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

This study evaluated fuel consumption and CO2 emissions for 13 typical log trucks in operating conditions in Finland. The effects of season, transportation distance, mass, vehicle and road properties, and weather conditions on fuel consumption for driving were analyzed and modeled. The average fuel...
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Autor*in:	Perttu Anttila [verfasserIn] Tuomas Nummelin [verfasserIn] Kari Väätäinen [verfasserIn] Juha Laitila [verfasserIn] Jari Ala-Ilomäki [verfasserIn] Antti Kilpeläinen [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	Log truck Fuel economy Greenhouse gas emissions CAN bus Machine learning

Übergeordnetes Werk:	In: Transportation Research Interdisciplinary Perspectives - Elsevier, 2020, 15(2022), Seite 100671-
Übergeordnetes Werk:	volume:15 ; year:2022 ; pages:100671-

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1016/j.trip.2022.100671

Katalog-ID:	DOAJ022692436

Internformat


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520			\|a This study evaluated fuel consumption and CO2 emissions for 13 typical log trucks in operating conditions in Finland. The effects of season, transportation distance, mass, vehicle and road properties, and weather conditions on fuel consumption for driving were analyzed and modeled. The average fuel consumption and CO2 emission of the 76-t trucks when driving with a load was 0.013 l(t·km)−1 and 30.856 g(t·km)−1 respectively. The consumptions and emissions for the 68- and 76-tonners were found to be at the same level per tonne kilometer due to the overload of the former. The highest consumptions were measured in January (on average 57.5 l(100 km)−1), and the lowest in July (on average 48.7 l(100 km)−1). Machine learning was applied to predict fuel consumption with the above-mentioned factors for 73,686 road segments. Based on the developed models, driving speed was the most influential explanatory variable, in addition to road gradient, pavement, and sinuosity. Engine power and truck mass had minor importance. Wind effect was the only significant weather variable. The “big data” approach, as used in this study, enables the collection of a vast amount of data on very varying conditions in log transportation. However, higher resolution data than the fleet management system data used in this study will be needed to construct more accurate models.
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650		4	\|a Fuel economy
650		4	\|a Greenhouse gas emissions
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700	0		\|a Tuomas Nummelin \|e verfasserin \|4 aut
700	0		\|a Kari Väätäinen \|e verfasserin \|4 aut
700	0		\|a Juha Laitila \|e verfasserin \|4 aut
700	0		\|a Jari Ala-Ilomäki \|e verfasserin \|4 aut
700	0		\|a Antti Kilpeläinen \|e verfasserin \|4 aut
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allfields	10.1016/j.trip.2022.100671 doi (DE-627)DOAJ022692436 (DE-599)DOAJ917746e6060c44518e0fc86b65e9163e DE-627 ger DE-627 rakwb eng HE1-9990 Perttu Anttila verfasserin aut Effect of vehicle properties and driving environment on fuel consumption and CO2 emissions of timber trucking based on data from fleet management system 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This study evaluated fuel consumption and CO2 emissions for 13 typical log trucks in operating conditions in Finland. The effects of season, transportation distance, mass, vehicle and road properties, and weather conditions on fuel consumption for driving were analyzed and modeled. The average fuel consumption and CO2 emission of the 76-t trucks when driving with a load was 0.013 l(t·km)−1 and 30.856 g(t·km)−1 respectively. The consumptions and emissions for the 68- and 76-tonners were found to be at the same level per tonne kilometer due to the overload of the former. The highest consumptions were measured in January (on average 57.5 l(100 km)−1), and the lowest in July (on average 48.7 l(100 km)−1). Machine learning was applied to predict fuel consumption with the above-mentioned factors for 73,686 road segments. Based on the developed models, driving speed was the most influential explanatory variable, in addition to road gradient, pavement, and sinuosity. Engine power and truck mass had minor importance. Wind effect was the only significant weather variable. The “big data” approach, as used in this study, enables the collection of a vast amount of data on very varying conditions in log transportation. However, higher resolution data than the fleet management system data used in this study will be needed to construct more accurate models. Log truck Fuel economy Greenhouse gas emissions CAN bus Machine learning Transportation and communications Tuomas Nummelin verfasserin aut Kari Väätäinen verfasserin aut Juha Laitila verfasserin aut Jari Ala-Ilomäki verfasserin aut Antti Kilpeläinen verfasserin aut In Transportation Research Interdisciplinary Perspectives Elsevier, 2020 15(2022), Seite 100671- (DE-627)1690634936 25901982 nnns volume:15 year:2022 pages:100671- https://doi.org/10.1016/j.trip.2022.100671 kostenfrei https://doaj.org/article/917746e6060c44518e0fc86b65e9163e kostenfrei http://www.sciencedirect.com/science/article/pii/S2590198222001312 kostenfrei https://doaj.org/toc/2590-1982 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4392 GBV_ILN_4393 GBV_ILN_4700 AR 15 2022 100671-
