Counting the Minutes—Measuring Truck Driver Time Efficiency

Abstract

How truck drivers spend their time is crucial to the profitability of motor carriers. However, the aspect of time has been marginally addressed in industry practice and academic literature on motor-carrier strategies and operations. Given the importance of the drivers in transport operations, the purpose of this article is to outline how driver time in motor-carrier operations can be measured using various methods. The underlying empirical data related to transport efficiency have been collected over five years throughout numerous projects in Europe. The article summarizes the experiences from the measurements carried out in these projects. In total, 80 drivers from 22 motor carriers were measured using different types of participant observations and driver self-observation. Practitioners addressing drivers' time use will find important principles to guide their work in measuring drivers' activities. For future research into driver activities, we suggest using the nine activity categories proposed in our note.

Urban Logistics: Establishing Key Concepts and Building a Conceptual Framework for Future Research

“…Furthermore, examining driver performance and retention in the urban context offers interesting avenues for future research, with more attractive access to home time (compared to long-haul drivers) potentially outweighed by added stress of navigating congested urban areas (Keller and Ozment 1999 ;LeMay et al 2013 ;Stephenson and Fox 1996 ;Williams, Garver, and Taylor 2011 ). Additionally, previous logistics research examines the impact of formal and informal controls on driver performance and driver-time effi ciency (Procki and Sternberg 2015 ;Saldanha, Hunt, and Mello 2013 ), but the research does not incorporate urban factors such as constrained space, traffi c congestion, and external stakeholders imposing additional controls.…”

Break‐taking behaviour pattern of long‐distance freight vehicles based on GPS trajectory data

“…From the above three plots, we can determine that the three can almost be fit to the similar long-tailed distribution.Based on the three vehicles mentioned above, we further statistically analyse the distribution of the break duration of all the vehicles. The Project "Stardriver" took advantage of a smartphone app to support driver self-observation, and finally offered six mainly measurement categories of drivers' activities, such as drive, wait, load, unload, break and service (including fueling), combined with the actual situation[36], in general, we can divide the classification of a long-distance freight vehicle driver's daily break-taking activities into four categories: waiting for a traffic light, having a meal (as the service's representative), sleeping, and loading or unloading cargo. On this basis, to determine the characteristics of the distribution of the break duration of all the vehicles, we must ensure that the distributions of the break durations of every vehicle have similar characteristics using the t-test method as the significance test.…”