基于数据挖掘技术的天气相关因素对道路交通事故影响分析

发布时间：2023-06-03 03:12

　　道路交通安全状态是复杂多因素协同作用的结果,道路交通事故背后的致因可以指导采取不同的措施来降低其危害。该研究评估了道路事故严重程度与天气相关因子如何相关联。天气对高速公路交通安全的影响已成为道路交通安全部门日益关注的问题,如众多与天气有关的碰撞事故发生在潮湿路面和降雨状态。因此,通过统计学和机器学习技术方法探索天气相关变量与道路交通事故严重程度之间的关系非常重要。尽管以前的文献中存在天气对道路交通事故的影响,但需要构筑更加准确的模型,详细分析解释每个天气因素的变化对碰撞事故严重程度的影响。本研究旨在基于公路安全信息系统(来自HSIS)事故数据集来分析和量化天气相关因素(来自国家气候数据中心数据)对道路交通事故严重程度的影响。为了找到更好的模型拟合相关变量,研究选择四个模型:order logit(OL)模型,决策树模型,随机森林模型和神经网络模型,分别在Stata和python中进行建模分析。在模型构建过程中,考虑了与天气条件有关的七个主要因素,分别是气温,平均风速,日降水量,月降水量,年降水量,阵风和相对湿度。本文对比了统计学模型和机器学习模型的建模结果,通过灵敏度分析从机器学习模型...

【文章页数】：76 页

【学位级别】：硕士

【文章目录】：
摘要
Abstract
Chapter1 Introduction
    1.0 Background
    1.1 An Explanation of Contributing Factors to Accidents Injury Severity
    1.2 Problem Statement of the Thesis:
    1.3 Research Aims and Objectives
    1.4 Organization and Summary of Thesis
Chapter2 Literature Review
    2.1 Introduction
        2.1.1 Impact of Weather on Vehicle Condition
        2.1.2 Impact of Weather on Road Condition
        2.1.3 Impact of Weather on Driver Behaviour
        2.1.4 Impact of Weather on Traffic Flow
    2.2 Accident Severity Models
    2.3 Limitations in Literature Review
Chapter3 Methodology
    3.1 Ordered Logistic Regression Model
    3.2 Artificial Neural Network or MLP
        3.2.1 Introduction:
        3.2.2 How ANN Algorithm Works
        3.2.3 Learning parameters
    3.3 Decision Tree
        3.3.1 Introduction
        3.3.2 The Decision Tree Learning Algorithm
        3.3.3 Deciding the“Best Attribute”
            Entropy
            Information Gain
            Gini Index
            Advantages of Decision Tree
            Disadvantages of Decision Tree
    3.4 Random Forest
        3.4.1 Introduction
        3.4.2 Feature Importance
        3.4.3 How does The Random Forests Algorithm work?
            Advantages of Random Forest
            Disadvantages of Random Forest
        3.4.4 Difference between Random Forests and Decision Trees
Chapter4 Data Collection and Preprocessing
    4.1 Data Collection
    4.2 Crash Severity Categories
    4.3 Variables Considered in the Study
    4.4 Data Description
    4.5 Data Preparation and Preprocessing
    4.6 Correlation Analysis
        Metrics for Evaluating Classification Models
Chapter5 Models Results and Analysis
    5.1 Introduction
    5.2 Statistical Analysis
    5.3 Machine Learning Models Results
        5.3.1 MLP Detailed Training Results:
            5.3.1.1 Deep Neural Network Training Results for Occupancy Dataset
            5.3.1.2 Deep Neural Network Results of Training on Vehicle Dataset
            5.3.1.3 Deep Neural Network Results of Training on Accident Dataset
        5.3.2 Random Forest Classifier
            Random Forest Results Summary
        5.3.3 Decision Tree Results Summary
    5.4 Comparison of ML Models Results
Chapter6 Results Discussion
    6.1 Impact of Weather Related Factors on Accident Severity
        6.1.1 Air Temperature
        6.1.2 Average wind speed
        6.1.3 Rainfall
        6.1.4 Humidity
        6.1.5 Wind Gust
    6.2 Summarization
Chapter7 Conclusion and Recommendations
    7.1 Conclusion
    7.2 Recommendations
Acknowledgements
References
List of Figures
List of Tables
List of Synonyms



本文编号：3828549