Yang Wang

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.

This research aims to find the most ecological itineraries for urban mobility in a small city (eco-routes), where distances are rather short, but car dependence is really high. A real life citywide survey was carried out in the city of Caceres (Spain) with almost 100,000 inhabitants. Research was done on alternating routes, traffic, times of day, and weather conditions. The output of the study was to assess fuel consumption, CO₂, and regulated pollutant emissions for different type of vehicles, routes, and drivers. The results show that in the case studied, urban roads had fewer emissions (CO₂ and pollutants) but there was an increase in the population affected by pollutants. On the contrary, bypasses reduced travel time and congestion but increased fuel consumption and emissions. Traffic conditions had a greater influence on fuel consumption in petrol vehicles than diesel ones. Therefore, there must be a balanced distribution of traffic in order to minimize congestion, and at the same time to reduce emissions and the number of people affected by harmful pollution levels. There should be a combination of regulatory measures in traffic policies in order to achieve that balance by controlling access to city centres, limiting parking spaces, pedestrianization, and lowering traffic speeds in sensitive areas.

The road sector is the dominant transport mode in terms of demand for both passengers and goods

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.

Despite technological advances in engines and fuels, the transportation sector is still one of the largest emitters of greenhouse gas (GHG). Driving patterns, including eco-driving techniques, are a complementary measure for saving GHG emissions. Most eco-driving studies so far have been conducted in large cities suffering chronic congestion problems. The aim of this research is therefore to analyse the potential of driver behaviour for reducing emissions in a small non-congested city. Driver performance parameters such as travel speeds, number of stops, revolutions per minute, and maximum acceleration-deceleration are also studied. The methodology is designed to measure the effect of both eco-driving and eco-routing under real traffic conditions. A campaign was carried out in the city of Caceres (Spain) to collect data on various types of roads under different traffic conditions. This research concludes that eco-driving leads to CO₂ savings on all routes and road types of 17% in gasoline engines and 21% in diesel, although travel times are increased by 7.5% on average. The shortest route is also the most ecological, regardless of the traffic volume and characteristics, implying that consumption in non-congested cities depends mainly on distance travelled rather than driving patterns in terms of number of stops, speed and acceleration.

Urban areas play a key role in the development of European territories, and it is essential for them to be sustainable and efficient. However, the European cities are facing some challenges related to certain trends that are threatening their sustainable development and operational efficiency. In this paper, we compare the contribution of three policy measures—cordon toll accompanied by public transport improvements, teleworking and re-densification—to address different city challenges. The policy assessment requires a long term simulation tool, i.e., the MARS (Metropolitan Activity Relocation Simulator) model, which is able to consider interactions between land use and transport systems. The simulations of the different policy scenarios were carried out for the case of Madrid in the period 2012–2031. The contribution of the policy measures to address the city challenges was measured through different indicators. The results indicated that the three policy measures contributed to the time efficiency challenge, by saving time for the commute, reducing congestion and improving the traffic flows at peak hours. The most effective policy in this regard is the teleworking measure. Another challenge addressed by the three policies was the accessibility to PT. The three policy scenarios, when simulated, showed higher PT use, especially the cordon toll scenario. However, the only policy that really contributed to the energy, emissions and pollution city challenges was the re-densification measure, which reduced travel distances and encouraged a mobility that relies more on PT and slow modes.