CRC for Rail Innovation Safety Research
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The document details the CRC for Rail Innovation's safety research program, a collaborative initiative involving over 100 projects focused on six key themes, including safety and security, climate change, and smart technologies. It describes specific projects aimed at improving safety at railway level crossings, optimizing driver training through simulators, and developing a framework for national fatigue risk management standards. The research aims to create effective safety measures, enhance training methodologies, and standardize fatigue management across the rail industry.
CRC for Rail Innovation Safety Research
- 1. CRC for Rail Innovation Safety Research David George, CEO and Prof Andry Rakotonirainy,QUT Established and Supported under Australia’s Cooperative Research Centres Programme
- 2. Collaborative Research to achieve more with less $100m research program over 7 years Collaboration between industry and universities Over 100 projects under 6 themes Industry driven, adoption focused research
- 3. Six Research Themes Research program focusing on six themes: Safety & Security Climate Change and the Environment Performance Smart Technologies Urban Rail Access Workforce Development
- 4. Outline of selected safety projects Affordable Railway crossing stage 2 (R3.122) Baseline Level Crossing video (R2.119) ITS for safer level crossing (R2.111) Route Knowledge/Driving strategies (R2,112/113) Level crossing intervention (R2.118) Rail Incident Investigator (P4.113) Track worker protection technology (R3.120) Next Generation Fatigue Risk Management (R2.109/110)
- 5. R3.122 Affordable Level Crossings Project – Stage 2 Project commenced in March 2011 Aims: evaluate and trial low-cost level crossing warning devices in several jurisdictions; • Candidate devices will be trialled in shadow-mode (i.e. overlaid on a vital track circuit without installation of the candidate device’s road-user interface)
- 6. Why Affordable Level Crossings? Collectively, passive crossings represent a significant safety issue for Australia. They cost at least 25% of the cost of traditional crossing technologies. For a given investment, more crossings can be treated; • Greater safety benefit for same investment used to treat crossings using traditional technologies. (ATSB, 2008a; RSRP, 2008)
- 7. Expected Outcomes Set of requirements for LCLCWDs with safety and availability targets; • Risk assessment model • Human reliability assessment model Lifecycle assessment criteria; • Identification of where cost savings can be made Trial results; • Comparative performance and operational data (reliability, availability, maintainability) Results from human factors study; • Effectiveness of various measures to improve performance of road users at level crossings that are unavailable (effective communication of crossing state)
- 8. R2.119 Baseline Level Crossing Video Project commenced in July 2011 Aims: capture the context of near-misses using video obtained from forward facing cameras installed in trains • Digital image processing algorithms will be developed to identify events of interest from video footage
- 9. Problems Project is Addressing Subjectivity of near-miss reporting • Near-misses are self reported – a near-miss to one driver is not the same as a near-miss to another • Under-reporting is also an issue Near-miss data is the most important precursor data available • Currently because of it's unreliability, it is not suitable for analysis • Crash data is of limited use due to statistical uncertainty (because of low number of occurrences) Precise definitions and sub-categorizations of near-miss (technically derived from video image processing and other context data) will significantly improve the usefulness of near-miss data • Supports causal analysis, better continuity for trend analysis, • Improves risk models to support better prioritization of upgrade funding, etc.
- 10. Expected Outcomes Establish precise definitions of near-miss • Using objectively measurable information (from in-cab video capture and data logging system) Establish technical data capture performance criteria to underpin essential data capture needed • Allows industry to approach suppliers for equipment that meets these needs Improving understanding of the context of near-misses and causal factors • Expected to inform measures to improve safety at level crossings (i.e. Using a data-mining approach, correlations can be found between near-misses and contextual factors Project can inform simulator training for drivers • Can potentially provide the basis for competency assessment in relation to identification of near-misses
- 11. R2.111 ITS for safer level crossings Project commenced in July 2010 Aims to assess capability of Intelligent Transport Systems technologies to reduce crashed at RLXs • Trialing 3 types of ITS on an advanced driving simulator
- 12. Scope Research question: Can ITS intervention reduce crashes at RLX by improving driver’s awarenessp; • Trial 3 emerging ITS interventions on the advanced driving simulator (HMI side). • In-vehicle and road-based interventions. • Drivers’ errors or violations the largest contributor to RLX crashes. Strategy: Reduce main driver’s errors at crossings (failure to detect crossing/train and misjudgments of train approach speed/distance).
