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研究生:武凱瀚
研究生(外文):Kai-Han Wu
論文名稱:平交道風險因素分析與其應用
論文名稱(外文):Accident Risk Analysis and Applications of Railway Level Crossing
指導教授:胡守任胡守任引用關係
指導教授(外文):Shou-Ren Hu
學位類別:碩士
校院名稱:國立成功大學
系所名稱:交通管理學系碩博士班
學門:運輸服務學門
學類:運輸管理學類
論文種類:學術論文
畢業學年度:96
語文別:英文
論文頁數:111
中文關鍵詞:羅吉斯迴歸零膨脹迴歸模式危險平交道卜瓦松迴歸模式負二項迴歸模式改善方案平交道
外文關鍵詞:Poisson regression modelnegative binominal regression modelrailway level crossingzero-inflated Poisson regression modelcountermeasurelogits modelblack spot
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鐵路平交道是鐵路與公路的交會點,為兩種不同運輸系統所共享;然而在時間與空間上的分享容易產生衝突。根據歷史資料顯示,平交道是鐵路運輸系統中事故發生與生命財產損失最為嚴重的地方之一,因此,改善平交道的安全等級益顯重要。過去在經費與資源的限制下,鐵路平交道相關的改善計畫一直未受到應有的重視,諸如分析影響意外事故發生的潛在因子與改善對策等。為了減少平交道交通事故的頻率與事故發生的嚴重程度,以提升平交道的安全等本研究旨在建立可預測平交道意外事故的相關模型,並對事故發生頻率與衝擊程度進行預測,同時找出影響意外發生之潛在風險因子,以利改善計畫之施行。本研究利用民國87年前台灣省政府交通處所蒐集的平交道普查資料,包括民國84~86年的交通事故資料、平交道種類、道路幾何型態、列車班次數與平均每年每日交通量等曝光量資料。藉由上述平交道相關資料建立交通事故頻率預測模式、衝擊程度模式、風險評估模式,並建立相關績效指標,用以評估不同模式的績效表現。最後並應用平交道風險模式進行危險平交道之預測與相關改善方案之績效評估。
Railway level crossing is the interface of railway and highway facilities where traffic flows are interchanged at a spatial location. In the past, traffic accidents frequently occurred at level crossings and resulted in large property loss and people killed and injury. Due to the budgetary constraint, safety improvement programs focusing on identifying the key factors which might be associated with the occurrence of accidents were not generally considered. In order to reduce property loss and casualties, it is crucial to develop effective evaluation models that are capable of providing effective forecast of accident frequency and severity given a vector of covariates for safety improvement purpose. This research develops a set of statistical count data models for the evaluation of traffic accidents at railway level crossings. The developed models are not only able to evaluate accident frequency and severity but also capable to explore the potential risk factors that are responsible for traffic accidents. In the numerical analysis, the research uses the data set collected by the Ministry of Transportation and Communications (MOTC) in 1998. It consists of both historical accident data and railway level crossing related data, such as crossing type, highway geometric characteristics, daily trains and average annual daily traffic (AADT), etc. Accordingly, this research uses the available data set to establish a set of accident evaluation models, including an accident frequency model, an accident severity model, a risk model, and an accident risk model. Finally, this research has also constructed relevant performance indices to evaluate various model performances, and the preferred models were employed to providing evaluation of black spots and evaluation of countermeasure effects.
CHAPTER 1 INTRODUCTION 1
1.1 Research motivation and background 1
1.2 Research objective 2
1.3 Research scope 2
1.4 Research content and methods 2
1.5 Flow chart 3
CHAPTER 2 LITERATURE REVIEW 6
2.1 Problem description 6
2.2 Safety risk management 10
2.3 Domestic literature review 13
2.4 Foreign literature review 17
2.4.1 Related Literatures of Level Crossing 17
2.4.2 Count data model 18
2.4.3 Zero inflated model 20
2.4.4 Bivariate statistical model 23
2.5 Summary 27
CHPATER 3 METHODOLGY 28
3.1 Count data models 29
3.1.1 Poisson regression model 29
3.1.2 Negative binomial regression model (NB) 31
3.1.3 Zero inflated Poisson regression model (ZIP) 32
3.2 Comparison of Poisson, NB, ZIP model 35
3.2.1 Limitations of Poisson regression model 35
3.2.2 Detection of overdispersion and extra zero events 36
3.3 Categorical data model 38
3.3.1 Logits regression model 38
3.3.2 Multinomial logits model 40
3.3.3 Ordered logistic model 41
3.4 Summary 44
CHAPTER 4 DATA COLLECTION AND ANALYSIS 46
4.1 Data source 46
4.2 Data feature 46
4.2.1 Level crossing accident data 46
4.2.2 Level crossing attribute data 48
4.3 Variable definition and illustration 50
4.3.1 Definition of accident severity and risk 50
4.3.2 Descriptive statistics 52
4.4 Summary 59
CHAPTER 5 EMPIRICAL ANALYSIS AND MODEL EAVALUATION 60
5.1 Accident frequency model 60
5.1.1 Model framework 60
5.1.2 Model setting 60
5.1.3 Variable selection 60
5.1.4 Model analysis 63
5.2 Accident severity model 66
5.2.1 Model structure 66
5.2.2 Model setting 66
5.2.3 Model analysis 66
5.3 Accident risk model 70
5.3.1 Model framework 70
5.3.2 Model setting 70
5.3.3 Variable selection 70
5.3.4 Model analysis 73
5.4 Expected distribution analysis 75
5.5 Model performance evaluation 78
5.6 Summary 81
CHAPTER 6 APPLICATIONS 82
6.1 Black-spots identification 82
6.1.1 Risk evaluation method 82
6.1.2 Risk matrix method 84
6.1.3 Probability-based method 90
6.1.4 Black-spots identification 92
6.2 Countermeasure evaluation 92
CHAPTER 7 CONCLUSION AND RECOMMENDATIONS 95
7.1 Conclusions 95
7.2 Limitation 97
References 99
APPENDIX 1 104
APPENDIX 2 107
APPENDIX 3 109
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