spelling	10.1016/j.trip.2022.100671 doi (DE-627)DOAJ022692436 (DE-599)DOAJ917746e6060c44518e0fc86b65e9163e DE-627 ger DE-627 rakwb eng HE1-9990 Perttu Anttila verfasserin aut Effect of vehicle properties and driving environment on fuel consumption and CO2 emissions of timber trucking based on data from fleet management system 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This study evaluated fuel consumption and CO2 emissions for 13 typical log trucks in operating conditions in Finland. The effects of season, transportation distance, mass, vehicle and road properties, and weather conditions on fuel consumption for driving were analyzed and modeled. The average fuel consumption and CO2 emission of the 76-t trucks when driving with a load was 0.013 l(t·km)−1 and 30.856 g(t·km)−1 respectively. The consumptions and emissions for the 68- and 76-tonners were found to be at the same level per tonne kilometer due to the overload of the former. The highest consumptions were measured in January (on average 57.5 l(100 km)−1), and the lowest in July (on average 48.7 l(100 km)−1). Machine learning was applied to predict fuel consumption with the above-mentioned factors for 73,686 road segments. Based on the developed models, driving speed was the most influential explanatory variable, in addition to road gradient, pavement, and sinuosity. Engine power and truck mass had minor importance. Wind effect was the only significant weather variable. The “big data” approach, as used in this study, enables the collection of a vast amount of data on very varying conditions in log transportation. However, higher resolution data than the fleet management system data used in this study will be needed to construct more accurate models. Log truck Fuel economy Greenhouse gas emissions CAN bus Machine learning Transportation and communications Tuomas Nummelin verfasserin aut Kari Väätäinen verfasserin aut Juha Laitila verfasserin aut Jari Ala-Ilomäki verfasserin aut Antti Kilpeläinen verfasserin aut In Transportation Research Interdisciplinary Perspectives Elsevier, 2020 15(2022), Seite 100671- (DE-627)1690634936 25901982 nnns volume:15 year:2022 pages:100671- https://doi.org/10.1016/j.trip.2022.100671 kostenfrei https://doaj.org/article/917746e6060c44518e0fc86b65e9163e kostenfrei http://www.sciencedirect.com/science/article/pii/S2590198222001312 kostenfrei https://doaj.org/toc/2590-1982 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4392 GBV_ILN_4393 GBV_ILN_4700 AR 15 2022 100671-
allfields_unstemmed	10.1016/j.trip.2022.100671 doi (DE-627)DOAJ022692436 (DE-599)DOAJ917746e6060c44518e0fc86b65e9163e DE-627 ger DE-627 rakwb eng HE1-9990 Perttu Anttila verfasserin aut Effect of vehicle properties and driving environment on fuel consumption and CO2 emissions of timber trucking based on data from fleet management system 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This study evaluated fuel consumption and CO2 emissions for 13 typical log trucks in operating conditions in Finland. The effects of season, transportation distance, mass, vehicle and road properties, and weather conditions on fuel consumption for driving were analyzed and modeled. The average fuel consumption and CO2 emission of the 76-t trucks when driving with a load was 0.013 l(t·km)−1 and 30.856 g(t·km)−1 respectively. The consumptions and emissions for the 68- and 76-tonners were found to be at the same level per tonne kilometer due to the overload of the former. The highest consumptions were measured in January (on average 57.5 l(100 km)−1), and the lowest in July (on average 48.7 l(100 km)−1). Machine learning was applied to predict fuel consumption with the above-mentioned factors for 73,686 road segments. Based on the developed models, driving speed was the most influential explanatory variable, in addition to road gradient, pavement, and sinuosity. Engine power and truck mass had minor importance. Wind effect was the only significant weather variable. The “big data” approach, as used in this study, enables the collection of a vast amount of data on very varying conditions in log transportation. However, higher resolution data than the fleet management system