Machine Learning Applications in Logistics: Predicting Truck Driver Turnover
Channel: MIT Center for Transportation & Logistics
Date: 2024-02-05

Dr. Dave Correll, a researcher at MIT’s FreightLab, uses electronic logging devices to predict truck driver turnover and ultimately reduce it. Dr. Correll’s data shows that truck drivers are not driving as much as they could be, contributing to the shortage of drivers in the industry.

Highlights
🚛 Data-driven insights: ELD data revealed a gap between the legal maximum driving hours for truckers and the actual hours they drive.
📈 Predictive modeling: Machine learning models can identify drivers at risk of leaving, enabling managers to intervene and address concerns.
⌛ Utilization crisis: The shortage of truck drivers is largely due to underutilization, as drivers spend excessive time waiting to load or unload.
🗣️ Driver perspective: Qualitative research involving conversations with truck drivers highlights their frustrations and suggests that improving dwell time can enhance job satisfaction.
🤖 Autonomous driving impact: While autonomous trucks could alleviate the driver shortage, Correll cautions that they may exacerbate the underutilization issue if their time is not valued as much as human drivers.

Summary: Watch the first webinar of 2024 in the MITx MicroMasters Program in SCM! Dr. David Correll, Lecturer and Research Scientist at the MIT Center for Transportation & Logistics and Co-Director of the MIT FreightLab, will join hosts Laura Allegue and Kellen Betts, Course Leads in the MicroMasters Program, to discuss recent work in the FreightLab that aims to solve a significant challenge in many supply chains — truck driver turnover. Using machine learning to predict which drivers are at risk of leaving, this research helps companies develop new interventions and strategies for driver retention. In this interactive live event, Dr. Correll will share their research approach and preliminary results working with distribution companies.

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