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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. [ABSTRACT FROM AUTHOR]

The road transportation sector is responsible for around 25% of total man-made CO2 emissions worldwide. Considerable efforts are therefore underway to reduce these emissions using several approaches, including improved vehicle technologies, traffic management and changing driving behaviour. Detailed traffic and emissions models are used extensively to assess the potential effects of these measures. However, if the input and calibration data are not sufficiently detailed there is an inherent risk that the results may be inaccurate. This article presents the use of Floating Car Data to derive useful speed and acceleration values in the process of traffic model calibration as a means of ensuring more accurate results when simulating the effects of particular measures. The data acquired includes instantaneous GPS coordinates to track and select the itineraries, and speed and engine performance extracted directly from the on-board diagnostics system. Once the data is processed, the variations in several calibration parameters can be analyzed by comparing the base case model with the measure application scenarios. Depending on the measure, the results show changes of up to 6.4% in maximum speed values, and reductions of nearly 15% in acceleration and braking levels, especially when eco-driving is applied. [ABSTRACT FROM AUTHOR]

• Eco-driving impacts are tested in a field trial with 1156 trips in two cities. • Fuel consumption is associated with both driving patterns and road conditions. • Eco-driving training influences directly drivers' driving habits. • Traffic congestion and steep road slope negatively influence fuel consumption. Eco-driving, as individual car-use behavior, is a cost-effective way of improving fuel efficiency, reducing CO 2 emissions and other air pollutants like NO x. This paper aims to expand the knowledge on the short-term impacts of eco-driving by developing an analytical model of the key factors that explain fuel consumption and eco-driving, and to examine their relations in greater depth. Additionally, this paper analyses the effects on drivers' stress levels after eco-driving. An eco-driving field trial is applied to collect real data of 1156 trips, using two vehicles and 24 drivers (42% Female; Age, M = 30.15; Years of driving experience, M = 10.30) in two Spanish cities with different road characteristics. A sequential method involving factor analysis, regression analysis and path analysis is used to analyze the sample. The results confirm that eco-driving is strongly affected by driving behavior like deceleration rate, RPM and speed, also showing that external factors as congestion and road slope have a direct influence on fuel consumption. The results also reveal that perceived stress levels vary among drivers, but there is not significant change when drivers eco driving. [ABSTRACT FROM AUTHOR]