Collaborative Research: Integrated Data-Driven Methodologies to Addressing the Driver Turnover and Shortage Problems in Truckload Transportation

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In the Truckload transportation industry, high driver turnover is a chronic problem, representing an annual cost of up to $2.8 billion. A primary reason is extended on-road times for drivers, largely a consequence of the point-to-point dispatching most often used in industry. Another related problem that is becoming increasingly important is the driver shortage, estimated to reach 239K in a decade, due to the retirement of the baby-boomer generation, an improving economy, and newly implemented hours-of-service regulations. The research will comprehensively investigate the driver turnover and shortage problems and address them through a systems-oriented design focus that also includes new proactive operational paradigms. In contrast to existing operational practices to fire-fight individual issues as they arise, a systems approach framework will take into account the perspectives of all stakeholders (drivers, companies, and customers) and involve modeling and analysis of both current and new systems and their operations via descriptive, predictive, and prescriptive analytics. The outcome of this research is expected to make substantial impact in addressing the turnover and shortage problems by providing a systems-oriented framework that incorporates both operational and turnover related costs while improving driver satisfaction with a data-driven collaborative dispatching tool. The research has the potential to significantly reduce an unproductive cost on the order of billions in the industry, and to benefit society by: 1) supporting the driver workforce (and families) with improved health, prosperity, and welfare, 2) unlocking the potential for employment opportunities for new workforce, and 3) reducing costs with improved transportation operations.The system design and operations can improve driver satisfaction by better accommodating individual preferences for location, schedule regularity, mileage, and other factors while also improving economic measures important to all stakeholders. System development will proceed following a systems-oriented approach that is structured around a framework of three integrated analytical components. These components will be the product of 1) descriptive (designing and collecting field data from groups of drivers and dispatchers to determine the factors affecting truckload operations and turnover as well as their quantified contributions), 2) predictive (applying these data to the design of a new data-driven turnover prediction model in the context of a new collaborative dispatching paradigm), and 3) prescriptive (designing optimum relay and point-to-point networks via an optimization-and-simulation framework that incorporates collaborative dispatching/data-driven turnover prediction and considers operational costs (e.g., empty repositioning) as well as turnover costs obtained by the data-driven turnover prediction model) analytical methods.