Modeling Fuel Consumption of Heavy-duty Trucks Using Telematics Data.
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| Title: | Modeling Fuel Consumption of Heavy-duty Trucks Using Telematics Data. |
|---|---|
| Authors: | Nariendra, Pradhana Wahyu1 pradhananariendra@gmail.com, Santosa, Wimpy1, Sutandi, Anastasia Caroline1 |
| Source: | Periodica Polytechnica: Transportation Engineering. 2026, Vol. 54 Issue 1, p41-48. 8p. |
| Subjects: | Heavy duty trucks, Automotive telematics, Energy consumption in transportation, Multiple regression analysis, Energy consumption, Operating costs, Greenhouse gases, Linear statistical models |
| Geographic Terms: | Indonesia |
| Abstract: | Fuel is the largest cost component in Vehicle Operating Costs (VOC) and a significant contributor to greenhouse gas (GHG) emissions in the trucking sector. This study developed a real-time telematics-based fuel consumption model for Euro-3 and Euro-4 trucks operating on toll roads in Indonesia, focusing on 5-axle heavy-duty trucks. The model utilizes telematics data, including average speed, gross vehicle weight, and road gradient under free-flow conditions, a novel aspect of this research. Two modeling approaches were applied: Model 1 employed multiple linear regression with Box-Cox transformation, while Model 2 utilized Generalized Linear Models (GLM) with a Gamma distribution and log link. Model 1 performed better, explaining 85.8% of the variability in fuel consumption (adjusted R2 = 0.858) with a deviance of 0.947, RMSE of 0.033, and AIC of -3.246.625. Conversely, Model 2 recorded a deviance of 8.827, RMSE of 0.296, and AIC of -2.483. The Wilcoxon Signed Ranks Test indicated no significant differences between predicted and observed fuel consumption for both truck types, with a Z value of -1.700 (p = 0.089) for Euro-4 and -0.038 (p = 0.970) for Euro-3, supporting the model′s reliability. Beyond optimizing fuel consumption, the model offers practical recommendations for truck operators considering conversion to Euro-4 and provides valuable insights for policymakers developing energy efficiency strategies in the transportation sector. Further research is recommended to expand the model′s application to non-toll routes and integrate machine learning for more complex patterns. [ABSTRACT FROM AUTHOR] |
| Copyright of Periodica Polytechnica: Transportation Engineering is the property of Periodica Polytechnica and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Links: – Type: pdflink Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 191821052 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Modeling Fuel Consumption of Heavy-duty Trucks Using Telematics Data. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Nariendra%2C+Pradhana+Wahyu%22">Nariendra, Pradhana Wahyu</searchLink><relatesTo>1</relatesTo><i> pradhananariendra@gmail.com</i><br /><searchLink fieldCode="AR" term="%22Santosa%2C+Wimpy%22">Santosa, Wimpy</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Sutandi%2C+Anastasia+Caroline%22">Sutandi, Anastasia Caroline</searchLink><relatesTo>1</relatesTo> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Periodica+Polytechnica%3A+Transportation+Engineering%22">Periodica Polytechnica: Transportation Engineering</searchLink>. 2026, Vol. 54 Issue 1, p41-48. 8p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Heavy+duty+trucks%22">Heavy duty trucks</searchLink><br /><searchLink fieldCode="DE" term="%22Automotive+telematics%22">Automotive telematics</searchLink><br /><searchLink fieldCode="DE" term="%22Energy+consumption+in+transportation%22">Energy consumption in transportation</searchLink><br /><searchLink fieldCode="DE" term="%22Multiple+regression+analysis%22">Multiple regression analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Energy+consumption%22">Energy consumption</searchLink><br /><searchLink fieldCode="DE" term="%22Operating+costs%22">Operating costs</searchLink><br /><searchLink fieldCode="DE" term="%22Greenhouse+gases%22">Greenhouse gases</searchLink><br /><searchLink fieldCode="DE" term="%22Linear+statistical+models%22">Linear statistical models</searchLink> – Name: SubjectGeographic Label: Geographic Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Indonesia%22">Indonesia</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Fuel is the largest cost component in Vehicle Operating Costs (VOC) and a significant contributor to greenhouse gas (GHG) emissions in the trucking sector. This study developed a real-time telematics-based fuel consumption model for Euro-3 and Euro-4 trucks operating on toll roads in Indonesia, focusing on 5-axle heavy-duty trucks. The model utilizes telematics data, including average speed, gross vehicle weight, and road gradient under free-flow conditions, a novel aspect of this research. Two modeling approaches were applied: Model 1 employed multiple linear regression with Box-Cox transformation, while Model 2 utilized Generalized Linear Models (GLM) with a Gamma distribution and log link. Model 1 performed better, explaining 85.8% of the variability in fuel consumption (adjusted R2 = 0.858) with a deviance of 0.947, RMSE of 0.033, and AIC of -3.246.625. Conversely, Model 2 recorded a deviance of 8.827, RMSE of 0.296, and AIC of -2.483. The Wilcoxon Signed Ranks Test indicated no significant differences between predicted and observed fuel consumption for both truck types, with a Z value of -1.700 (p = 0.089) for Euro-4 and -0.038 (p = 0.970) for Euro-3, supporting the model′s reliability. Beyond optimizing fuel consumption, the model offers practical recommendations for truck operators considering conversion to Euro-4 and provides valuable insights for policymakers developing energy efficiency strategies in the transportation sector. Further research is recommended to expand the model′s application to non-toll routes and integrate machine learning for more complex patterns. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Periodica Polytechnica: Transportation Engineering is the property of Periodica Polytechnica and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.3311/PPtr.38337 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 8 StartPage: 41 Subjects: – SubjectFull: Heavy duty trucks Type: general – SubjectFull: Automotive telematics Type: general – SubjectFull: Energy consumption in transportation Type: general – SubjectFull: Multiple regression analysis Type: general – SubjectFull: Energy consumption Type: general – SubjectFull: Operating costs Type: general – SubjectFull: Greenhouse gases Type: general – SubjectFull: Linear statistical models Type: general – SubjectFull: Indonesia Type: general Titles: – TitleFull: Modeling Fuel Consumption of Heavy-duty Trucks Using Telematics Data. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Nariendra, Pradhana Wahyu – PersonEntity: Name: NameFull: Santosa, Wimpy – PersonEntity: Name: NameFull: Sutandi, Anastasia Caroline IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 01 Text: 2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 03037800 Numbering: – Type: volume Value: 54 – Type: issue Value: 1 Titles: – TitleFull: Periodica Polytechnica: Transportation Engineering Type: main |
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