Strathie Ailsa, Walker Guy H
Institute of Infrastructure and Environment, Heriot-Watt University, Edinburgh, United Kingdom
Institute of Infrastructure and Environment, Heriot-Watt University, Edinburgh, United Kingdom.
Hum Factors. 2016 Mar;58(2):205-17. doi: 10.1177/0018720815613183. Epub 2015 Dec 11.
A proof-of-concept analysis was conducted to establish whether link analysis could be applied to data from on-train recorders to detect patterns of behavior that could act as leading indicators of potential safety issues.
On-train data recorders capture data about driving behavior on thousands of routine journeys every day and offer a source of untapped data that could be used to offer insights into human behavior.
Data from 17 journeys undertaken by six drivers on the same route over a 16-hr period were analyzed using link analysis, and four key metrics were examined: number of links, network density, diameter, and sociometric status.
The results established that link analysis can be usefully applied to data captured from on-vehicle recorders. The four metrics revealed key differences in normal driver behavior. These differences have promising construct validity as leading indicators.
Link analysis is one method that could be usefully applied to exploit data routinely gathered by on-vehicle data recorders. It facilitates a proactive approach to safety based on leading indicators, offers a clearer understanding of what constitutes normal driving behavior, and identifies trends at the interface of people and systems, which is currently a key area of strategic risk.
These research findings have direct applications in the field of transport data monitoring. They offer a means of automatically detecting patterns in driver behavior that could act as leading indicators of problems during operation and that could be used in the proactive monitoring of driver competence, risk management, and even infrastructure design.
进行了一项概念验证分析，以确定链路分析是否可应用于车载记录仪的数据，以检测可能作为潜在安全问题先行指标的行为模式。
车载数据记录仪每天捕捉数千次常规行程中的驾驶行为数据，提供了一个未开发的数据来源，可用于深入了解人类行为。
使用链路分析对6名司机在16小时内沿同一路线进行的17次行程的数据进行分析，并检查了四个关键指标：链路数量、网络密度、直径和社会计量状态。
结果表明，链路分析可有效地应用于从车载记录仪捕获的数据。这四个指标揭示了正常驾驶员行为的关键差异。这些差异作为先行指标具有良好的结构效度。
链路分析是一种可有效应用于利用车载数据记录仪常规收集的数据的方法。它有助于基于先行指标采取主动的安全措施，更清楚地了解正常驾驶行为的构成，并识别人员与系统界面处的趋势，这是目前战略风险的一个关键领域。
这些研究结果在交通数据监测领域有直接应用。它们提供了一种自动检测驾驶员行为模式的方法，这些模式可能作为运营期间问题的先行指标，并可用于主动监测驾驶员能力、风险管理甚至基础设施设计。
