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Transportation Big Data
Theory and Methods
- 1st Edition - December 1, 2024
- Authors: Zhiyuan Liu, Ziyuan Gu
- Language: English
- Paperback ISBN:9 7 8 - 0 - 4 4 3 - 3 3 8 9 1 - 5
- eBook ISBN:9 7 8 - 0 - 4 4 3 - 3 3 8 9 2 - 2
Transportation Big Data: Theory and Methods is centered around big data theory and methods. As big data is now a key topic in transport because the volume of data has increa… Read more
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Request a sales quoteTransportation Big Data: Theory and Methods is centered around big data theory and methods. As big data is now a key topic in transport 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 commonly used methods for handling transportation big data. It is supported by a wealth of transportation engineering examples with codes. The book offers a concise, yet comprehensive description 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
Practitioners in the transportation sector who seek to learn and apply big data and machine learning techniques for solving transportation problems
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. Python in data science
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 Exception Handling
2.7 Anonymous Functions
2.8 Python Modules
2.9 Chapter Conclusion
3. 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 Non-linear 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. Python in data science
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 Exception Handling
2.7 Anonymous Functions
2.8 Python Modules
2.9 Chapter Conclusion
3. 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 Non-linear 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
- No. of pages: 275
- Language: English
- Edition: 1
- Published: December 1, 2024
- Imprint: Elsevier
- Paperback ISBN: 9780443338915
- eBook ISBN: 9780443338922
ZL
Zhiyuan Liu
Dr Zhiyuan Liu is a Professor at Southeast University, China. He obtained his Ph.D. degree from the National University of Singapore, Singapore. He is currently with the National Graduate College for Elite Engineers at Southeast University, China. His research background lies in the intersection and integration of transportation system analysis, big data analytics, and machine learning methods. He has published more than 100 papers in this area
Affiliations and expertise
Professor, Southeast University, ChinaZG
Ziyuan Gu
Dr Gu is is currently with the School of Transportation at Southeast University, China. His research interests include data-driven transportation system analysis, machine learning assisted traffic simulation and optimization, etc. He has published over 40 papers in these areas
Affiliations and expertise
School of Transportation, Southeast University, China