GOALI/Collaborative Research: Human Maintenance - A Prognostics Framework to Model Changes in Drivers' Safety Performance and Optimize Dispatching Policies

这个赠款机会学术联络与行业（GOALI）项目将调查纳入分析工具建模卡车司机的安全性能和调度政策的后续优化的机会。这项研究的动机是这样一个事实，即交通事故仍然是一个紧迫的公共安全问题，在美国和世界各地。驾驶员因车祸而表现恶化是造成致命交通事故的一个主要因素，特别是在涉及商用半拖车卡车的交通事故中。卡车司机在复杂和动态的环境中工作，目前对各种因素如何与司机安全履行所需职责的能力相互作用还没有足够的了解。与此同时，大量的数据要么是由卡车运输公司例行收集的，要么是从其他地方获得的。这些数据包括路线和休息时间表的详细信息，驾驶员记录的小时数，交通和天气状况，驾驶相关的结果等。该研究项目旨在了解驾驶员的性能变化如何随着这些数据集所代表的驾驶条件的变化而发展，以及随后如何将这些信息用于实际决策。这项研究是集成的教育计划，其基石是一个在线平台，将允许传播的研究成果，以当前和未来的practitioners.It假设，驾驶结果的恶化可以使用累积老化模型从可靠性理论建模。驾驶员安全性能的变化（例如，疲劳、注意力集中、嗜睡或冒险）将使用卡车活动数据而不是更具侵入性的机舱内记录（脑电图、心率监视器等）来参数化地或非参数化地建模为老化函数。接下来，这个模型将被用来将安全和司机的性能考虑在路由和调度策略。这里的重点将是框架的数据驱动性质，随着更多数据的可用，能够不断改进和完善模型。如果成功，该项目将为卡车运输业的决策提供一个全面的框架。

This Grant Opportunity for Academic Liaison with Industry (GOALI) project will investigate opportunities for incorporating analytical tools for modeling truck drivers' safety performance and subsequent optimization of dispatching policies. This research is motivated by the fact that transportation incidents remain a pressing public safety issue in the United States and throughout the world. Fatigue-related deterioration of driver's performance is a major factor contributing to fatal road incidents, especially among those involving commercial semi-trailer trucks. Truck drivers operate in a complex and dynamic environment, and currently there is not enough understanding of how various factors interact with the driver's ability to safely perform the required duties. At the same time, large amounts of data are either routinely collected by trucking and transportation companies or are available elsewhere. These data include route and rest schedule details, hours logged by the drivers, traffic and weather conditions, driving-related outcomes, etc. The research project aims at understanding how changes in a driver's performance develop as a function of driving conditions represented by those datasets, and subsequently, how this information can be used in practical decision making. The research is integrated with an education plan whose cornerstone is an online platform that will allow for the dissemination of the research outcomes to current and future practitioners.It is posited that deterioration in driving outcomes can be modeled using cumulative aging models from reliability theory. The changes in in a driver's safety performance (due to e.g., fatigue, attentiveness, sleepiness, or risk-taking) will be modeled as an aging function either parametrically or non-parametrically using truck activity data rather than more invasive in-cabin recordings (electroencephalograms, heart rate monitors, etc.). Next, this model will be employed to incorporate safety and driver's performance considerations in routing and dispatching policies. The focus here will be on the data-driven nature of the framework enabling continuous improvement and refining of the models as more data becomes available. If successful, this project will provide a comprehensive framework for decision making in the trucking industry.