Effect of vehicle properties and driving environment on fuel consumption and CO2 emissions of timber trucking based on data from fleet management system

Anttila, Perttu; Nummelin, Tuomas; Väätäinen, Kari; Laitila, Juha; Ala‐Ilomäki, Jari; Kilpeläinen, Antti

doi:10.1016/j.trip.2022.100671

Cited by 10 publications

(13 citation statements)

References 8 publications

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“…1). Data about 13 timber trucks were collected between April 2018 and May 2019 (Anttila et al 2022). During the follow-up period, the odometer reading, cumulative fuel consumption, coordinates, and timestamp were recorded at approximately 10-minute intervals, except for one truck, which had an interval of approximately 1 minute.…”

Section: Methodsmentioning

confidence: 99%

“…With stricter EU-level greenhouse gas emission reduction targets, a deeper understanding of the factors affecting the driving speed and fuel consumption of timber trucking is indispensable. Anttila et al (2022) divided these factors related to the vehicle, driver, and driving environment. For example, maximum engine output, and the number and dimension of tyres are vehicle-dependent factors affecting fuel consumption (Almer 2015;Walnum and Simonsen 2015;Bousonville et al 2019).…”

Section: Introductionmentioning

confidence: 99%

“…Road geometry and surface roughness are known to affect both driving speed and fuel consumption (Svenson and Fjeld 2016;Perrotta et al 2017;Svenson and Fjeld 2017;Bousonville et al 2019;Anttila et al 2022). Road databases contain information on road classes or pavement types.…”

Section: Introductionmentioning

confidence: 99%

“…In the spring, the thaw -and increasingly bad weather conditions in the autumn -have the same effect on speed and consumption. Weather variables or season have therefore been used as explanatory variables (Almer 2015;Walnum and Simonsen 2015;Bousonville et al 2019;Anttila et al 2022). Nurminen and Heinonen (2007) created time consumption models for trucking activities in Finland.…”

Section: Introductionmentioning

confidence: 99%

“…However, in two example transport cases, the high-end estimate of the 95% confidence interval was twice the low-end estimate, indicating that considerable variation remains that cannot be explained by work phase and transport distance alone. Later, Anttila et al (2022) tested several explanatory variables to explain timber truck fuel consumption. The study utilised data from a timber truck fleet management system (FMS) combined with road and weather data.…”

Section: Introductionmentioning

confidence: 99%

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The effect of road characteristics on timber truck driving speed and fuel consumption based on visual interpretation of road database and data from fleet management system

Anttila¹,

Ojala²,

Palander³

et al. 2023

Silva Fenn

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Road transport produces 90% of greenhouse gas emissions in timber transport in Finland. It is therefore necessary to understand the factors that affect driving speed, fuel consumption, and ultimately, emissions. The objective of this study was to assess the effect of road characteristics on timber truck driving speed and fuel consumption. Data from the fleet management and transport management systems of two timber trucks were collected over a year. A sample of 104 trips was drawn, and the tracking points were overlaid on the road data in a geographical information system. Thereafter, work phases were determined for the points, and they were visually classified into road and pavement classes. Subsequently, the data of 80 trips were utilised in regression analysis to further study the effects of the visually interpreted variables on driving speed and fuel consumption. Fuel consumption was explained by the proportion of forest roads and distance travelled with a loader, and the number of crossings and season when driving without a load. When driving with a load, both asphalt and gravel pavements decreased consumption, in contrast to an unpaved road. Crossings increased fuel consumption, as did the winter and spring months, and the total laden mass of the truck. In conclusion, the study showed that the functional Finnish road and pavement classes can be used to predict driving speed and fuel consumption.

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Section: Methodsmentioning

confidence: 99%