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

Auburn University, Auburn AL

Investigators

Abstract

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.