Eco-driving is becoming more widespread as individual car-use behaviour is a cost-effective way of improving vehicle fuel economy and reducing CO₂ emissions. The literature shows a wide range of efficiencies as a result of eco-driving, depending on route selection, traffic characteristic, road slope, and the specific impact evaluation method. This paper follows this line of research and assesses the impact of an eco-driving training programme on fuel savings and reduction of CO₂ emissions in a well-designed field trial, focusing on the specific impacts according to road type. The methodology includes a comprehensive trial on different types of road sections under various traffic conditions; a processed dataset using R codes to integrate, clean, and process all the information collected; and a systematic method to evaluate the overall and specific impacts of eco-driving. The final results show a general fuel saving after eco-driving training of up to an average of 6.3% regardless of fuel and road type. Driving performance, as represented by selected parameters (average and maximum RPM, average and maximum speed, aggressive acceleration/deceleration), changed significantly after the training. The highest fuel savings are achieved on major arterial road sections with a certain number of roundabouts and pedestrian crossings. This work contributes to an understanding of the key factors for eco-driving efficiency according to road type under real traffic conditions. It offers greater insights for policymakers in road transport planning and for drivers when applying eco-driving techniques.

The increasingly widespread use of vehicles has intensified fuel consumption and hence the emission of air pollutants, causing a negative environmental impact on both human health and climate change. It is well known that vehicle fuel consumption depends on several factors such as engine and vehicle technology, road characteristics, traffic conditions, and driver ability. Although the relationship between these variables has been subject of several researches, the combined influence of traffic flow with road type on vehicle fuel consumption has not yet been studied in depth. This paper aims to fill this gap by processing a large dataset of real-world driving data from an experiment carried out in Madrid, Spain; and to develop and validate a new approach using cluster analysis to define real traffic conditions. The results indicate that poor traffic conditions can significantly reduce vehicle’s energy efficiency and influence driving behavior, rather drastically depending on the road typology. While on high-capacity roads the speed covariance increases up to 73% in congestion, on low-capacity roads it increases by 31%, since road geometry also covers an important role; indeed, due to their complex and segmented geometry, local streets show 37% less vehicle’s energy efficiency compared with highways. The outcomes of this study suggest that energy efficiency depends on avoiding congestion on high-capacity roads, selecting green itineraries using the right road sections and having a more homogeneous driving behavior on low-capacity roads, through eco-driving whenever possible.

CO₂ emissions reduction is a top element of transport policy agenda. Among other mitigation policy measures, eco-driving techniques have proven to be effective in reducing fuel consumption and CO₂ emissions. The aim of this paper is to compare the impacts of adopting eco-driving in different cities, road segments, traffic, and driver features. It intends to gain an insight into how city size and driving characteristics can reduce fuel consumption and CO₂ emissions in order to develop specific eco-driving strategies. Field trials were conducted in two Spanish cities (Madrid and Caceres). 24 drivers, with different driving experiences, drove two different vehicles (petrol and diesel) along roads with different characteristics. The experiment was divided into two periods of 2 weeks; after the first one, drivers received an eco-driving training course. The impacts of eco-driving were measured comparing before and after results. They showed that eco-driving is highly effective in reducing fuel consumption and CO₂ emissions in both, large-congested and small, cities. Savings between 5% and 12% were achieved. The efficiency increases with road capacity and decreased with city size. Eco-driving appears to be more effective in small, uncongested cities. In addition, limiting speeds on high capacity roads has proven to be a good energy saving measure.

Cities are experiencing a process of suburbanization and last-mile delivery has grown, worsening traffic congestion, pollutant emissions, and citizens’ quality of life. Based on a real-life case study, this research compares the environmental performance of different delivery routes carried out by Diesel Light-Duty Vehicles (LDV) according to delivery area, city center or peri-urban. Some 242 delivery routes performed by thirteen drivers were recorded for one month, including instantaneous GPS position, speed, and other parameters (7262 km travelled). Four different delivery routes typologies were compared, and the drag function of the vehicles was characterized. It enabled calibration and modelling dynamics to calculate fuel consumption and pollutant emissions according to delivery routes. The results show that pedestrian crossings, traffic lights, and traffic congestion reduce the average operating speed by up to 57% in the city center and consequently overall energy efficiency. Our results highlight the urgency of replacing diesel LDV for deliveries in the city center with no-motorized transport modes and of implementing intermodality to cover deliveries in residential peri-urban areas. Due to low speeds and frequent start-stops, the efficiency of vehicles in these areas is reduced to a minimum and pollutant emissions increase. The outputs set a basis for recommendations for using LDV only for delivery routes with less traffic interruptions and foster intermodal solutions.