A big data approach for investigating the performance of road infrastructure
“Using truck sensors for road pavement performance investigation” is a research project within TRUSS, an innovative training network funded from the EU under the Horizon 2020 programme. The project aims at assessing the impact of the condition of the road pavement unevenness and macrotexture, on the...
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| Format: | Conference or Workshop Item |
| Language: | English |
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2018
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| Online Access: | https://eprints.nottingham.ac.uk/53360/ |
| _version_ | 1848798926446002176 |
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| author | Perrotta, Federico Parry, Tony Neves, Luís C. Mesgarpour, Mohammad Benbow, Emma Viner, Helen |
| author_facet | Perrotta, Federico Parry, Tony Neves, Luís C. Mesgarpour, Mohammad Benbow, Emma Viner, Helen |
| author_sort | Perrotta, Federico |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | “Using truck sensors for road pavement performance investigation” is a research project within TRUSS, an innovative training network funded from the EU under the Horizon 2020 programme. The project aims at assessing the impact of the condition of the road pavement unevenness and macrotexture, on the fuel consumption of trucks to reduce uncertainty in the framework of life-cycle assessment of road pavements.
In the past, several studies claimed that a road pavement in poor condition can affect the fuel consumption of road vehicles. However, these conclusions are based just on tests performed on a selection of road segments using a few vehicles and this may not be representative of real conditions. That leaves uncertainty in the topic and it does not allow road mangers to review the current road maintenance strategies that could otherwise help in reducing costs and greenhouse gas emissions from the road transport industry.
The project investigated an alternative approach that considers large quantities of data from standard sensors installed on trucks combined with information in the database of road agencies that includes measurements of the conditions of the road network. In particular, using advanced regression techniques, a fuel consumption model that can take into consideration these effects has been developed.
The paper presents a summary of the findings of the project, it highlights implications for road asset management and the road maintenance strategies and discusses advantages and limitations of the approach used, pointing out possible improvements and future work. |
| first_indexed | 2025-11-14T20:27:32Z |
| format | Conference or Workshop Item |
| id | nottingham-53360 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-14T20:27:32Z |
| publishDate | 2018 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-533602018-10-08T10:47:59Z https://eprints.nottingham.ac.uk/53360/ A big data approach for investigating the performance of road infrastructure Perrotta, Federico Parry, Tony Neves, Luís C. Mesgarpour, Mohammad Benbow, Emma Viner, Helen “Using truck sensors for road pavement performance investigation” is a research project within TRUSS, an innovative training network funded from the EU under the Horizon 2020 programme. The project aims at assessing the impact of the condition of the road pavement unevenness and macrotexture, on the fuel consumption of trucks to reduce uncertainty in the framework of life-cycle assessment of road pavements. In the past, several studies claimed that a road pavement in poor condition can affect the fuel consumption of road vehicles. However, these conclusions are based just on tests performed on a selection of road segments using a few vehicles and this may not be representative of real conditions. That leaves uncertainty in the topic and it does not allow road mangers to review the current road maintenance strategies that could otherwise help in reducing costs and greenhouse gas emissions from the road transport industry. The project investigated an alternative approach that considers large quantities of data from standard sensors installed on trucks combined with information in the database of road agencies that includes measurements of the conditions of the road network. In particular, using advanced regression techniques, a fuel consumption model that can take into consideration these effects has been developed. The paper presents a summary of the findings of the project, it highlights implications for road asset management and the road maintenance strategies and discusses advantages and limitations of the approach used, pointing out possible improvements and future work. 2018-08-29 Conference or Workshop Item PeerReviewed application/pdf en https://eprints.nottingham.ac.uk/53360/1/Perrotta%20et%20al%20%282018%29%20CERI2018%20-%20A%20Big%20Data%20approach%20for%20investigating%20the%20performance%20of%20the%20road%20infrastructure.pdf Perrotta, Federico, Parry, Tony, Neves, Luís C., Mesgarpour, Mohammad, Benbow, Emma and Viner, Helen (2018) A big data approach for investigating the performance of road infrastructure. In: Civil Engineering Research in Ireland, CERI 2018, 29-30 August 2018, Dublin, Ireland. Fuel Consumption; Road Performance Evaluation; Big Data Analysis; TRUSS ITN |
| spellingShingle | Fuel Consumption; Road Performance Evaluation; Big Data Analysis; TRUSS ITN Perrotta, Federico Parry, Tony Neves, Luís C. Mesgarpour, Mohammad Benbow, Emma Viner, Helen A big data approach for investigating the performance of road infrastructure |
| title | A big data approach for investigating the performance of road infrastructure |
| title_full | A big data approach for investigating the performance of road infrastructure |
| title_fullStr | A big data approach for investigating the performance of road infrastructure |
| title_full_unstemmed | A big data approach for investigating the performance of road infrastructure |
| title_short | A big data approach for investigating the performance of road infrastructure |
| title_sort | big data approach for investigating the performance of road infrastructure |
| topic | Fuel Consumption; Road Performance Evaluation; Big Data Analysis; TRUSS ITN |
| url | https://eprints.nottingham.ac.uk/53360/ |