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系統識別號 U0026-0907201621124200
論文名稱(中文) 鐵路路線入侵事故分析-以臺鐵為例
論文名稱(英文) Analysis of Rail Line Trespassing Accidents- A Case Study of the TRA System
校院名稱 成功大學
系所名稱(中) 交通管理科學系
系所名稱(英) Department of Transportation & Communication Management Science
學年度 104
學期 2
出版年 105
研究生(中文) 黃品芸
研究生(英文) Pin-Yun Huang
學號 R56031143
學位類別 碩士
語文別 英文
論文頁數 78頁
口試委員 指導教授-胡守任
口試委員-王中允
口試委員-邱裕鈞
口試委員-褚志鵬
口試委員-鄭永祥
中文關鍵字 鐵路路線入侵事故  計數資料迴歸模式  TOBIT迴歸模式  地理加權迴歸模式 
英文關鍵字 Rail line trespassing accident  Count data regression model  TOBIT regression model  Geographically weighted regression model 
學科別分類
中文摘要 軌道運輸具有專用路權、運量大等特性,因此軌道運輸系統近年來受到各國的廣泛重視與推廣。根據交通部臺灣鐵路管理局(以下簡稱:臺鐵局)民國103年的旅客運量人數統計,臺鐵局年運量高達2億3282萬6496人次,且日平均運量達63萬7881人次,較民國102年增加了4.99%,說明了臺鐵局在城際運輸中扮演相當重要之角色。相較於公路系統,臺鐵局的事故發生頻率相對較低;然而,倘若發生事故,往往造成重大的生命財產損害,甚至衍伸許多有形與無形的外部成本,例如:列車班次延誤、影響列車駕駛的身心狀況、因事故衍伸的醫療復健及維修費用等。有關鐵路事故之相關研究,過去大部分著重於平交道事故之研究,鮮少提及鐵路正線上之路線入侵案件。目前臺鐵局總營業里程為1061.3公里(含支線91公里),僅剩466座不同管制方式的平交道,而在全長一千餘公里的營業里程範圍內,針對入侵鐵路正線所造成的事故風險與衝擊程度,更不容小覷。 針對上述研究課題,本研究擬針對臺鐵局最近十年(2005-2014)鐵路路線入侵事故統計資料進行兩階段的探討,第一階段將以對應的統計模式分別探討臺鐵局鐵路路線入侵事故之發生頻次與事故嚴重程度。事故發生頻次模式包括:卜瓦松迴歸模式、負二項迴歸模式、TOBIT迴歸模式;第二階段則納入地區特性,並以地理加權迴歸模式探討影響各縣市發生鐵路路線入侵事故案件之因果關係。透過兩階段的實證分析結果顯示,區域型模式具有較佳的配適能力,同時可以反應地區異質特性對於事故發生頻次的影響,因此可以提供臺鐵局與鐵路主管機關根據地區差異擬定不同的事故防範策略。
英文摘要 Rail transport, characterized by its exclusive right of way and high service capacity, has received increasing attention in the international transport community. According to the statistics of Taiwan Railways Administration (TRA), the TRA served approximately 232.8 million passengers in 2014 with an average daily volume of 637,881 passengers, an increase of 4.99% compared to that of 2013. The constant increase of passenger trips has indicated that TRA plays a crucial role for intercity transport services in Taiwan. In terms of traffic accidents for the TRA system, it is relatively few compared to that of the highway system. Nevertheless, in case of a rail accident, it usually causes significant number of casualties and different degrees of impact, even induces some external costs (such as train delay, locomotive engineer’s psychological impact, and injury recovery and facility maintenance expenses, etc.). Past studies concerning rail accidents mainly focused on highway-rail grade crossing accidents. Investigation of rail trespassing incidents is relatively rare. Currently, the operation mileage of the TRA system is 1061.3 kilometers including a total of 91 kilometers of the branch lines. Along the main and branch lines of the TRA system, there are only 466 grade crossings with different control means (mainly active control by quadrant-type gate). Compared to those traffic accidents occurred on grade crossings, the accident frequency and potential impacts of rail line trespassing incidents along the entire service scope of the TRA system should not be ignored, and this topic is deserved in-depth investigation.
To resolve the above research question, this study aims to investigate the casual relationship between rail trespassing incidents and a set of covariates using a most recent ten-year (2005-2014) railway line incident dataset provided by the TRA by a two-stage research approach. In the first stage of this study, rail trespassing accident frequency data are modeled by Poisson regression, Negative binomial regression, TOBIT regression models. In the second stage, local traffic and environmental characteristics are incorporated into the modeling process. Geographically weighted regression model is developed to further evaluate the effects of local characteristics on the occurrence of a rail trespassing incident. According to the empirical results of this study, the local model has a better goodness of fit and is able to explore the heterogeneity in different regions. Thereby, the empirical study results of the local models are expected to provide the TRA and related government offices with a reference in preparing rail line trespassing incident prevention strategies.
論文目次 摘要 I
Abstract II
TABLE OF CONTENTS V
LIST OF FIGURES VII
LIST OF TABLES VIII
Chapter 1 Introduction 1
Chapter 2 Background and Literature Review 9
2.1 Rail Trespassing Incidents 9
2.2 Statistical Models for Rail Incidents and/or accidents 12
2.2.1 Count Data Regression Model 13
2.2.2 Tobit Regression Model (TRM) 13
2.2.3 Geographically Weighted Regression Model (GWR) 14
2.3 Summary 14
Chapter 3 Methodology 16
3.1 Count Data Regression Model 18
3.1.1 Poisson Regression Model (PRM) 18
3.1.2 Negative Binomial Regression Model (NBRM) 20
3.2 Tobit Regression Model (TRM) 24
3.3 Geographically Weighted Regression (GWR) 26
3.3.1 Weighted functions 28
3.3.2 The bandwidth calculation 29
3.3.3 Fixed Kernel or Adaptive Kernel 30
3.4 Summary 31
Chapter 4 Empirical Study 33
4.1 Data collection and description 33
4.2 Descriptive Statistics 37
4.3 Results and Discussion 41
4.4 Global Model 41
4.5 Local Model 45
4.6 Discussion 47
Chapter 5 Conclusion and Recommendation 57
5.1 Findings and conclusions 57
5.2 Future research and recommendations 58
References 59
Appendices 67
Appendix A: Descriptive Statistics for Trespassing Variables 67
Appendix B: Original data of rail line trespassing accidents provided by TRA 71
Appendix C: Rearranged data of rail line trespassing accidents prepared by this study 77
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