A big data approach to assess the influence of road pavement condition on truck fleet fuel consumption

Perrotta, Federico; Parry, Tony; Neves, Lu�s C.

Abstract

In Europe, the road network is the most extensive and valuable infrastructure asset. In England, for example, its value has been estimated at around £344 billion and every year the government spends approximately £4 billion on highway maintenance (House of Commons, 2011).
Fuel efficiency depends on a wide range of factors, including vehicle characteristics, road geometry, driving pattern and pavement condition. The latter has been addressed, in the past, by many studies showing that a smoother pavement improves vehicle fuel efficiency. A recent study estimated that road roughness affects around 5% of fuel consumption (Zaabar & Chatti, 2010). However, previous studies were based on experiments using few instrumented vehicles, tested under controlled conditions (e.g. steady speed, no gradient etc.) on selected test sections. For this reason, the impact of pavement condition on vehicle fleet fuel economy, under real driving conditions, at network level still remains to be verified.
A 2% improvement in fuel efficiency would mean that up to about 720 million liters of fuel (~£1 billion) could be saved every year in the UK. It means that maintaining roads in better condition could lead to cost savings and reduction of greenhouse gas emissions.
Modern trucks use many sensors, installed as standard, to measure data on a wide range of parameters including fuel consumption. This data is mostly used to inform fleet managers about maintenance and driver training requirements. In the present work, a ‘Big Data’ approach is used to estimate the impact of road surface conditions on truck fleet fuel economy for many trucks along a motorway in England. Assessing the impact of pavement conditions on fuel consumption at truck fleet and road network level would be useful for road authorities, helping them prioritize maintenance and design decisions.

Citation

Perrotta, F., Parry, T., & Neves, L. C. A big data approach to assess the influence of road pavement condition on truck fleet fuel consumption. Presented at International Congress on Transport Infrastructure and Systems (TIS Roma 2017)

• Conference Name: International Congress on Transport Infrastructure and Systems (TIS Roma 2017)
• End Date: Apr 12, 2017
• Acceptance Date: Nov 15, 2017
• Publication Date: Apr 11, 2017
• Deposit Date: Apr 20, 2017
• Publicly Available Date: Apr 20, 2017
• Peer Reviewed: Peer Reviewed
• Keywords: Road pavement condition, Fuel economy, Big data, Road maintenance strategy
• Public URL: https://nottingham-repository.worktribe.com/output/855647
• Related Public URLs: http://tisroma.aiit.it/
• Additional Information: Please visit: https://esr13truss.blogspot.co.uk/
• Contract Date: Apr 20, 2017