- 13. Expected Outcomes A scientific assessment of the safety impacts of RLX – emerging ITS based interventions on driver behaviour. Cost benefits assessments. Recommendations to industry.
- 14. R2.112/113 Route Knowledge & Driving Strategies Project commenced in October 2010 Aims: Devise simulator scenarios to optimize skills acquisition and knowledge transfer during learning • A simulator scenario suite will be developed and tested under heavy haul and passenger train driving conditions
- 15. Why Simulator Scenarios? Increasing need in the Australian rail industry to train drivers faster and more effectively. Industry-based simulator usage is widespread but there is little consistency in application methodologies. A good understanding of route knowledge and substantive driving strategies is required to cultivate train driving competency • Very little is currently known about how route knowledge is mentally encoded. • Driving strategies are subject to considerable individual differences.
- 16. Expected Outcomes A comprehensive picture and understanding of how the railway is psychologically structured • Route knowledge review • Mental schematics • Alignment with effective driving strategy Identification of a scenario suite • Captures train driving skill • Dimensionalises task demand Results from simulator evaluation • Scientifically informed simulator scenario suite • Advise national simulator training practice • Introduce better consistency in simulator application and management
- 17. R2.118: RLX intervention framework Project commenced July 2011 Aim: Identify an optimal intervention framework for managing safety upgrades to railway level crossings.
- 18. Scope Research questions: 1. Which framework (incremental or system-wide) effectively optimizes the goals of increased network safety and low equipment cost for management of railway level crossings? 2. Is it legally viable to upgrade level crossings to a standard that is not fail-to-safe? Approach: Basic risk analysis and clarity on the legal position for implementing countermeasures that do not render railway level crossings fail-to-safe.
- 19. Expected Outcomes Legal advice on argument to deploy LCLCWDs within Rail Safety Act. Decision making framework. Advocacy campaign (ARA).
- 20. Rail Incident Investigator (P4.113) Project commenced November 2010 Aims: • Develop a national training program & capability framework for rail incident investigators. • Establish the potential market demand. • Define the curricula for a multi-level national training program. • Explore training providers & delivery options.
- 21. Scope A previous scoping report (P4.107) recognised that the Australian Rail Industry did not have a national approach to developing rail incident investigations. By 1 January, 2013, a National Rail Safety Regulator will be appointed. This has led to strong support from the industry for a more collaborative approach. There was agreement amongst participants on the need for a competency framework and qualification pathway for investigators. Approach: Structured interviews and weighted checklists were utilised to determine core competencies for investigators as well as forming the basis for the training needs analysis.
- 22. Project Benefits Development of a rail-specific competency framework and curricula for a multi-level national training program will: Allow industry to share training resources. Increase the recruitment pool of qualified professional investigators. Provide a nationally recognised career pathway for rail investigators. Enhance the quality of both curricula and training providers.
- 23. R2.109 Second generation Fatigue Risk management System (FRMS) Commenced: 2009 Aims: To develop a framework for a flexible risk- based national standard for fatigue management for the rail industry.
- 24. Benefits A standardised approach to fatigue risk management based on current scientific knowledge and best practice. A set of practical tools and strategies to be used in the development of individual FRMS. Standardised guidelines for the use of pre-existing fatigue management tools such as FAID. A set of standardised key performance indicators against which rail operators and regulators can assess the performance of a rail organisation’s FRMS.
- 25. Outcomes Framework for a national standard, based on scientific evidence and current best practice. A compliance code to assist organisations in meeting the standard. Tools and guidelines for policy development and fatigue risk management.
- 26. R2.110 Next generation Fatigue Risk management System (FRMS) Project commenced Jul 2009 Aim: To improve the reliability and validity of the data used to inform fatigue models.
- 27. Benefits Work-related fatigue modeling tools representative of the different social/domestic profiles of different workgroups within the industry. Capacity to inform the likelihood that a given shift falls within a specified fatigue score range. Improving the match between observed and predicted fatigue (i.e. by using individual predictors); thereby limiting the unsafe work hours and reducing unnecessary restrictions of working hours.
- 28. Expected Outcomes New parameters for defining levels of fatigue risk associated with working time for different work groups and demographic profiles. A more flexible approach to fatigue modeling that reflects the current state of the industry.
- 29. Thank you for your attention David George and Professor Andry Rakotonirainy