Abstract

Factors affecting forwarding work are interesting because they can be used to better optimize forwarding routes and to predict costs. The main objective of this study was to investigate the association between driving speed and driving distance when forwarding. Data was automatically collected during 2.5 years from two large forwarders operating during final felling in central Sweden. Driving speeds for the work tasks Driving unloaded, Loading drive and Driving loaded were analysed using correlation, least-squares regression, and quantile regression. The results showed that speed and distance were strongly correlated for the work element Driving unloaded, while the correlation was weaker for Loading drive and Driving loaded. Possible factors leading to these results are as follows: longer travelling distances stimulate better planning and the establishment of better extraction roads; operators may feel stressed and drive faster as travelling distance increases; and finally, the relative influence of accelerations and decelerations decreases with increasing driving distance. Also, the use of quantile regression was successful and provided information that normal least-squares regression does not provide.

Keywords

cut-to-length; quantile regression; automatic data collection; forwarder; logging; big data

Published in

Journal of Forest Science
2019, Volume: 65, number: 5, pages: 183-194

SLU Authors

• Berg, Simon

  • Department of Forest Biomaterials and Technology, Swedish University of Agricultural Sciences
    

• Ersson, Back Tomas

  • School for Forest Management, Swedish University of Agricultural Sciences
    

Sustainable Development Goals

Build resilient infrastructure, promote inclusive and sustainable industrialization and foster innovation


UKÄ Subject classification

Forest Science


Publication identifier

DOI: https://doi.org/10.17221/23/2019-JFS

Permanent link to this page (URI)

https://res.slu.se/id/publ/101571