基于机器学习的加纳摩托车碰撞事故严重性分析

发布时间：2020-12-28 06:53

　　在加纳,摩托车注册数量差不多占机动车注册数量的四分之一。在加纳北部农村地区,骑摩托车已成为一种常见又便宜的出行方式。近年来,作为拥堵道路下经济可行的交通模式,摩托车在加纳的城市中也越来越流行。摩托车碰撞事故通常发生在共用道路上,而与其相关的伤害与死亡是道路交通安全的重要问题,在近些年显得尤为突出。目前,摩托车碰撞事故在加纳的行人死亡原因中排名第二位。因此,有必要对导致摩托车碰撞事故的因素进行研究。摩托车碰撞事故分析在全球范围内是一个重要的研究领域。而在加纳,还没有关于摩托车碰撞事故严重性及其影响因素的研究。目前,关于预测摩托车碰撞事故严重性的经典统计模型,相关文献较多。传统的统计模型有基本的假设和预定义关系,但如果它们不满足条件,将产生不准确的结果。鉴于统计模型的缺点,本文采用基于机器学习的算法来预测摩托车碰撞事故严重性。机器学习技术采用非参数模型,其没有预测变量和响应变量之间的关系推定。本文对不同的机器学习算法进行比较和评价。本文研究的事故数据来自加纳建筑与道路研究院(BRRI)的国家道路交通碰撞数据库中2011至2015年间的摩托车碰撞数据。该数据被划分为4种损伤严重性类型:致命,... 

【文章来源】：江苏大学江苏省

【文章页数】：140 页

【学位级别】：博士

【文章目录】：
摘要
ABSTRACT
1 INTRODUCTION
    1.1 Background
    1.2 Problem Statement
    1.3 Research Objectives and Scope
        1.3.1 Research Objective
        1.3.2 Scope of the Research
    1.4 Significance of the Study
    1.5 Structure of the Dissertation
2 REVIEW OF FACTORS AFFECTING MOTORCYCLE CRASH SEVERITY AND MOTORCYCLE CRASH SEVERITY ANALYSIS METHODS
    2.1 Introduction
    2.2 Contributing Factors Responsible to Motorcycle Crash Severity
        2.2.1 Motorcyclists’Characteristics
        2.2.2 Roadway Features and Roadside Fittings
        2.2.3 Crash Characteristics
        2.2.4 Temporal Characteristics
        2.2.5 Environmental Conditions
    2.3 Crash Severity Studies
        2.3.1 Characteristics of crash-injury severity data
        2.3.2 Traditional Statistical Techniques for Motorcycle Crash Severity Analysis
        2.3.3 Machine Learning Techniques for Motorcycle Crash Severity Analysis
    2.4 Summary
3 PROPOSED RESEARCH METHODS
    3.1 Introduction
    3.2 Classification Methods
        3.2.1 Neural Networks
        3.2.2 Rule-based classification
        3.2.3 Classification and Regression Trees
        3.2.4 J48 decision tree classifier
        3.2.5 Instance-Based learning with parameter k
    3.3 Ensemble methods
        3.3.1 Introduction
        3.3.2 AdaBoost
        3.3.3 Bagging
        3.3.4 Random Forest
        3.3.5 Majority Vote Combiner
    3.4 Modeling Tools
    3.5 Validation of the models
    3.6 Performance metrics
    3.7 Quantifying the Contributing Factors of Motorcyclist Injury Severity
    3.8 Summary
4 PREPARATION AND UNDERSTANDING OF DATA
    4.1 Introduction
    4.2 Data Collection
    4.3 Data Processing
    4.4 Description of the Data
    4.5 Summary
5 CONFIGURATION OF MACHINE LEARNING MODELS FOR PREDICTION OF MOTORCYCLE CRASH SEVERITY
    5.1 Introduction
    5.2 Loading the data into the WEKA
    5.3 Classifiers
        5.3.1 Multilayer Perceptron
        5.3.2 PART:Rule-based classifier
        5.3.3 Classification and Regression Trees
        5.3.4 J48 decision tree classifier
        5.3.5 Instance-Based learning with parameter k
    5.4 Improving Results with Construction of Ensembles
    5.5 Quantifying the Contributing Factors of Motorcyclist Injury Severity
    5.6 Summary
6 RESULTS AND COMPARATIVE ANALYSIS OF DEVELOPED CLASSIFIERS
    6.1 Introduction
    6.2 Comparing Results of Ensembles and Individual Classifiers
        6.2.1 Individual Classifiers
        6.2.2 Classifier Ensemble
    6.3 Quantifying the Contributing Factors of Motorcyclist Injury Severity
7 CONCLUSIONS AND FUTURE RESEARCH DIRECTIONS
    7.1 Introduction
    7.2 Conclusions
    7.3 Future Research Directions
REFERENCE
ACKNOWLEDGEMENTS
PUBLICATIONS
Appendix A Summary of Output from the Classifiers
    Appendix A.1 MLP Classifier
    Appendix A.2 PART Classifier
    Appendix A.3 CART Classifier
    Appendix A.4 J48 Classifier
    Appendix A.5 IBk Classifier
Appendix B Summary of Output from the Ensembles
    Appendix B.1 AdaBoost
        Appendix B1.1 BoostingMLP
        Appendix B1.2 BoostingPART
        Appendix B1.3 BoostingCART
        Appendix B1.4 BoostingJ
        Appendix B1.5 BoostingIBk
    Appendix B.2 Bagging
        Appendix B2.1 BaggingMLP
        Appendix B2.2 BaggingPART
        Appendix B2.3 BaggingCART
        Appendix B2.4 BaggingJ
        Appendix B2.5 BaggingIBk
    Appendix B.3 Random Forest
    Appendix B.4 Majority Vote Combiner
Appendix C Summary of Output from the Evaluator



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