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Predicting truck driver turnover


  • Suzuki, Yoshinori
  • Crum, Michael R.
  • Pautsch, Gregory R.


We propose a decision tool for truckload carriers that can help control driver turnover rates. Our approach is to use an existing econometric method, along with the drivers' work data, to predict the quit probability of each driver on a weekly basis, so that carriers can identify a subset of drivers who are "about to quit" in a timely manner. Empirical results from two case studies indicate that our approach does a nice job of predicting driver exits, and that it may become a useful management decision tool. Our method was recently adopted by two US truckload carriers.

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  • Suzuki, Yoshinori & Crum, Michael R. & Pautsch, Gregory R., 2009. "Predicting truck driver turnover," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 45(4), pages 538-550, July.
  • Handle: RePEc:eee:transe:v:45:y:2009:i:4:p:538-550

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    References listed on IDEAS

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    Cited by:

    1. Zolfagharinia, Hossein & Haughton, Michael, 2016. "Effective truckload dispatch decision methods with incomplete advance load information," European Journal of Operational Research, Elsevier, vol. 252(1), pages 103-121.
    2. Lannoo, Steven & Verhofstadt, Elsy, 2016. "What drives the drivers? Predicting turnover intentions in the Belgian bus and coach industry," Transportation Research Part A: Policy and Practice, Elsevier, vol. 91(C), pages 251-259.
    3. Corsi, Thomas M. & Grimm, Curtis M. & Cantor, David E. & Sienicki, Dale, 2012. "Safety performance differences between unionized and non-union motor carriers," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 48(4), pages 807-816.
    4. repec:spr:jbecon:v:87:y:2017:i:8:d:10.1007_s11573-017-0847-7 is not listed on IDEAS


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