data used in this study will be needed to construct more accurate models. Log truck Fuel economy Greenhouse gas emissions CAN bus Machine learning Transportation and communications Tuomas Nummelin verfasserin aut Kari Väätäinen verfasserin aut Juha Laitila verfasserin aut Jari Ala-Ilomäki verfasserin aut Antti Kilpeläinen verfasserin aut In Transportation Research Interdisciplinary Perspectives Elsevier, 2020 15(2022), Seite 100671- (DE-627)1690634936 25901982 nnns volume:15 year:2022 pages:100671- https://doi.org/10.1016/j.trip.2022.100671 kostenfrei https://doaj.org/article/917746e6060c44518e0fc86b65e9163e kostenfrei http://www.sciencedirect.com/science/article/pii/S2590198222001312 kostenfrei https://doaj.org/toc/2590-1982 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4392 GBV_ILN_4393 GBV_ILN_4700 AR 15 2022 100671-
allfieldsGer	10.1016/j.trip.2022.100671 doi (DE-627)DOAJ022692436 (DE-599)DOAJ917746e6060c44518e0fc86b65e9163e DE-627 ger DE-627 rakwb eng HE1-9990 Perttu Anttila verfasserin aut Effect of vehicle properties and driving environment on fuel consumption and CO2 emissions of timber trucking based on data from fleet management system 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This study evaluated fuel consumption and CO2 emissions for 13 typical log trucks in operating conditions in Finland. The effects of season, transportation distance, mass, vehicle and road properties, and weather conditions on fuel consumption for driving were analyzed and modeled. The average fuel consumption and CO2 emission of the 76-t trucks when driving with a load was 0.013 l(t·km)−1 and 30.856 g(t·km)−1 respectively. The consumptions and emissions for the 68- and 76-tonners were found to be at the same level per tonne kilometer due to the overload of the former. The highest consumptions were measured in January (on average 57.5 l(100 km)−1), and the lowest in July (on average 48.7 l(100 km)−1). Machine learning was applied to predict fuel consumption with the above-mentioned factors for 73,686 road segments. Based on the developed models, driving speed was the most influential explanatory variable, in addition to road gradient, pavement, and sinuosity. Engine power and truck mass had minor importance. Wind effect was the only significant weather variable. The “big data” approach, as used in this study, enables the collection of a vast amount of data on very varying conditions in log transportation. However, higher resolution data than the fleet management system data used in this study will be needed to construct more accurate models. Log truck Fuel economy Greenhouse gas emissions CAN bus Machine learning Transportation and communications Tuomas Nummelin verfasserin aut Kari Väätäinen verfasserin aut Juha Laitila verfasserin aut Jari Ala-Ilomäki verfasserin aut Antti Kilpeläinen verfasserin aut In Transportation Research Interdisciplinary Perspectives Elsevier, 2020 15(2022), Seite 100671- (DE-627)1690634936 25901982 nnns volume:15 year:2022 pages:100671- https://doi.org/10.1016/j.trip.2022.100671 kostenfrei https://doaj.org/article/917746e6060c44518e0fc86b65e9163e kostenfrei http://www.sciencedirect.com/science/article/pii/S2590198222001312 kostenfrei https://doaj.org/toc/2590-1982 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4392 GBV_ILN_4393 GBV_ILN_4700 AR 15 2022 100671-
allfieldsSound	10.1016/j.trip.2022.100671 doi (DE-627)DOAJ022692436 (DE-599)DOAJ917746e6060c44518e0fc86b65e9163e DE-627 ger DE-627 rakwb eng HE1-9990 Perttu Anttila verfasserin aut Effect of vehicle properties and driving environment on fuel consumption and CO2 emissions of timber trucking based on data from fleet management system 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This study evaluated fuel consumption and CO2 emissions for 13 typical log trucks in operating conditions in Finland. The effects of season, transportation distance, mass, vehicle and road properties, and weather conditions on fuel consumption for driving were analyzed and modeled. The average fuel consumption and CO2 emission of the 76-t trucks when driving with a load was 0.013 l(t·km)−1 and 30.856 g(t·km)−1 respectively. The consumptions and emissions for the 68- and 76-tonners were found to be at the same level per tonne kilometer due to the overload of the former. The highest consumptions were measured in January (on average 57.5 l(100 km)−1), and the lowest in July (on average 48.7 l(100 km)−1). Machine learning was applied to predict fuel consumption with the above-mentioned factors for 73,686 road segments. Based on the developed models, driving speed was the most influential explanatory variable, in addition to road gradient, pavement, and sinuosity. Engine power and truck mass had minor importance. Wind effect was the only significant weather