Development of a Quantitative Safety Risk Assessment Model for Rail Safety Management System

Transcription

1 Development of a Quantitative Safety Risk Assessment Model for Rail Safety Management System Its Application towards Assessing and Prioritising Safety Risks at Interfaces of Railway and Highway by RAJALINGAM RAJAYOGAN M.Sc., University of South Bank, London (1994) B.Eng. (Mech), University of Peradeniya, Sri Lanka (1982) A Thesis Submitted in Partial Fulfilment of the Requirements for the Degree of Doctor of Philosophy School of Business University of Western Sydney Australia February 2012

2 Dedication I dedicate this thesis to my almighty God Lord Ganesha and to my beloved Bhagawan Sri Sathiya Sai Baba, who gave me love and huge blessings to initiate and to successfully complete my doctoral studies. (Aum Ganeshaya Namaha & Aum Sai Ram) ii

3 Declaration of Originality This is to certify that the research work reported in this thesis is, to the best of my knowledge and belief, original and has not been submitted to any other University or Institution for the award of a higher degree. Rajalingam Rajayogan iii

4 Acknowledgment During my doctoral studies at the University of Western Sydney, I have had a great opportunity to work on my favourite topic which is directly related to the nature of my current employment at RailCorp-NSW. Through this study I have gained very interesting and valuable experiences while meeting many local and international people, colleagues and friends who helped me with my research along the way, either directly or indirectly. I therefore take this opportunity to express my sincere appreciation and gratitude to them. First and foremost, I am deeply grateful to my principal supervisor Dr Premaratne Samaranayake for his gentle encouragement and guidance, and my co-supervisor Dr Kenan Matawie for his assistance in the statistical analysis of this study. I would like to thank my former supervisors Dr K Ramanathan, Dr V Jayaraman and Dr R Agrawal. I also thank my friends Dr P Jayakumar and Dr D Jeyaraman who initiated the wonderful idea about PhD studies in my mind. I express my sincere thanks to Dr Siriyani Dias, Ms Maria Lozano and Mr Tony Davies from my previous employment at WorkCover-NSW, who assisted me in learning the statistical analysis system in order to conduct statistical research work. I also thank Mr Ian Cooke for helping me in the initial preparation of data. It is also necessary to thank my current employer (RailCorp-NSW) and my work supervisors Mr Matthew Coates and Ms Mandie Thomas for allowing me to take considerable time off from my work to undertake this study at the University. I offer my sincere salutes to my beloved leader late Dr M G Ramachandran (MGR) who gave me courage and self confidence indirectly. My sincere appreciations go to Prof. E. Ambikairajah and my family friends (including the members of our music group Sydney Geetha Saagara ) who provided assistance in various ways. Finally, I would like to especially thank my wife Naguleswary (Rahini), my daughter Sujanthini and my son Shayanthan who offered me love, patience and moral support during my studies. iv

5 Table of Contents PAGE Dedication... ii Declaration of Originality... iii Acknowledgement... iv Table of Contents... v List of Tables... x List of Figures... xiii Abbreviations... xv Nomenclature... xvi Abstract... xvii Chapter 1: Introduction to the Research Introduction Overview of Risk and Occupational Health & Safety Safety Management System (SMS) Background of Rail Safety Risk Potentials and Rail SMS Significance of the Study Objectives of the Study Target of the Study Benefits of the Study Limitations of the Study Structure of the Thesis Chapter 2: Literature Review Introduction Definitions of Terms Used in Relations to Safety Accidents Hazards Risks Safety Safety Management System (SMS) at Organisational Level The Four P Principles of Safety Management System Safety Culture Organisational Involvement in SMS Comparison of Current SMS with Traditional Approach Major Modules of SMS Initiatives to Build an SMS Employer s Responsibilities Leadership Skills Communicating Safety Critical Information Elements of a Safe Working Environment Measurements on Effectiveness of SMS Risk Management within SMS Major Processes in Risk Management Hazard Identification Dividing Hazard Identification into Manageable Portions Developing an Inventory of Tasks Analysing Tasks v

6 Identifying the Hazards Involved Considering the People Factor Aiding Hazard Identification Hazard Identification as Ongoing Process Recording Hazard Identification Data Undertaking Risk Assessment Risk Assessment Risk Assessment Matrix Recording Results of Risk Assessment Considering Current Controls Setting Times Limits for Action Risk Control or Elimination Risk Control Involvement Hierarchy of Controls Sequence of Risk Control Undertaking Monitoring and Review Safety Risk Potentials in Railways Priority Issues Identified in Rail Safety System Based Safety Issues People Based Safety Issues Challenges of Safety Faced in Rail Sector Rail Safety Management System Rail SMS as a Central System to All Rail Operations Development and Management of Rail Safety Key Components of SMS Managed by Rail Sectors External Bodies Assisting in Rail Safety Need for Measuring Rail Safety Risk Assessment in Rail SMS Major Safety Issues at Railway-Highway Interfaces Statistical Overview of Global Level Crossing Collisions and Consequences Global Comparison of Level Crossing Accidents Level Crossing Collisions Level Crossing Fatalities Background of Research Problem Significance of Safety Improvement at Level Crossings Previous Research on Risk Assessment at Grade Crossings The Need for Improving Railway Grade Crossings Safety Summary Chapter 3: Research Methodology Introduction Fundamental Concepts on Safety Risks Evaluation Identification of General Risks Evaluation of Risks Analysis of Risk Types of Risk Analysis Methods Qualitative Risk Analysis Quantitative Risk Analysis Semi-Quantitative Risk Analysis Developing Theoretical Framework on Safety Risk Evaluation at Rail Grade Crossings Development of a Quantitative Risk Assessment Model for Safety Evaluation at Rail Crossings - Safety Risk Index (SRI) Basic Concepts Used in Developing SRI Numerical Example on Application of SRI Major Steps to Achieve Objectives of Study Grade Crossing Accidents Data for Analysis Major Factors Influencing Accident Risks at Grade Crossings Crossings Characteristics Railway Characteristics vi

