Revolving drivers: data mining and discovering the causes of driver turnover
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Date
2019-12
Authors
Pazdor, Adam
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Abstract
Turnover (or Churn) is a great concern to every industry. Employees who leave
represent hours of training wasted and the expense of hiring a replacement, something
undesirable for any business. Few industries experience the problem as acutely as the
trucking industry, where turnover rates have been as high as 90%. Uncovering the
underlying reasons that are behind why so many drivers leave their jobs is a point
of priority for many trucking companies. A solution, or even an explanation, could
mean hundreds of training hours and thousands of dollars saved. In this M.Sc. thesis,
I examine real-life data from a trucking company and use a random forest model to
understand the driver turnover situation.
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Keywords
Data mining, Transportation data mining, Data science, Business analytics, Churn rate, Turnover rate, Data analytics