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CRC for Rail Innovation Safety Research

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Presentation by David George, CEO of the CRC for Rail Innovation, to the Rail Safety Strategic Forum, November 2011

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CRC for Rail Innovation Safety Research

  1. 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. 2. Collaborative Research to achieve more with less$100m research program over 7 yearsCollaboration between industry anduniversitiesOver 100 projects under 6 themesIndustry driven, adoption focused research
  3. 3. Six Research ThemesResearch program focusing onsix themes: Safety & Security Climate Change and the Environment Performance Smart Technologies Urban Rail Access Workforce Development
  4. 4. Outline of selected safety projectsAffordable 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. 5. R3.122 Affordable LevelCrossings Project – Stage 2Project commenced in March 2011Aims: evaluate and trial low-cost level crossing warningdevices 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. 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. 7. Expected OutcomesSet of requirements for LCLCWDs with safety andavailability targets; • Risk assessment model • Human reliability assessment modelLifecycle assessment criteria; • Identification of where cost savings can be madeTrial 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. 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. 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 its 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. 10. Expected OutcomesEstablish precise definitions of near-miss • Using objectively measurable information (from in-cab video capture and data logging system)Establish technical data capture performance criteria tounderpin essential data capture needed • Allows industry to approach suppliers for equipment that meets these needsImproving understanding of the context of near-missesand 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 factorsProject can inform simulator training for drivers • Can potentially provide the basis for competency assessment in relation to identification of near-misses
  11. 11. R2.111 ITS for safer level crossings Project commenced in July 2010Aims to assess capability of Intelligent TransportSystems technologies to reduce crashed at RLXs • Trialing 3 types of ITS on an advanced driving simulator
  12. 12. ScopeResearch 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. 13. Expected OutcomesA scientific assessment of the safetyimpacts of RLX – emerging ITS basedinterventions on driver behaviour.Cost benefits assessments.Recommendations to industry.
  14. 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. 15. Why Simulator Scenarios?Increasing need in the Australian rail industry to traindrivers faster and more effectively.Industry-based simulator usage is widespread but thereis little consistency in application methodologies.A good understanding of route knowledge andsubstantive driving strategies is required to cultivatetrain driving competency • Very little is currently known about how route knowledge is mentally encoded. • Driving strategies are subject to considerable individual differences.
  16. 16. Expected OutcomesA comprehensive picture and understanding of how therailway is psychologically structured • Route knowledge review • Mental schematics • Alignment with effective driving strategyIdentification of a scenario suite • Captures train driving skill • Dimensionalises task demandResults from simulator evaluation • Scientifically informed simulator scenario suite • Advise national simulator training practice • Introduce better consistency in simulator application and management
  17. 17. R2.118: RLX intervention framework Project commenced July 2011Aim: Identify an optimal intervention framework for managing safety upgrades to railway level crossings.
  18. 18. ScopeResearch 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. 19. Expected OutcomesLegal advice on argumentto deploy LCLCWDs withinRail Safety Act.Decision making framework.Advocacy campaign (ARA).
  20. 20. Rail Incident Investigator (P4.113) Project commenced November 2010Aims:• 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. 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. 22. Project BenefitsDevelopment of a rail-specific competencyframework and curricula for a multi-levelnational 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. 23. R2.109 Second generation FatigueRisk management System (FRMS) Commenced: 2009Aims: To develop a framework for a flexible risk- based national standard for fatigue management for the rail industry.
  24. 24. BenefitsA standardised approach to fatigue riskmanagement based on current scientific knowledgeand best practice.A set of practical tools and strategies to be used inthe development of individual FRMS.Standardised guidelines for the use of pre-existingfatigue management tools such as FAID.A set of standardised key performance indicatorsagainst which rail operators and regulators canassess the performance of a rail organisation’sFRMS.
  25. 25. OutcomesFramework for a national standard,based on scientific evidence andcurrent best practice.A compliance code to assistorganisations in meeting the standard.Tools and guidelines for policydevelopment and fatigue riskmanagement.
  26. 26. R2.110 Next generation Fatigue Risk management System (FRMS) Project commenced Jul 2009Aim: To improve the reliability and validity of the data used to inform fatigue models.
  27. 27. BenefitsWork-related fatigue modeling toolsrepresentative of the different social/domesticprofiles of different workgroups within theindustry.Capacity to inform the likelihood that a givenshift falls within a specified fatigue scorerange.Improving the match between observed andpredicted fatigue (i.e. by using individualpredictors); thereby limiting the unsafe workhours and reducing unnecessary restrictionsof working hours.
  28. 28. Expected OutcomesNew parameters for defining levelsof fatigue risk associated withworking time for different workgroups and demographic profiles.A more flexible approach to fatiguemodeling that reflects the currentstate of the industry.
  29. 29. Thank you for your attentionDavid George and Professor Andry Rakotonirainy

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