7 Highway Characteristics Vehicle Attributes Driver Attributes Environmental Attributes Overview of Current Statistical Models for Predicting Accidents and Consequences at Grade Crossings Review on Common Models Predicting Highway Accidents Poisson Regression Models Negative Binomial Regression Models Gamma Models Zero-Inflated Poisson Models Empirical Bayesian Model Overview on Existing Models Developed for Prediction of Collisions at Railway-Highway Interfaces Relative Risk Models Absolute Risk Models Overview of Existing Models in Predicting Consequences of Collisions at Railway- Highway Interfaces USDOT Consequence Model (1987) Canada - University of Waterloo Consequence Model (2003) Overview of Existing Models in Predicting Overall Safety Risks at Railway-Highway Interfaces Evaluation of Risk at Grade Crossings with Application of Safety Risk Index (SRI) Identifying Worst Accident Crossing Locations (Black-Spots) Developing an Improved Quantitative Method for Black-Spots Identification with Application of SRI Summary Chapter 4: Data Collection and Consolidation Introduction Source of Rail Crossing Accidents Data and Information Database of Railway-Highway Crossings Data and Information (Inventory Database) Attributes and Variables in Inventory Database Selection of Appropriate Variables from Inventory Database Extraction of Public Grade Crossings from Inventory Database Database of Railway-Highway Crossing Accidents Information (Occurrence Database) Attributes and Variables in Occurrence Database Selection of Appropriate Variables for Developing Models Selection of Appropriate Records from Occurrence Database Consolidated Database by Combining Inventory and Occurrence Databases Preliminary Data Analysis on Rail Crossings Accidents Accidents at All Railway-Highway Crossings Annual Accident Rates for All Rail Crossings Relations to Travel Annual Accident Frequency Rates for Rail Crossings Reasons for Research Focus on Public Grade Crossings Accidents and Consequences at Public Grade Crossings Reasons for Grouping Public Grade Crossings by Protection Types for Model Development Inventory Data on Public Grade Crossings by Protection Type Statistics of Accident Frequency and Consequence for Public Grade Crossings by Protection Type ( ) Statistics of Variables Used in Models by Protection Types Summary Chapter 5: Development and Validation of Grade Crossing Accidents and Consequences Prediction Models Introduction Overview of Current Safety Risk Assessment Models vii

8 5.2 Common Models of Accident Frequency Prediction Poisson Models Poisson Distribution Zero-Inflated Poisson Distribution Multiplicative Poisson Regression Distribution Negative Binomial Regression Model Empirical Bayesian (EB) Model Common Models of Accidental Consequences Prediction Consequence Model by US Department of Transportation Consequence Model by Canada Transport Development Centre Major Steps in the Process of Model Development Functional Form of Model Model Distribution Structure Poisson Distribution Gamma Distribution Negative Binomial Distribution Empirical Bayesian Selection of Explanatory Variables for a Best-Fit Model Procedures for Selecting Appropriate Variables for a Model Assessment of Final Model for Goodness-of-Fit Procedures for Selecting Final Model Step-1: Developing a GLM Poisson Regression Model Step-2: Developing a GLM Negative Binomial Regression Model Step-3: Selection of Appropriate Model - Poisson or Negative Binomial Step-4: Utilising Empirical Bayesian Models Results on Models Developed for Each Protection Type Generating Models Predicting Accident Frequencies Crossing Protection Type 1 (No Signs or No signals) Crossing Protection Type 2 (Stop Signs or Cross-bucks) Crossing Protection Type 3 (Signals, Bells or Warning Devices) Crossing Protection Type 4 (Gates or Full Barrier) Generating Models Predicting Accidental Consequences Crossing Protection Type 1 (No Signs or No signals) Crossing Protection Type 2 (Stop Signs or Cross-bucks) Crossing Protection Type 3 (Signals, Bells or Warning Devices) Crossing Protection Type 4 (Gates or Full Barrier) Summary Chapter 6: Development of Safety Risk Index (SRI) for Risks Assessment at Grade Crossings Introduction Development of Safety Risk Index (SRI) Model Defining Safety Risk Index (SRI) Identifying Safety Status of a Crossing using Graphical Method Identifying Black-Spots (Crossings with Unacceptable Higher Safety Risk Index Values) Introducing Threshold Curves of Safety Risk Index Selecting Safety Risk Index Threshold Value Identifying Black-Spots in Each Protection Type Crossing Protection Type 1 (No Signs or No signals) Crossing Protection Type 2 (Stop Signs or Cross-bucks) Crossing Protection Type 3 (Signals, Bells or Warning Devices) Crossing Protection Type 4 (Gates or Full Barrier) List of All Black-Spots Identified in the Study Validation of Safety Risk Index (SRI) Model Analysis of Black-spot Cluster Regions (All Protection Types) Summary Chapter 7: Impact Analysis on Risk Assessment Models Introduction viii

9 7.1 Impact (Sensitivity) Analysis Examining Models Predicting Collisions Effects of Highway Characteristics on Four Protection Types Effects of Railway Characteristics on Four Protection Types Effects of Upgrading Protection Types on Collisions Related to Highway Characteristics Effects of Upgrading Protection Types on Collisions Related to Railway Characteristics Examining Models Predicting Consequences Effects of Highway Characteristics on Four Protection Types Effects of Railway Characteristics on Four Protection Types Examining Models Predicting Safety Risk Index (SRI) Effects of Highway Characteristics on Four Protection Types Effects of Railway Characteristics on Four Protection Types Summary Chapter 8: Conclusions and Recommendations Introduction Overview of Research Findings Accident Frequency Prediction Model Accident Consequences Prediction Model Estimation of Safety Risk Index (SRI) Black-Spots Identified Using Safety Risk Index Contributions of Research Study Benefits of Research Study Research Limitations Recommendations Future Research References Bibliography Appendix 1: All Variables in USDOT FRA Databases Appendix 2: Graphical Distribution of Variables Used Appendix 3: Descriptive Statistics on Model Variables Appendix 4: List of Publications ix

