Using truck sensors for road pavement performance investigation
Considering data from 260 articulated trucks, with ~12900 cc Euro 6 engines driving along a motorway in England (M18), the study first shows how different approaches lead to the conclusion that road pavement surface conditions influence fuel consumption of the considered truck fleet. Then, a multipl...
| Main Authors: | , , |
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| Format: | Conference or Workshop Item |
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2017
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| Online Access: | https://eprints.nottingham.ac.uk/43787/ |
| _version_ | 1848796768649609216 |
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| author | Perrotta, Federico Parry, Tony Neves, Luís C. |
| author_facet | Perrotta, Federico Parry, Tony Neves, Luís C. |
| author_sort | Perrotta, Federico |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | Considering data from 260 articulated trucks, with ~12900 cc Euro 6 engines driving along a motorway in England (M18), the study first shows how different approaches lead to the conclusion that road pavement surface conditions influence fuel consumption of the considered truck fleet. Then, a multiple linear regression for the prediction of fuel consumption was generated. The model shows that evenness and macrotexture can impact the truck fuel consumption by up to 3% and 5%, respectively. It is a significant impact which confirms that, although the available funding for pavement maintenance is limited, the importance of limiting GHG emissions, together with the economic benefits of reducing fuel consumption are reasons to improve road condition (Zaabar & Chatti, 2010). |
| first_indexed | 2025-11-14T19:53:14Z |
| format | Conference or Workshop Item |
| id | nottingham-43787 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| last_indexed | 2025-11-14T19:53:14Z |
| publishDate | 2017 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-437872020-05-04T18:47:07Z https://eprints.nottingham.ac.uk/43787/ Using truck sensors for road pavement performance investigation Perrotta, Federico Parry, Tony Neves, Luís C. Considering data from 260 articulated trucks, with ~12900 cc Euro 6 engines driving along a motorway in England (M18), the study first shows how different approaches lead to the conclusion that road pavement surface conditions influence fuel consumption of the considered truck fleet. Then, a multiple linear regression for the prediction of fuel consumption was generated. The model shows that evenness and macrotexture can impact the truck fuel consumption by up to 3% and 5%, respectively. It is a significant impact which confirms that, although the available funding for pavement maintenance is limited, the importance of limiting GHG emissions, together with the economic benefits of reducing fuel consumption are reasons to improve road condition (Zaabar & Chatti, 2010). 2017-05-25 Conference or Workshop Item PeerReviewed Perrotta, Federico, Parry, Tony and Neves, Luís C. (2017) Using truck sensors for road pavement performance investigation. In: ESREL 2017, 19-22 Jun 2017, Portoroz, Slovenia. Fuel Consumption Fuel Economy Road Conditions Roughness Evenness Macro-texture Fleet Management Asset Management https://www.crcpress.com/ESREL-2017-Portoroz-Slovenia-18-22-June-2017/Cepin-Bris/p/book/9781138629370 |
| spellingShingle | Fuel Consumption Fuel Economy Road Conditions Roughness Evenness Macro-texture Fleet Management Asset Management Perrotta, Federico Parry, Tony Neves, Luís C. Using truck sensors for road pavement performance investigation |
| title | Using truck sensors for road pavement performance investigation |
| title_full | Using truck sensors for road pavement performance investigation |
| title_fullStr | Using truck sensors for road pavement performance investigation |
| title_full_unstemmed | Using truck sensors for road pavement performance investigation |
| title_short | Using truck sensors for road pavement performance investigation |
| title_sort | using truck sensors for road pavement performance investigation |
| topic | Fuel Consumption Fuel Economy Road Conditions Roughness Evenness Macro-texture Fleet Management Asset Management |
| url | https://eprints.nottingham.ac.uk/43787/ https://eprints.nottingham.ac.uk/43787/ |