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. -> Link to full text in repository