10 List of Tables PAGE Chapter 2: Table 2.1: Typical Measure of Risk Exposures 49 Table 2.2: Typical Measure of Likelihood 49 Table 2.3: Typical Measure of Consequences 50 Table 2.4: A Typical Risks Assessment Matrix 50 Table 2.5: Level Crossing Accident Statistics in Selected European Countries 87 Chapter 3: Table 3.1: Group Selection for Estimating Probability of an Event 100 Table 3.2: Group Selection for Measuring Consequences of an Event 100 Table 3.3: A Typical Qualitative Risks Ranking Matrix 101 Table 3.4: Probability of Deaths by Causes 102 Table 3.5: USDOT Accident Prediction Equations for Category by Characteristic Factors 130 Table 3.6: Coefficients of Coleman-Stewart Model 131 Chapter 4: Table 4.1: Filtering Process in Selecting Appropriate Variables from Inventory Database 146 Table 4.2: Number of All Crossings by Type Vs Position ( ) 147 Table 4.3: Filtering Process in Selecting Appropriate Variables from Accident Database 152 Table 4.4: All Level Crossing Accidents and Casualties ( ) 156 Table 4.5: Number of All Level Crossing Accidents and Accident Rates ( ) 158 Table 4.6: Accident Frequency Rates by Type of Crossings 160 Table 4.7: Accident Frequency Rates by Type by Position of Crossings 161 Table 4.8: Public Grade Crossing Accident Casualties ( ) 163 Table 4.9: Public Grade Crossings Data by Protection Type ( ) 165 Table 4.10: Accidents Data of Public Grade Crossings by Protection Type ( ) 165 Table 4.11: Consequences Data of Public Grade Crossings by Protection Type ( ) 166 Chapter 5: Table 5.1: Comparison of Accidental Crossings Predicted by Poisson Model to History 173 Table 5.2: Over-Dispersion Test Values on Number of Accidents by Crossing Types 178 Table 5.3: Comparison of Observed and Predicted Values for Accidental Crossings by ZIP 184 Table 5.4: Descriptive Statistics on Variables Used in the Model - Protection Type Table 5.5: Pearson Correlation Between Variables Used in the Model - Protection Type Table 5.6: Parameter Estimates of GLM Poisson Regression Model - Protection Type Table 5.7: Goodness-of-Fit Result of GLM Poisson Regression Model - Protection Type Table 5.8: Parameter Estimates in GLM Negative Binomial Model - Protection Type Table 5.9: Goodness-of-Fit Result of GLM Negative Binomial Model - Protection Type Table 5.10: Over-Dispersion Parameter and Weighting Factors Estimation - EB Model 214 Table 5.11: Goodness-of-Fit Result of NB & EB Models - Max Timetable Train Speed 215 Table 5.12: Goodness-of-Fit Result of NB & EB Models - AADT 216 Table 5.13: Top Ten Accidental Locations by EB Model Prediction in Protection Type Table 5.14: Descriptive Statistics on Variables Used in the Model - Protection Type Table 5.15: Pearson Correlation Between Variables Used in the Model - Protection Type Table 5.16: Parameter Estimates in GLM Poisson Regression Model - Protection Type Table 5.17: Goodness-of-Fit Result of GLM Poisson Regression Model - Protection Type Table 5.18: Parameter Estimates in GLM Negative Binomial Model - Protection Type Table 5.19: Goodness-of-Fit Result of GLM Negative Binomial Model - Protection Type Table 5.20: Over-Dispersion Parameter and Weighting Factors Estimation - EB Model 222 Table 5.21: Goodness-of-Fit Result of NB & EB Models - Timetable Train Speed 223 Table 5.22: Goodness-of-Fit Result of NB & EB Models - Highway Speed 223 Table 5.23: Goodness-of-Fit Result of NB & EB Models - Number of Traffic Lanes 224 Table 5.24: Goodness-of-Fit Result of NB & EB Models - Daily Train Movement 224 Table 5.25: Goodness-of-Fit Result of NB & EB Models - AADT 224 Table 5.26: Top Ten Accidental Locations by EB Model Prediction in Protection Type x

