Utilization of the American Truck Driver
Name
scm2020-buttgenbach-utilization-of-the-american-truck-driver.pdf
Description
Supply Chain Management capstone research project
Size
2.16 MB
Format
Adobe PDF
Checksum (MD5)
09164655c97319b1b36b3cf13bfa6cc5
Author(s)
Zhang, Mei Qing
Buttgenbach, Adam
Date Issued
July 24, 2020
Abstract
The electronic logging device mandate was implemented with the intention of keeping
truck drivers in compliance with the hours of service regulations to reduce driver fatigue and
trucking accidents. Two years after the electronic logging device mandate became law, there
have not been many studies that use trucking operational data such as the newly available
electronic logs to look for efficiency gain. Our team received six months newly available raw
logging data. This paper aims to use different analysis techniques in machine learning on the
raw electronic logging data to find areas of opportunity that can be used by management to
control and improve driver utilization. The three significant factors that we investigated for on
the amount of time a driver spends at each freight location are: the time of day the driver arrives
at a shipper location, the impact from a specific location, and the frequency that the carrier visits
a specific shipper. Each of the three factors were found to imply a statistically significant impact
on the stop duration. This study shows the usefulness of using electronic logging data to identify
the underlying factors on stop time so that managers can schedule truck drivers more efficiently.
This will allow for higher driving hours during the day, which translates to higher income for the
drivers. Since the raw electronic logging device data is readily available for all On The Road
carriers, we hope to inspire further data analysis on electronic logging device data to help
improve the lives of truck drivers.
Subjects
Transportation
Data Analytics
Machine Learning
Persistent DSpace Link