variable. The “big data” approach, as used in this study, enables the collection of a vast amount of data on very varying conditions in log transportation. However, higher resolution data than the fleet management system data used in this study will be needed to construct more accurate models. Log truck Fuel economy Greenhouse gas emissions CAN bus Machine learning Transportation and communications Tuomas Nummelin verfasserin aut Kari Väätäinen verfasserin aut Juha Laitila verfasserin aut Jari Ala-Ilomäki verfasserin aut Antti Kilpeläinen verfasserin aut In Transportation Research Interdisciplinary Perspectives Elsevier, 2020 15(2022), Seite 100671- (DE-627)1690634936 25901982 nnns volume:15 year:2022 pages:100671- https://doi.org/10.1016/j.trip.2022.100671 kostenfrei https://doaj.org/article/917746e6060c44518e0fc86b65e9163e kostenfrei http://www.sciencedirect.com/science/article/pii/S2590198222001312 kostenfrei https://doaj.org/toc/2590-1982 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4392 GBV_ILN_4393 GBV_ILN_4700 AR 15 2022 100671-
language	English
source	In Transportation Research Interdisciplinary Perspectives 15(2022), Seite 100671- volume:15 year:2022 pages:100671-
sourceStr	In Transportation Research Interdisciplinary Perspectives 15(2022), Seite 100671- volume:15 year:2022 pages:100671-
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Log truck Fuel economy Greenhouse gas emissions CAN bus Machine learning Transportation and communications
isfreeaccess_bool	true
container_title	Transportation Research Interdisciplinary Perspectives
authorswithroles_txt_mv	Perttu Anttila @@aut@@ Tuomas Nummelin @@aut@@ Kari Väätäinen @@aut@@ Juha Laitila @@aut@@ Jari Ala-Ilomäki @@aut@@ Antti Kilpeläinen @@aut@@
publishDateDaySort_date	2022-01-01T00:00:00Z
hierarchy_top_id	1690634936
id	DOAJ022692436
language_de	englisch
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The effects of season, transportation distance, mass, vehicle and road properties, and weather conditions on fuel consumption for driving were analyzed and modeled. The average fuel consumption and CO2 emission of the 76-t trucks when driving with a load was 0.013 l(t·km)−1 and 30.856 g(t·km)−1 respectively. The consumptions and emissions for the 68- and 76-tonners were found to be at the same level per tonne kilometer due to the overload of the former. The highest consumptions were measured in January (on average 57.5 l(100 km)−1), and the lowest in July (on average 48.7 l(100 km)−1). Machine learning was applied to predict fuel consumption with the above-mentioned factors for 73,686 road segments. Based on the developed models, driving speed was the most influential explanatory variable, in addition to road gradient, pavement, and sinuosity. Engine power and truck mass had minor importance. Wind effect was the only significant weather variable. 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callnumber-first	H - Social Science
author	Perttu Anttila
spellingShingle	Perttu Anttila misc HE1-9990 misc Log truck misc Fuel economy misc Greenhouse gas emissions misc CAN bus misc Machine learning misc Transportation and communications Effect of vehicle properties and driving environment on fuel consumption and CO2 emissions of timber trucking based on data from fleet management system
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topic_title	HE1-9990 Effect of vehicle properties and driving environment on fuel consumption and CO2 emissions of timber trucking based on data from fleet management system Log truck Fuel economy Greenhouse gas emissions CAN bus Machine learning
topic	misc HE1-9990 misc Log truck misc Fuel economy misc Greenhouse gas emissions misc CAN bus misc Machine learning misc Transportation and communications
topic_unstemmed	misc HE1-9990 misc Log truck misc Fuel economy misc Greenhouse gas emissions misc CAN bus misc Machine learning misc Transportation and communications
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title	Effect of vehicle properties and driving environment on fuel consumption and CO2 emissions of timber trucking based on data from fleet management system
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title_full	Effect of vehicle properties and driving environment on fuel consumption and CO2 emissions of timber trucking based on data from fleet management system
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title_sort	effect of vehicle properties and driving environment on fuel consumption and co2 emissions of timber trucking based on data from fleet management system
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title_auth	Effect of vehicle properties and driving environment on fuel consumption and CO2 emissions of timber trucking based on data from fleet management system