11 Table 5.27: Descriptive Statistics on Variables Used in the Model - Protection Type Table 5.28: Pearson Correlation Between Variables Used in the Model - Protection Type Table 5.29: Parameter Estimates in GLM Poisson Regression Model - Protection Type Table 5.30: Goodness-of-Fit Result of GLM Poisson Regression Model - Protection Type Table 5.31: Parameter Estimates in GLM Negative Binomial Model - Protection Type Table 5.32: Goodness-of-Fit Result of GLM Negative Binomial Model - Protection Type Table 5.33: Over-Dispersion Parameter and Weighting Factors Estimation - EB Model 230 Table 5.34: Goodness-of-Fit Result of NB & EB Models - Max Timetable Train Speed 232 Table 5.35: Goodness-of-Fit Result of NB & EB Models - Highway Speed 232 Table 5.36: Goodness-of-Fit Result of NB & EB Models - Number of Traffic Lanes 232 Table 5.37: Goodness-of-Fit Result of NB & EB Models - Daily Train Movement 233 Table 5.38: Goodness-of-Fit Result of NB & EB Models - AADT 233 Table 5.39: Top Ten Accidental Locations by EB Model Prediction in Protection Type Table 5.40: Descriptive Statistics on Variables Used in the Model - Protection Type Table 5.41: Pearson Correlation Between Variables Used in the Model - Protection Type Table 5.42: Parameter Estimates in GLM Poisson Regression Model - Protection Type Table 5.43: Goodness-of-Fit Result of GLM Poisson Regression Model - Protection Type Table 5.44: Parameter Estimates in GLM Negative Binomial Model - Protection Type Table 5.45: Goodness-of-Fit Result of GLM Negative Binomial Model - Protection Type Table 5.46: Over-Dispersion Parameter and Weighting Factors Estimation - EB Model 239 Table 5.47: Goodness-of-Fit Result of NB & EB Models - Number of Main Tracks 240 Table 5.48: Goodness-of-Fit Result of NB & EB Models - Number of Traffic Lanes 240 Table 5.49: Goodness-of-Fit Result of NB & EB Models - Daily Train Movement 241 Table 5.50: Goodness-of-Fit Result of NB & EB Models - AADT 241 Table 5.51: Top Ten Accidental Locations by EB Model Prediction in Protection Type Table 5.52: Equivalent Fatality Score comparison for various accident consequences 243 Table 5.53: Descriptive Statistics on Variables Used in the Model - Protection Type Table 5.54: Pearson Correlation Between Variables Used in the Model - Protection Type Table 5.55: Parameter Estimates in GLM Poisson Regression Model - Protection Type Table 5.56: Goodness-of-Fit Result of GLM Poisson Regression Model - Protection Type Table 5.57: Parameter Estimates in GLM Negative Binomial Model - Protection Type Table 5.58: Goodness-of-Fit Result of GLM Negative Binomial Model - Protection Type Table 5.59: Over-Dispersion Parameter and Weighting Factors Estimation - EB Model 249 Table 5.60: Goodness-of-Fit Result of NB & EB Models - Max Timetable Train Speed 251 Table 5.61: Goodness-of-Fit Result of NB & EB Models - Total Occupants in Vehicle 251 Table 5.62: Top Ten Locations by Consequence Predicted with EB in Protection Type Table 5.63: Descriptive Statistics on Variables Used in the Model - Protection Type Table 5.64: Pearson Correlation Between Variables Used in the Model - Protection Type Table 5.65: Parameter Estimates in GLM Poisson Regression Model - Protection Type Table 5.66: Goodness-of-Fit Result of GLM Poisson Regression Model - Protection Type Table 5.67: Parameter Estimates in GLM Negative Binomial Model - Protection Type Table 5.68: Goodness-of-Fit Result of GLM Negative Binomial Model - Protection Type Table 5.69: Over-Dispersion Parameter and Weighting Factors Estimation - EB Model 257 Table 5.70: Goodness-of-Fit Result of NB & EB Models - Max Timetable Train Speed 259 Table 5.71: Goodness-of-Fit Result of NB & EB Models - Total Occupants in Vehicle 259 Table 5.72: Top Ten Locations by Consequence Predicted with EB in Protection Type Table 5.73: Descriptive Statistics on Variables Used in the Model - Protection Type Table 5.74: Pearson Correlation Between Variables Used in the Model - Protection Type Table 5.75: Parameter Estimates in GLM Poisson Regression Model - Protection Type Table 5.76: Goodness-of-Fit Result of GLM Poisson Regression Model - Protection Type Table 5.77: Parameter Estimates in GLM Negative Binomial Model - Protection Type Table 5.78: Goodness-of-Fit Result of GLM Negative Binomial Model - Protection Type Table 5.79: Over-Dispersion Parameter and Weighting Factors Estimation - EB Model 264 Table 5.80: Goodness-of-Fit Result of NB & EB Models - Max Timetable Train Speed 266 Table 5.81: Goodness-of-Fit Result of NB & EB Models - Total Occupants in Vehicle 266 Table 5.82: Top Ten Locations by Consequence Predicted with EB in Protection Type Table 5.83: Descriptive Statistics on Variables Used in the Model - Protection Type Table 5.84: Pearson Correlation Between Variables Used in the Model - Protection Type Table 5.85: Parameter Estimates in GLM Poisson Regression Model - Protection Type Table 5.86: Goodness-of-Fit Result of GLM Poisson Regression Model - Protection Type xi

12 Table 5.87: Parameter Estimates in GLM Negative Binomial Model - Protection Type Table 5.88: Goodness-of-Fit Result of GLM Negative Binomial Model - Protection Type Table 5.89: Over-Dispersion Parameter and Weighting Factors Estimation - EB Model 271 Table 5.90: Goodness-of-Fit Result of NB & EB Models - Max Timetable Train Speed 273 Table 5.91: Goodness-of-Fit Result of NB & EB Models - Total Occupants in Vehicle 273 Table 5.92: Top Ten Locations by Consequence Predicted with EB in Protection Type Chapter 6: Table 6.1: Summary of Proposed Threshold Critical Values by Protection Types 283 Table 6.2: List of 3 Black-Spots Identified in Protection Type Table 6.3: List of Top Five within 129 Black-Spots Identified in Protection Type Table 6.4: List of Top Five within 76 Black-Spots Identified in Protection Type Table 6.5: List of Top Five within 239 Black-Spots Identified in Protection Type Table 6.6: List of 447 Black-Spots Identified from the Study and Their Locations 292 Table 6.7: Number of Black-Spots Identified by Top Ten States 305 Table 6.8: Number of Black-Spots Identified by Top Eight Counties 305 Table 6.9: Common 17 Black-Spots Identified in All Three Circles (A, B and C) 306 Chapter 7: Table 7.1: Controlled Values for Parameters Constructing Collision Prediction Models 312 Table 7.2: Controlled Values for Parameters Constructing Consequence Prediction Models 324 Table 7.3: Controlled Values for Parameters Constructing Safety Risk Index Models 327 Chapter 8: Table 8.1: EB Modelling Equations for Accident Frequency Prediction with Variables 339 Table 8.2: Impact Effect of Railway and Highway Characteristics on Accident Prediction 340 Table 8.3: EB Modelling Equations for Consequence Prediction with Explained Variables 341 Table 8.4: Impact Effect of All Factors on Consequences Prediction by Protection Type 342 xii

