Extracting Leading Indicators from Transport Data Monitoring Programs using Human Factors Methods

使用人为因素方法从交通数据监测项目中提取先行指标

• 批准号: EP/I036222/1
• 负责人: Guy Walker
• 金额: $ 12.23万
• 依托单位: Heriot-Watt University
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2012
• 资助国家: 英国
• 起止时间: 2012 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FI036222%2F1
• 关键词: Extracting; Leading; Indicators; Transport; Data

Flight Data Monitoring (FDM) is the process by which data from on-board recorders (or so-called 'black boxes') is subject to regular and systematic analysis, not just after emergencies but after every flight. This is performed so that subtle trends which arise as pre-cursors to more serious incidents can be detected in advance and used to proactively manage risk. The same technique is also used as a way of meeting environmental and economic goals through improved operational efficiency, fuel consumption and maintenance. FDM is relevant to the railway industry because since 2005 all trains now have to carry similar on-board recorders. The primary motivation is to provide accident investigators with an invaluable diagnostic tool, but like the aviation sector, because accidents are comparatively rare a far greater quantity of data is collected on normal, routine, non-accident journeys. As a result, recorder data in the rail industry represents a significantly underused resource. The proposed research relates to a class of problem which occurs firmly at the human/system interface and which is shared by both the aviation and rail sectors. Both domains experience problems, for example, Signals Passed At Danger (or SPADs) and Controlled Flight Into Terrain (CFIT), where several safety systems are defeated by human operators and otherwise fully functional trains or aircraft are placed in highly unsafe conditions. Problems such as these fall within the purview of Human Factors. On-board recorder data, be it from the rail or aviation sectors, represents a novel source of input for established human factors methodologies targeted at addressing them. The primary goal of the research, therefore, will be to couple recorder data to human factors methods in a way not previously attempted. The outcome will be 'leading indicators' of problems which, so far, have proven resistant to conventional safety interventions. Related to this are leading indicators, or metrics, that could help to inform ongoing research into operational efficiency, 'eco-driving', and potentially cost-saving insights into future maintenance practices. These opportunities can be systematically examined with reference both to human factors methods and to the mature FDM processes that currently exist in the aviation industry. The project is set against, and motivated by, a wider backdrop of European rail interoperability, a desire to maximise the environmental benefits of rail travel and the UK's position as a world leader in FDM processes. Whilst the research has at its core an innovative programme of theoretical advance, it is also coupled to several near-term applications. Firstly, the UK Civil Aviation Authority (CAA) seek to inform (and be informed of) best practice in other transport domains and the proposed project aims to provide a conduit for such knowledge. Secondly, both the CAA and the Association of Train Operating Companies (ATOC) are actively seeking leading indicators of safety related problems, particularly those which occur at the human/system interface. The project will map important theoretical developments in human factors methods to these real-world applications. Third, the proposed research is directly relevant to current industry projects managed by the Rail Safety and Standards Board (RSSB), including several relating to safety management systems, eco-driving and route knowledge. In summary, the proposed research represents a highly innovative approach to understanding and diagnosing issues which occur at the boundary of humans and transport systems. It is also an example of research with high economic and societal impacts, and an example of research application with great potential to develop further work and collaborations with industry partners.

飞行数据监控（FDM）是一个过程，通过该过程，来自机载记录器（或所谓的“黑匣子”）的数据受到定期和系统的分析，不仅在紧急情况后，而且在每次飞行后。这样做是为了提前检测到作为更严重事件前兆出现的微妙趋势，并用于主动管理风险。同样的技术也被用作通过提高运营效率、燃料消耗和维护来实现环境和经济目标的一种方式。FDM与铁路行业相关，因为自2005年以来，所有列车都必须携带类似的车载记录仪。主要动机是为事故调查人员提供宝贵的诊断工具，但与航空部门一样，由于事故相对罕见，因此在正常、常规、非事故旅程中收集的数据要多得多。因此，铁路行业的记录仪数据是一种严重未充分利用的资源。拟议中的研究涉及一类问题，这类问题发生在人/系统界面上，航空和铁路部门都有。这两个领域都存在问题，例如，危险信号传递（SPAD）和受控飞行进入地面（CFIT），其中几个安全系统被人类操作员击败，否则功能齐全的火车或飞机处于高度不安全的条件下。诸如此类的问题属于人为因素的范畴。车载记录仪数据，无论是来自铁路还是航空部门，都是针对解决这些问题的既定人为因素方法的一个新的输入来源。因此，这项研究的主要目标是以一种以前没有尝试过的方式将记录仪数据与人为因素方法结合起来。其结果将是迄今为止已证明对传统安全干预具有抵抗力的问题的“领先指标”。与此相关的是领先的指标或指标，可以帮助为正在进行的运营效率研究提供信息，“生态驾驶”，以及对未来维护实践的潜在成本节约见解。这些机会可以参照人为因素方法和航空业目前存在的成熟FDM过程进行系统地检查。该项目的背景和动机是欧洲铁路互操作性的更广泛背景，最大限度地提高铁路旅行的环境效益的愿望，以及英国作为FDM过程的世界领导者的地位。虽然这项研究的核心是理论进步的创新方案，但它也与几个近期应用相结合。首先，英国民航局（CAA）寻求告知（和被告知）在其他运输领域的最佳做法，拟议的项目旨在提供这种知识的渠道。其次，民航局和列车运营公司协会（ATOC）都在积极寻找与安全有关的问题的领先指标，特别是那些发生在人/系统界面的问题。该项目将把人因方法的重要理论发展映射到这些实际应用中。第三，拟议的研究与铁路安全和标准委员会（RSSB）管理的当前行业项目直接相关，包括与安全管理系统，生态驾驶和路线知识有关的几个项目。总之，拟议的研究代表了一种高度创新的方法来理解和诊断发生在人类和运输系统边界的问题。它也是具有高度经济和社会影响的研究的一个例子，以及具有与行业合作伙伴开展进一步工作和合作的巨大潜力的研究应用的一个例子。

项目成果

Leading indicators of operational risk on the railway: a novel use for underutilised data recordings

• DOI: 10.1016/j.ssci.2014.11.017
• 发表时间: 2015-04
• 期刊: Safety Science
• 影响因子: 6.1
• 作者: G. Walker;A. Strathie

EPSRC EP/I036222/1 Human Factors Leading Indicators

EPSRC EP/I036222/1 人为因素领先指标

• 作者: Guy Walker (Author)

Broken components versus broken systems: why it is systems not people that lose situation awareness

• DOI: 10.1007/s10111-015-0324-4
• 发表时间: 2015-05
• 期刊: Cognition, Technology & Work
• 作者: P. Salmon;G. Walker;N. Stanton

From flight data monitoring to rail data monitoring

从飞行数据监控到铁路数据监控

• 发表时间: 2012
• 期刊: Proceedings of the 4th International Conference on Applied Human Factors and Ergonomics AHFE 2012
• 作者: Dr Guy Walker

EPSRC EP/I036222/1 Review of the Knowledge Base

EPSRC EP/I036222/1 知识库审查

• 作者: Guy Walker (Author)