||Aberrant Driving Behavior Analysis and Driving Risk Level Assessment for Inter-city Bus Fleet
||Department of Transportation & Communication Management Science
Aberrant driving behavior
Artificial neural network
Driving risk level
本研究配合個案客運公司調查客運業職業駕駛員的人因特性資料，並透過智慧行車紀錄器取得偏差駕駛行為資料。首先藉由偏差駕駛行為資料建立駕駛風險分級評估機制，將駕駛員依據潛在危險程度區分為高低等級，而後探討人因特性可能導致的偏差駕駛行為及駕駛風險分級結果，透過量化駕駛員的駕駛風險以進行有效的車隊管理。本研究應用人工神經網路(Artificial neural network, ANN)模型建構人因特性與個別偏差駕駛行為和整體駕駛風險分級結果之間的關聯性模型，模型表現出良好的預測準確度，接著透過斯皮爾曼相關得出駕駛疲勞、症狀疾病及高神經質是造成駕駛風險的主要因素，充足的睡眠、高和善性及高年家戶所得則是減少駕駛風險的主要因素。針對研究結果提出管理建議，期望能在事故前端的角度來防範交通事故的發生，進一步提升企業的安全誠信與聲譽。
Due to the growing trend of public transportation, inter-city bus (highway bus) as ground transport plays an essential role in Taiwan. However, the traffic accidents involving with bus usually accompany serious casualties and financial loss. The fatality rate of bus are 10 times higher than sedans in recent years, and it shows urgency of bus safety in Taiwan. Thus, driving behavior is gradually valued by inter-bus carriers. The bus driver is charged with serious responsibility, and his driving behavior is influenced to a significant degree by his own human factors.
This study will collect information of inter-city bus driver human factors and the status of aberrant driving behavior from the case study company. A relative risk level evaluation mechanism will be developed based on the frequency and distribution of aberrant driving behavior. The research aims to enhance efficiency of the fleet management system and highway safety in general through quantifying relative driving risk of each driver. We apply artificial neural network (ANN) models to predict the frequency of aberrant driving behavior and the risk level of each driver by individual human factors. The predictive models perform high accuracy in case. Spearman correlation coefficient was used to calculate the correlation between the human factors and driving risk. Driving fatigue, symptom, disease and high neuroticism would cause high driving risk; Enough sleep hours, high agreeableness and high annual household income lead to low driving risk. By establishing a systematic driving risk assessment mechanism, inter-city bus industry can reduce the occasion of traffic accidents and further raise corporate integrity and reputation.
TABLE OF CONTENTS iv
LIST OF TABLES vii
LIST OF FIGURES ix
CHAPTER 1 INTRODUCTION 1
1.1 Research background and motivation 1
1.2 Research objectives 3
1.3 Research flow chart 5
CHAPTER 2 LITERATURE REVIEW 8
2.1 Human Factors 8
2.1.1 Ergonomics 9
2.1.2 Personality 11
2.1.3 Human factors affecting driving performance 13
2.2 Driving Behavior 15
2.2.1 Aberrant driving behavior 16
2.2.2 Driving risk 19
2.2.3 Studies of driving behavior 20
2.3 Performance 23
2.3.1 Job performance 23
2.3.2 Driving performance 24
2.3.3 Studies of driving performance 27
2.4 Driver Grading System 28
2.4.1 Grading system in transportation 28
2.4.2 Studies of driver grading system 29
2.5 Summary 31
CHAPTER 3 RESEARCH METHODOLOGY 34
3.1 Research Structure 34
3.2 Research Variable and Questionnaire Design 37
3.2.1 Driver’s human factors 38
3.2.2 Aberrant driving behavior 48
3.3 Research Methodology 50
3.3.1 Literature review and organization 50
3.3.2 Data collection 50
3.3.3 Descriptive statistical analysis 51
3.3.4 Correlation analysis 51
3.3.5 Box-and-whisker plot 51
3.3.6 Jenks natural breaks optimization 52
3.3.7 Elbow method 54
3.3.8 Artificial Neural Network (ANN) Model 54
3.3.9 Performance assessment 63
CHAPTER 4 EMPIRICAL EXPERIMENT 65
4.1 Team Research Overview 65
4.2 Data Description 67
4.2.1 Description of human factor 68
4.2.2 Description of driving behavior 72
4.2.3 Correlation analysis 73
4.2.4 Adjustment 76
4.3 Driving Risk Conversion 78
4.3.1 Features of driving behavior 78
4.3.2 Single driving risk 80
4.3.3 Converting process 80
4.4 Constructing the Overall Driving Risk Level 87
4.4.1 Overall driving risk 87
4.4.2 Driving risk level 88
4.5 Network Model for Predicting Driving Risk 90
4.5.1 Pre-processing and clarification of Model 90
4.5.2 Single behavior 94
4.5.3 Overall driving risk 95
4.5.4 Sensitivity analysis 97
4.6 Summary 100
CHAPTER 5 CONCLUSIONS AND SUGGESTIONS 102
5.1 Conclusions 102
5.2 Suggestions 107
Appendix A Sample Distribution of Quantitative Human Factors 120
Appendix B Result of Random ANN Models 124
Appendix C Human Factor Questionnaire in Chinese 130
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