13 List of Figures PAGE Chapter 1: Figure 1.1: A Typical Railway-Highway Crossing in Australia 8 Figure 1.2: Outcome of a Major Accident at Level Crossing (Lismore, Australia 2006) 9 Figure 1.3: Logical Flow of Research Areas Leading to the Thesis Topic 15 Figure 1.4: Flow Diagram for Developing Structure of the Thesis 17 Chapter 2: Figure 2.1: Characteristics of an Event of Accident 19 Figure 2.2: The 4 P s Principles for Safety Management System 26 Figure 2.3: The Basic Safety Management System Process 29 Figure 2.4: The 3 C Elements of Leadership 33 Figure 2.5: Three Major Processes in Risk Management System 39 Figure 2.6: Three Elements in Determination of Risk Assessment 47 Figure 2.7: Two Major Groups Identified in Rail Safety Issues 60 Figure 2.8: Rail Safety Management System as a Central System to All Rail Operations 73 Figure 2.9: International Comparison of Annual Collision Rate for Level Crossings 88 Figure 2.10: International Comparison of Annual Fatality Rate for Level Crossings 88 Chapter 3: Figure 3.1: Three Common Types in Risk Analysis Methods 99 Figure 3.2: Three Basic Elements of Measurements in the Development of SRI 106 Figure 3.3: Graphical Representation of Three Elements of Rail Safety Risk Evaluation 107 Figure 3.4: Flow Diagram for Identifying Black-Spots Within Grade Crossings 110 Figure 3.5: Model of Accident Risk Factors and Associated Variables at Grade Crossings 112 Figure 3.6: Graphical Model of a Typical Quantitative Safety Risk Matrix 135 Figure 3.7: Flow Diagram for Procedures of Identifying Black-Spots 136 Figure 3.8: Identifying Black-Spots within Grade Crossings Based on Safety Risk Index 137 Chapter 4: Figure 4.1: Distribution of Different Categories of Variables in the Inventory Database 142 Figure 4.2: Process for Extraction of Public Grade Crossings from Inventory Database 148 Figure 4.3: Distribution of Different Categories of Variables in the Occurrence Database 150 Figure 4.4: Process for Extraction of Crossings Accidents Information 153 Figure 4.5: All Level Crossing Accidents and Casualties ( ) 157 Figure 4.6: Number of All Level Crossing Accidents and Accident Rates ( ) 158 Figure 4.7: Number of Level Crossing Accidents by Crossing Type ( ) 159 Figure 4.8: Annual Accident Frequency Rate per Crossing Type 160 Figure 4.9: Number of Crossings within Each Type of Crossing ( ) 161 Figure 4.10: Number of Accidents within Each Type of Crossing ( ) 162 Figure 4.11: Accident Frequency Rates within Each Type of Crossing ( ) 162 Figure 4.12: Public Grade Crossing Accidents and Casualties ( ) 163 Chapter 5: Figure 5.1: Number of Crossings Predicted by Poisson Model for Protection Type Figure 5.2: Number of Crossings Predicted by Poisson Model for Protection Type Figure 5.3: Number of Crossings Predicted by Poisson Model for Protection Type Figure 5.4: Number of Crossings Predicted by Poisson Model for Protection Type Figure 5.5: Number of Crossings Predicted by ZIP Model for Protection Type Figure 5.6: Number of Crossings Predicted by ZIP Model for Protection Type Figure 5.7: Number of Crossings Predicted by ZIP Model for Protection Type xiii

14 Figure 5.8: Number of Crossings Predicted by ZIP Model for Protection Type Figure 5.9: Flow Diagram of Developing Empirical Bayesian (EB) Model 189 Figure 5.10: Flow Diagram of Better-Fit Poisson Model Building Process (Step 1) 204 Figure 5.11: Flow Diagram of Better-Fit NB Model Building Process (Step 2) 205 Figure 5.12: Flow Diagram of Comparing Poisson and NB Models (Step 3) 206 Figure 5.13: Flow Diagram of Best-Fit EB Model Building Process (Step 4) 207 Chapter 6: Figure 6.1: Flow Diagram of Developing Safety Risk Index (SRI) Model 278 Figure 6.2: Identifying Safety Status of Grade Crossings by Safety Risk Index Curve 279 Figure 6.3: Safety Risk Index Curves with Different SRI Values 280 Figure 6.4: Estimating Threshold Value for Black-Spots Identification 282 Figure 6.5: Number of Black-Spots by Protection Types Vs Standardized Score of SRI 283 Figure 6.6: Safety Risk Index Vs Percentage of Protection Type 1 Accidental Crossings 285 Figure 6.7: Black-Spots Identification in Protection Type 1 Grade Crossings 285 Figure 6.8: Safety Risk Index Vs Percentage of Protection Type 2 Accidental Crossings 286 Figure 6.9: Black-Spots Identification in Protection Type 2 Grade Crossings 287 Figure 6.10: Safety Risk Index Vs Percentage of Protection Type 3 Accidental Crossings 288 Figure 6.11: Black-Spots Identification in Protection Type 3 Grade Crossings 288 Figure 6.12: Safety Risk Index Vs Percentage of Protection Type 4 Accidental Crossing 289 Figure 6.13: Black-Spots Identification In Protection Type 4 Grade Crossings 290 Figure 6.14: All 447 Black-Spots Identified in Four Protection Type Grade Crossings 291 Figure 6.15: Graphical Demonstration on Comparison of Black-Spots to Crossings Ranked 306 Figure 6.16: Three Cluster Regions of 447 Black-Spots in All Protection Types 308 Chapter 7: Figure 7.1: Flow Diagram for Impact Analysis on Models Developed in the Study 311 Figure 7.2: Effect of AADT on Collision Prediction by Protection Type 313 Figure 7.3: Effect of Number of Traffic Lanes on Collision Prediction by Protection Type 314 Figure 7.4: Effect of Highway Speed on Collision Prediction by Protection Type 315 Figure 7.5: Effect of Daily Train Traffic on Collision Prediction by Protection Type 316 Figure 7.6: Effect of Number of Main Tracks on Collision Prediction by Protection Type 317 Figure 7.7: Effect of Train Speed on Collision Prediction by Protection Type 318 Figure 7.8: Effect of AADT on Predicted Collision Ratio 319 Figure 7.9: Effect of Number of Traffic Lanes on Predicted Collision Ratio 320 Figure 7.10: Effect of Highway Speed on Predicted Collision Ratio 321 Figure 7.11: Effect of Daily Train Traffic on Predicted Collision Ratio 322 Figure 7.12: Effect of Number of Main Tracks on Predicted Collision Ratio 323 Figure 7.13: Effect of Train Speed on Predicted Collision Ratio 324 Figure 7.14: Effect of Occupants in Vehicle on Consequences Prediction by Protection Type 325 Figure 7.15: Effect of Train Speed on Consequences Prediction by Protection Type 326 Figure 7.16: Effect of AADT on Estimation of SRI by Protection Type 328 Figure 7.17: Effect of Number of Traffic Lanes on Estimation of SRI by Protection Type 329 Figure 7.18: Effect of Highway Speed on Estimation of SRI by Protection Type 330 Figure 7.19: Effect of Occupants in Vehicle on Estimation of SRI by Protection Type 331 Figure 7.20: Effect of Daily Train Traffic on Estimation of SRI by Protection Type 332 Figure 7.21: Effect of Number of Main Tracks on Estimation of SRI by Protection Type 333 Figure 7.22: Effect of Train Speed on Estimation of SRI by Protection Type 334 Chapter 8: Figure 8.1: 447 Basic Black-Spots Identified in All Protection Types of Grade Crossings 343 Figure 8.2: Worst Black-Spots Identification in All Protection Types of Grade Crossings 344 Figure 8.3: Number of Worst Black-Spots Identified as per SRI Threshold Value Selected 345 xiv

