声明
Chapter 1 Introduction
1.1 Background
1.2 Statistics Summary of Crash Contributing Factors
1.3 Problem Statement and Intention of This Thesis
1.4 Research Aim and Objectives
1.5 Outline of the thesis
Chapter 2 Literature Survey
2.1 Introduction
2.2 Status of Road Safety
2.2.1 Status of Road Safety around the World
2.4 Literature Survey of Statistical Models for Crash Injury Severity
2.5 Literature Survey of Machine Learning Models for Crash Injury Severity
2.6 Summarization and Limitations
Chapter 3 Methodology for Crash Modeling
3.1 Introduction
3.2 The Design of Methodology
3.3 Statistical Models
3.3.1 Ordered Probit Regression Model
3.3.2 Checking for Multi-Collinearity
3.3.3 Multinomial Logistic Model (MNLM) Design
3.4 Machine Learning Models
3.4.1 Classification and Adaptive Regression Trees (CART)
3.4.2 Support Vector Machine
3.4.3 Naive Bayes Classifier
3.4.4 K-Nearest Neighbors – Classification
3.4.5 Random Forest
Chapter 4 Crash Data Collection and Data Description
4.1 Introduction
4.1.1 Hong Kong Transportation Department Accident Data
4.1.: Variables Considered In the Study
4.2 Data Preparation
4.2.1 Based on Accident
4.2.2 Based on Vehicle
4.2.3 Based on casualty
4.3 Data Pre-Processing
4.3.1 Missing Data Treatment
4.3.2 Data Normalization
4.4 Estimation of Accuracy in Classification
4.5 Models Selection by Performance Evaluation
Chapter 5 Data Analysis and Modeling Results
5.1 Statistical Models Results
5.2 Machine Learning Models Analysis Results
5.3 Experiments and Results
5.3.1 CART Experimental Results
5.3.2 Support Vector Machine Results
5.3.3 K-Nearest Neighbor Results
5.3.4 Gaussian Na?ve Bayes Results
5.3.5 Random Forest Results
5.4 Results Comparison of Machine Learning Models
5.5 Summary
Chapter 6 Discussion of Findings
6.1 Sensitivity Analysis
6.2 Comparison of Variable Impact on Crash Severity from ML Models
6.3 Summary
Chapter 7 Conclusion and Recommendations
7.1 Conclusion
7.2 Recommendations
参考文献
致谢
List of Figures
List of Tables
List of Acronyms
东南大学;
