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
Readership
Readership
Practitioners in the transportation sector who seek to learn and apply big data and machine learning techniques for solving transportation problems
Table of contents
Table of contents
1. Introduction
1.1 Aims and Scopes
1.2 Foundations of This Book
1.3 Introduction to the Example Datasets
1.4 Introduction to Chapters
2. Data analysis in Python
2.1 Setting Up the Python Environment
2.2 Python Basics
2.3 Container in Python
2.4 Control Flow Statements
2.5 Function Definition and Invocation
2.6 Anonymous functions
2.7 Python Modules
2.8 Chapter Conclusion
2.9 Chapter exercises
3. Data preprocessing and exploratory data analysis
3.1 Data Preprocessing
3.2 Basics of Spatiotemporal Data Analysis
3.3 Exploratory Data Analysis
3.4 Chapter Conclusion
3.5 Chapter Exercises
4. Data visualization
4.1 Setting Chart Elements
4.2 Common Visualization Charts
4.3 Fundamentals of Interactive Data Visualization
4.4 Basic of Point-Line Network Plotting
4.5 Chapter Conclusion
4.6 Chapter Exercises
5. Machine learning basics
5.1 Fundamental Concepts of Machine Learning
5.2 The Introduction of Tasks in Machine Learning
5.3 The Basic Process of Machine Learning
5.4 Model Evaluation and Selection
5.5 The Methods for Improving Model Performance
5.6 Techniques in Machine Learning Training
5.7 Chapter Conclusion
5.8 Chapter Exercises
6. Linear model
6.1 Linear Regression
6.2 Logistic Regression
6.3 Chapter Conclusion
6.4 Chapter Exercises
7. Support vector machine
7.1 Basic Concepts in Support Vector Machine
7.2 Linearly Separable Support Vector Machine
7.3 Soft Margin Linear Support Vector Machine
7.4 Nonlinear Support Vector Machine
7.5 Solving Support Vector Machine Models
7.6 Example Application of SVM Model
7.7 Chapter Conclusion
7.8 Chapter Exercises
8. Decision tree
8.1 Decision Tree Model
8.2 Decision Tree Generation
8.3 Decision Tree Pruning
8.4 The Application Examples of Decision Tree
8.5 Chapter Conclusion
8.6 Chapter Exercises
9. Clustering analysis
9.1 General Modelling Process of Cluster Analysis
9.2 K-mean clustering algorithms
9.3 Gaussian Mixture Clustering
9.4 Hierarchical Clustering Algorithm
9.5 DBSCAN Clustering Method Based on Density
9.6 Chapter Conclusion
9.7 Chapter Exercises
10. Ensemble learning
10.1 Classification of Ensemble Learning
10.2 Boosting
10.3 Bagging
10.4 Diversity of Individual Learners
10.5 Example Applications of Ensemble Learning Models
10.6 Chapter Conclusion
10.7 Chapter Exercises
11. Artificial neural network
11.1 Basic Structure of Neural Networks
11.2 Activation Functions and Forward Propagation
11.3 Reverse propagation
11.4 Solutions to Common Problems in Neural Networks
11.5 Application of Neural Networks to Transportation Problems
11.6 Chapter Conclusion
11.7 Chapter Exercises
12. Deep learning
12.1 Convolutional Neural Networks
12.2 Recurrent Neural Networks
12.3 Graph Neural Networks
12.4 Chapter Conclusion
12.5 Chapter Exercises
1.1 Aims and Scopes
1.2 Foundations of This Book
1.3 Introduction to the Example Datasets
1.4 Introduction to Chapters
2. Data analysis in Python
2.1 Setting Up the Python Environment
2.2 Python Basics
2.3 Container in Python
2.4 Control Flow Statements
2.5 Function Definition and Invocation
2.6 Anonymous functions
2.7 Python Modules
2.8 Chapter Conclusion
2.9 Chapter exercises
3. Data preprocessing and exploratory data analysis
3.1 Data Preprocessing
3.2 Basics of Spatiotemporal Data Analysis
3.3 Exploratory Data Analysis
3.4 Chapter Conclusion
3.5 Chapter Exercises
4. Data visualization
4.1 Setting Chart Elements
4.2 Common Visualization Charts
4.3 Fundamentals of Interactive Data Visualization
4.4 Basic of Point-Line Network Plotting
4.5 Chapter Conclusion
4.6 Chapter Exercises
5. Machine learning basics
5.1 Fundamental Concepts of Machine Learning
5.2 The Introduction of Tasks in Machine Learning
5.3 The Basic Process of Machine Learning
5.4 Model Evaluation and Selection
5.5 The Methods for Improving Model Performance
5.6 Techniques in Machine Learning Training
5.7 Chapter Conclusion
5.8 Chapter Exercises
6. Linear model
6.1 Linear Regression
6.2 Logistic Regression
6.3 Chapter Conclusion
6.4 Chapter Exercises
7. Support vector machine
7.1 Basic Concepts in Support Vector Machine
7.2 Linearly Separable Support Vector Machine
7.3 Soft Margin Linear Support Vector Machine
7.4 Nonlinear Support Vector Machine
7.5 Solving Support Vector Machine Models
7.6 Example Application of SVM Model
7.7 Chapter Conclusion
7.8 Chapter Exercises
8. Decision tree
8.1 Decision Tree Model
8.2 Decision Tree Generation
8.3 Decision Tree Pruning
8.4 The Application Examples of Decision Tree
8.5 Chapter Conclusion
8.6 Chapter Exercises
9. Clustering analysis
9.1 General Modelling Process of Cluster Analysis
9.2 K-mean clustering algorithms
9.3 Gaussian Mixture Clustering
9.4 Hierarchical Clustering Algorithm
9.5 DBSCAN Clustering Method Based on Density
9.6 Chapter Conclusion
9.7 Chapter Exercises
10. Ensemble learning
10.1 Classification of Ensemble Learning
10.2 Boosting
10.3 Bagging
10.4 Diversity of Individual Learners
10.5 Example Applications of Ensemble Learning Models
10.6 Chapter Conclusion
10.7 Chapter Exercises
11. Artificial neural network
11.1 Basic Structure of Neural Networks
11.2 Activation Functions and Forward Propagation
11.3 Reverse propagation
11.4 Solutions to Common Problems in Neural Networks
11.5 Application of Neural Networks to Transportation Problems
11.6 Chapter Conclusion
11.7 Chapter Exercises
12. Deep learning
12.1 Convolutional Neural Networks
12.2 Recurrent Neural Networks
12.3 Graph Neural Networks
12.4 Chapter Conclusion
12.5 Chapter Exercises
Product details
Product details
- Edition: 1
- Latest edition
- Published: November 29, 2024
- Language: English
About the authors
About the authors
ZL
Zhiyuan Liu
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.
Affiliations and expertise
Professor, Southeast University, ChinaZG
Ziyuan Gu
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.
Affiliations and expertise
School of Transportation, Southeast University, ChinaPL
Pan Liu
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
Affiliations and expertise
Southeast UniversityView book on ScienceDirect
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