15 Abbreviations AIC ALARP AS / NZS ATC ATP AADT DOT EB EFS EC ETSC FRA GLM GOF HRGC ITSRR LC LR NB OHS PPE RSSB RC RSA SMS SRI SPAD UN USDOT WD WCA ZIP Akaike's Information Criterion As Low As Reasonably Practicable Australian and New Zealand Standards Australian Transport Council Automatic Train Protection Average Annual Daily Traffic Department of Transportation Empirical Bayesian Equivalent Fatality Score European Countries European Transport Safety Council Federal Railroad Administration Generalised Linear Modelling Goodness-of-Fit Highway-Railway Grade Crossings Independent Transport Safety and Reliability Regulator Level Crossing Linear Regression Negative Binomial Occupational Health & Safety Personal Protective Equipment Rail Safety & Standards Board RailCorp Railway Safety Act Safety Management System Safety Risk Index Signal Passed at Danger United Nation US Department of Transportation Warning Device WorkCover Authority Zero-Inflated Poisson xv

16 Nomenclature P i C i E i SRI λ 2 s _ y a, b i Z i R i X Y R R o Risk score due to probability for i th hazard Risk score due to consequence for i th hazard Risk score due to exposure for i th hazard Safety Risk Index Poisson parameter (e.g. Accident occurrence rate) Sample variance Sample mean Regression parameters i th reference variable Measure of risk for a class of events Expected accident frequency at a grade crossing Estimated consequences (equivalent fatalities) at a grade crossing Estimated safety risk index (SRI) value at a grade crossing Critical (or threshold) safety risk index value ω 1 ω, 2 Weighting factors used in EB models Κ Over-dispersion parameter used in EB models Var (Y) Variance of accidents E ˆ ( Y ) Estimated mean value of accidents from Poisson / NB models y Actual number of accidents from the accident history E ˆ ( Y, y) Refined estimation of accidents from EB models E ˆ ( C Y ) Predicted number of equivalent fatalities by Poisson / NB Models E ˆ [( C Y ), ( C Y )] Refined estimation of equivalent fatalities from EB models e Exponential function Ln Logarithm function 2 R Values of determination coefficient in regression models 2 χ Chi-square statistic DT Daily Train Movement AADT Annual Average Daily Traffic MTTS Maximum Timetable Train Speed HS Highway Speed MT Number of Main Tracks TL Number of Traffic Lanes TCA Track Crossing Angle TOV Total Occupants in Vehicle EFS Equivalent Fatality Score FAT Number of fatalities INJ Number of Injuries PVD Property and vehicle damage in dollars xvi

17 Abstract In the global view, among other rail safety issues, highway-railway accidents continue to be a major problem from both public health and socio-economic perspectives. It is noted that many research studies have been conducted in the past in relation to developing appropriate models to assess the road traffic safety through collision prediction, but a considerable amount of work has been carried out only regarding safety at highway-railway grade crossings. The primary objective of this study is to provide an improved method for rail safety appraisal at railway-highway grade crossings through the development and application of suitable safety risk scores (called Safety Risk Index ) with a combination of both accident frequency and accidental consequences prediction models generated for crossings, and also by using these safety risk scores to identify the worst or most dangerous locations. The Safety Risk Index (SRI) is a simple composite index, which can measure, compare and rank safety levels at different risk situations and locations. These safety risk scores are designed to generate an overall grade crossings safety risk, which is based on the combination of three basic risk elements - namely the exposure of the crossing users, the probability of an accident taking place, and the severity of consequences should an accident occur. This method facilitates the assessment of the safety risks at grade crossings and also ranks, identifies and prioritises the worst performing crossings or the problematic crossings (Black-Spots). This model is very simple and easily understood by those with different levels of knowledge on safety. The SRI index based on quantitative methods and developed in this study seems very promising and has great potential to be a major tool for safety risk assessment at grade crossings in various countries. The secondary objective of this study is to provide an index with a single meaningful value for assessing the risk at grade crossings, through the gathering and analysis of data, information and knowledge (from various data sources) on rail safety. The research study also establishes appropriate statistical methodologies in order to develop and to construct a quantitative model for risk assessment. xvii

18 Chapter 1 Introduction to the Research 1.0 Introduction In the broader aspect of safety associated with various modes of transport, in particular with rail transport, safety at railway crossings is a major area for concern with an increasing number of accidents across various railroad infrastructures. In recent times, the safety of rail crossings and associated safety risk assessments across many situations of road-rail crossings have attracted increasing attention. However, the need for further research in this area, in particular developing comprehensive safety risk assessment models, is warranted with the increasing demand for improved safety which can contribute to improved public health and socio-economic benefits. This chapter introduces the research topic and briefly discusses the initiative of the research. It briefly presents an outline structure of the thesis and explains the reasons why this study is of interest. The aim of the study is also highlighted and a brief description of the developing concept of a risk assessment model (Safety Risk Index) is discussed. It initially provides the basic definition of safety and risks and briefly describes Occupational Health and Safety (OHS) in general industries. It provides an overview of general safety management systems used in industries. In addition, it elaborates the background of the existing potential of rail safety risks and the overview of rail safety management systems. It also describes the objectives of the research study and lists the benefits achieved in relation to this research. Finally it outlines the structure of the thesis in detail. 1.1 Overview of Risk and Occupational Health & Safety Human life is often put at risk when performing various activities in different ways. Basically, risk is the chance that a safety hazard will result in an accident which causes casualties such as loss of life, injury or property damage. Statistically, risk is 1