abstract	This study evaluated fuel consumption and CO2 emissions for 13 typical log trucks in operating conditions in Finland. The effects of season, transportation distance, mass, vehicle and road properties, and weather conditions on fuel consumption for driving were analyzed and modeled. The average fuel consumption and CO2 emission of the 76-t trucks when driving with a load was 0.013 l(t·km)−1 and 30.856 g(t·km)−1 respectively. The consumptions and emissions for the 68- and 76-tonners were found to be at the same level per tonne kilometer due to the overload of the former. The highest consumptions were measured in January (on average 57.5 l(100 km)−1), and the lowest in July (on average 48.7 l(100 km)−1). Machine learning was applied to predict fuel consumption with the above-mentioned factors for 73,686 road segments. Based on the developed models, driving speed was the most influential explanatory variable, in addition to road gradient, pavement, and sinuosity. Engine power and truck mass had minor importance. Wind effect was the only significant weather variable. The “big data” approach, as used in this study, enables the collection of a vast amount of data on very varying conditions in log transportation. However, higher resolution data than the fleet management system data used in this study will be needed to construct more accurate models.
abstractGer	This study evaluated fuel consumption and CO2 emissions for 13 typical log trucks in operating conditions in Finland. The effects of season, transportation distance, mass, vehicle and road properties, and weather conditions on fuel consumption for driving were analyzed and modeled. The average fuel consumption and CO2 emission of the 76-t trucks when driving with a load was 0.013 l(t·km)−1 and 30.856 g(t·km)−1 respectively. The consumptions and emissions for the 68- and 76-tonners were found to be at the same level per tonne kilometer due to the overload of the former. The highest consumptions were measured in January (on average 57.5 l(100 km)−1), and the lowest in July (on average 48.7 l(100 km)−1). Machine learning was applied to predict fuel consumption with the above-mentioned factors for 73,686 road segments. Based on the developed models, driving speed was the most influential explanatory variable, in addition to road gradient, pavement, and sinuosity. Engine power and truck mass had minor importance. Wind effect was the only significant weather variable. The “big data” approach, as used in this study, enables the collection of a vast amount of data on very varying conditions in log transportation. However, higher resolution data than the fleet management system data used in this study will be needed to construct more accurate models.
abstract_unstemmed	This study evaluated fuel consumption and CO2 emissions for 13 typical log trucks in operating conditions in Finland. The effects of season, transportation distance, mass, vehicle and road properties, and weather conditions on fuel consumption for driving were analyzed and modeled. The average fuel consumption and CO2 emission of the 76-t trucks when driving with a load was 0.013 l(t·km)−1 and 30.856 g(t·km)−1 respectively. The consumptions and emissions for the 68- and 76-tonners were found to be at the same level per tonne kilometer due to the overload of the former. The highest consumptions were measured in January (on average 57.5 l(100 km)−1), and the lowest in July (on average 48.7 l(100 km)−1). Machine learning was applied to predict fuel consumption with the above-mentioned factors for 73,686 road segments. Based on the developed models, driving speed was the most influential explanatory variable, in addition to road gradient, pavement, and sinuosity. Engine power and truck mass had minor importance. Wind effect was the only significant weather variable. The “big data” approach, as used in this study, enables the collection of a vast amount of data on very varying conditions in log transportation. However, higher resolution data than the fleet management system data used in this study will be needed to construct more accurate models.
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title_short	Effect of vehicle properties and driving environment on fuel consumption and CO2 emissions of timber trucking based on data from fleet management system
url	https://doi.org/10.1016/j.trip.2022.100671 https://doaj.org/article/917746e6060c44518e0fc86b65e9163e http://www.sciencedirect.com/science/article/pii/S2590198222001312 https://doaj.org/toc/2590-1982
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Effect of vehicle properties and driving environment on fuel consumption and CO2 emissions of timber trucking based on data from fleet management system

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