19 the probability of an untoward event or unfavourable consequence of an event. This truism may have very distinct meanings in the individual locations and populations of today s world. Australian / New Zealand Standard 4360 (2004) defines acceptable risk as An informed decision to accept the consequences and the likelihood of a particular risk. In order to understand what safety involves, it is important and necessary to know the nature of hazards. A hazard is an activity or combination of activities or set of circumstances which could produce an accident with the potential to harm life, health or property. Hazard identification is the process of identifying all risks in the workplace. Hazards are the main cause of occupational health and safety (OHS) problems. Therefore, finding ways of eliminating hazards or controlling the associated risks is the best way to reduce injury and illness. When attempting to interpret what safety means, an ambiguous situation is created. However, Australian / New Zealand Standard 4801 (2001) provides meaning to the term safety as A state in which the risk of harm (to persons) or damage (to properties) is limited to an acceptable level. Given the very close relationship between safety and accidents, the literature suggests that the level of safety is inversely proportional to the number of accidents (Dixit 2007, p.1). Safety at workplaces and also in public places continues to be one of the major emerging concerns and issues in most developing countries. Measuring safety is needed to assess the level of safety, and thereby to identify and improve processes and procedures outlined in the safety management systems. Most of the current safety accreditation procedures appear to allow the tolerance of some risks. In various industries, employees today face a wide array of potential risks to their health and well-being. Some hazards are reasonably apparent, whilst others are often more insidious. Hazards are the prime identifiable cause of occupational health and safety problems (WorkCover-NSW 1996, p.7). Some examples of occupational hazards are: Trip hazards in a passage or corridor; Lifting things in an unsafe manner; Using chemicals incorrectly; Handling of flammable liquids in the presence of ignition sources; 2

20 Loose asbestos released during demolition work which has the potential to cause lung cancer; Noise from an uninsulated chainsaw which can reach levels of up to 110 db with the potential to seriously damage hearing; Badly designed shovel (for example, with a short handle and very large blade) which has the potential to cause back injury; Waste oil from an engine which has the potential to damage workers' health through skin absorption, due to its carcinogenic properties. Occupational Health and Safety (OHS) issues arise not only from physical and chemical problems but also from other features of the operating conditions such as an employee s work experience. Under the OHS Acts and Regulations provided in various countries, the employer has ultimate responsibility to ensure that a safe workplace is maintained. To meet this requirement, employers must ensure that some forms of safety systems are in place and that responsibility has been allocated to managers, supervisors and workers in the organisation. In the meantime, all employees should take responsibility for their own health and safety and for others who may be affected by acts or omissions on their part. Safety responsibility should be part of the daily functions of everyone in the workplace. To assign the safety responsibilities the following are put in place for workers (NT WorkSafe 2003): Incorporate health and safety responsibilities into job descriptions for all workers and encourage workers to identify unsafe work situations; Responsibilities and accountabilities should be assigned for such things as induction training, first aid, emergency procedures and workplace inspections; Ensure that workers fully understand their responsibilities for health and safety. Using induction, adequate education and training programs can achieve this aim. A major challenge for many employers would be managing safety to meet the specified requirements set up by government and safety regulators. In recent times, safety management systems have been developed for managing the above challenges with some success across various industries. 3

21 1.2 Safety Management System (SMS) In general, a safety management system (SMS) is considered to be a businesslike approach to safety. It is a systematic, explicit and comprehensive system for managing safety risks. As with all management systems, a SMS provides for goal setting, planning, and measuring performance. A SMS is woven into the fabric of an organisation (Civil Aviation Safety Transport Canada 2001, p.1). In this regard, SMS is implemented across various functional areas of an organisation, aiming to manage and control the potential risks at workplaces. With the increased use of SMS across various industries and organisations, it has become part of the culture, the way people do their jobs. NT WorkSafe (2003, p.6) states that Safety management is described as a set of actions or procedures relating to health and safety in the workplace, put in place and actively endorsed by management to achieve the following : Identification, assessment and elimination or control of all workplace hazards and risks; Active involvement in health and safety matters with managers and workers working together both formally and informally to improve health and safety; Providing necessary information and training for people at all levels so they can effectively meet their responsibilities; and Designing and implementing company goals about health and safety. Further, a SMS provides an organisation with the capacity to anticipate and address safety issues before they lead to an incident or accident. A SMS also provides management with the ability to deal with accidents and near misses effectively so that valuable lessons are learnt and changes implemented to improve safety and efficiency (Civil Aviation Safety Transport Canada 2001, p.5). The SMS approach reduces losses and improves productivity. The basic safety management process is generally accomplished with major elements of events and functions such as: A safety issue / concern is raised, a hazard is identified, or an incident / accident happens; The concern / event is reported or brought to the attention of management; The event, hazard, or issue is analysed to determine its cause or source; Corrective action, control or mitigation is developed and implemented; and 4

22 The corrective action is evaluated to make sure it is effective. If the safety issue is resolved, the action can be documented and the safety enhancement maintained. If the problem or issue is not resolved, it should be re-analysed until it is resolved. When an organisation develops a safety management policy and associated procedures, they have to fit into the organisation in many ways. For example, safety management has to be comprehensive, but should not be more complex than the rest of the company's management program. Safety management must be compatible, and preferably, integrated into the overall management scheme. A list of the organisation s safety system procedures is helpful to managers who want to know more about how to make safety management a reality. Most items in this list will be familiar to managers. They are already part of the safety landscape. The fundamental changes are concerned with roles and accountability of company's management and the regulator. 1.3 Background of Rail Safety Risk Potentials and Rail SMS United Nations (2000, p.1) states that each year, accidents at level crossings not only cause the deaths of or serious injuries to many thousands of road users and railway passengers, but also impose a heavy financial burden in terms of interruption of railway and road services and damage to railway and road vehicles and property. This leads to the following phenomena: Many billions of dollars are paid in medical costs and disability payments; Medical insurance premiums are increased to meet the rising costs; Capacity of operations and productivity is decreased; Heavy loss of lives and human suffering; Inconvenience caused to the people injured, to others and to the environment. The Rail Safety & Standards Board (RSSB) in the United Kingdom claims that rail is still one of the safest forms of public transport and is nine times safer than travelling by car. However, the railway occurrences and the rise in accidental consequences (including fatalities, injuries and property damage) sustained by passengers, railway 5

23 employees and public in recent years provide a stark reminder of the potential for hazards in railway systems worldwide. Railway occurrences include both: Accidents affecting life and property of passengers, public and employees; and Incidents that do not result in accidents directly but have the potential to do so (known as near miss accidents ). Railway occurrences (accidents and incidents) don t just happen, nor are occurrences completely accidental in nature. There may be many factors which contribute to the railway occurrences, caused by the failure of one or more safety components of the railway system. Identifying, prioritising and targeting the hazard potentials and developing mitigative initiatives and controls can achieve the prevention of such occurrences. In order to improve rail safety, railway authorities and safety agencies keep continuously employing various rail SMS in several countries. These systems are designed to enhance the quality of safety performance for the rail passengers, public and rail employees. Therefore, the Rail SMS is important in ensuring rail safety. It provides a holistic, systematic and optimal way of managing and controlling rail safety risks to achieve desired safe outcomes in a sustainable way. Britain s main line railways have become increasingly safe in recent years (Rail Safety and Standards Board 2006). At the same time, the number of passengers is rising at an unprecedented rate, freight traffic has grown and is set to expand even further, and performance is improving. All this bears out what the Rail sector has always known - that high standards of performance and safety are inextricably linked. It provides what passengers and customers expect while creating the essential condition for growth in the traffic. As the authority for maintaining safety, the Rail sector needs to assure itself and the community (the public, passengers and employees) that the safety risks are being managed to levels that are As Low As Reasonably Practicable (ALARP). There are currently different approaches (national and international) to railway safety, different targets and methods applied. Technical standards, the rolling stock and the certification of staff and railway undertakings differ from one country to another and have not been adapted to the needs of an integrated Rail SMS. However, the world railway safety system covers safety requirements for the system as a 6

24 whole, including infrastructure and traffic management, and the interaction between railway undertakings and infrastructure senior managers. It also focuses on the establishment of common safety indicators in order to assess that the system complies with the common safety targets and facilitates the monitoring of railway safety performances. In general, the Rail SMS system has been developed to meet overall needs based on best international knowledge and practices. A number of initiatives taken under the Rail SMS have led to increased safety awareness and the application of a structured, proactive approach to safety. Through external benchmarking and external SMS reviews set up by the Australian/New Zealand Standards 4801 (2001), the SMS is reinforced to strive for continuous improvement. Based on state-of-the-art knowledge, international best practices and its own experience, the Rail sector s SMS is continuously being upgraded to meet the challenges and needs of a modern, safe mass transit railway. EUROPA (2004) states that safety rules and standards, such as operating rules, signalling rules, requirements on staff and technical requirements applicable to rolling stock, have been devised mainly nationally. Under the regulations currently in force, a variety of bodies deals with safety. These national safety rules, which are often based on national technical standards, should gradually be replaced by rules based on common standards, established by technical specifications for interoperability. The new national rules should be in line with current legislations and facilitate migration towards a common approach to railway safety. In this way, the Rail sector aims to ensure that: Railway safety is generally maintained and continuously improved, taking into consideration the development of current legislations; Safety rules are laid down, applied and enforced in an open and nondiscriminatory manner; Responsibility for the safe operation of the railway system and the control of risks associated with it is borne by the infrastructure managers and railway undertakings; Information is collected on common safety indicators through annual reports in order to assess the achievement of the common safety targets and monitor the general development of railway safety. 7

25 One major element of SMS is to develop appropriate methodologies to assess the safety risk in various operating sectors of railways. One of the major sectors urgently warranting safety evaluation is the interface of railway and highway. This safety evaluation includes a quantitative risk assessment procedure to determine the annual collective risks due to potential accident scenarios from level crossings. 1.4 Significance of the Study Rail Safety and Standards Board (2004, p.1) states at the outset that "Level Crossing Risk is likely to become the largest category of train accident risk on the National Rail network in Great Britain. It is also a significant risk for road users and pedestrians. Safety at level crossings is also one of the most serious safety issues faced by the rail industry in Australia. Sochon and Piamsa-Art (2007, p.1) states that approximately 100 level crossing crashes occur between a road vehicle and a train each year and about 8% of these crashes result in deaths. In addition, about 22 pedestrians die each year while crossing railways on public streets. Fatalities at level crossings are only a small proportion of the national road toll but a major contributor to the rail toll. A typical railway level crossing in Australia is shown in Figure 1.1. Figure 1.1: A Typical Railway-Highway Crossing in Australia 8

26 In recent times, the significance of level crossing safety has been highlighted in several studies. For example, a major trend has recently emerged involving heavy vehicles (trucks) colliding with trains and causing catastrophic damage to those trains and the people on board. In the period between January 2006 and June 2007, there were 11 level crossing crashes involving heavy vehicles. Tragically, 17 lives were lost. More than $100 million in damages resulted. These major incidents include the Kerang crash, where 11 lives were lost, and the Lismore crash, where there was one fatality and more than $25 million in damages resulted (Sochon & Piamsa-Art 2007, p.1). Figure 1.2 shows the consequences of a level crossing major accident which occurred in Lismore (Australia) in May Management of safety at level crossings falls upon individual jurisdictions. Each jurisdiction manages its own initiatives such as level crossing infrastructure upgrades and the development of modelling techniques to identify dangerous crossing locations. By working in partnership, the rail and road authorities can be drawn upon as necessary, allowing the development and delivery of better safety projects. Figure 1.2: Outcome of a Major Accident at Level Crossing (Lismore, Australia 2006) 9