Sustainable City Evaluation Using the Database for Estimation of Road Network Performance

This article introduces the Database for Estimation of Road Network Performance (DERNP) to enable wide-scale estimation of relevant Road Network Performance (RNP) factors for major German cities. The methodology behind DERNP is based on a randomized route sampling procedure that utilizes the Worldwide Harmonized Light Vehicles Test Procedure (WLTP) in combination with the tile-based HERE Maps Traffic API v7 and a digital elevation model provided by the European Union’s Earth Observation Programme Copernicus to generate a large set of independent and realistic routes throughout OpenStreetMap road networks. By evaluating these routes using the PHEMLight5 framework, a comprehensive list of RNP parameters is estimated and translated into polynomial regression models for general usage. The applicability of these estimations is demonstrated based on a case study of four major German cities. This case study considers network characteristics in terms of detours, infrastructure, traffic congestion, fuel consumption, and CO2 emissions. Our results show that DERNP and its underlying randomized route sampling methodology overcomes major limitations of previous wide-scale RNP approaches, enabling efficient, easy-to-use, and region-specific RNP comparisons

Análisis del estilo de conducción de un vehículo con motor MPI mediante el ciclo de emisiones reales RDE para determinar su influencia en el consumo de combustible en la ciudad de Quito-Ecuador

En la ciudad de Quito, Ecuador, el crecimiento del parque automotor y los altos precios de los combustibles han generado una creciente preocupación por reducir el consumo de combustible y disminuir las emisiones asociadas. Si bien existen diversas perspectivas para abordar esta inquietud, como el filtrado de aire, el octanaje del combustible, características del motor, etc., uno de los factores más influyentes en el elevado consumo de combustible es el estilo de conducción. El estilo de conducción, que se caracteriza por factores como el uso frecuente del freno y el acelerador, cambios de marcha inoportunos y un tiempo de ralentí prolongado, ha sido identificado como un factor determinante en el consumo de combustible. En este proyecto, se lleva a cabo un análisis exhaustivo de las características que están correlacionadas con el consumo de combustible, centrándose en su influencia en el estilo de conducción, ya sea normal o agresivo. Los resultados revelan que las revoluciones del motor, la velocidad, la posición de la mariposa y la presión absoluta del múltiple son aspectos críticos que convergen en el impacto sobre el consumo promedio de combustible junto con aquellos factores que varían según el comportamiento del conductor. Estas variables muestran una fuerte relación con el estilo de conducción y su influencia en la eficiencia del consumo de combustible. Además, se propone el desarrollo de un modelo de machine learning que clasifica el estilo de conducción en función de las condiciones de la ruta, según el ciclo RDE, y se relaciona con el consumo de combustible en cada etapa del ciclo: urbano, rural y carretera. Los resultados obtenidos indican que un estilo de conducción más agresivo se asocia con un aumento en el consumo de combustible.In the city of Quito, Ecuador, the growth of the automotive fleet and high fuel prices have raised increasing concerns about reducing fuel consumption and decreasing associated emissions. While there are various perspectives to address this concern, such as air filtering, fuel octane rating, engine characteristics, etc., one of the most influential factors in high fuel consumption is driving style. Driving style, characterized by factors such as frequent use of brakes and accelerator, untimely gear shifts, and extended idling time, has been identified as a determining factor in fuel consumption. In this project, a comprehensive analysis is conducted on the characteristics that are correlated with fuel consumption, focusing on their influence on driving style, whether it is normal or aggressive. The results reveal that engine revolutions, speed, throttle position, and absolute manifold pressure are critical aspects that converge in their impact on average fuel consumption, along with other factors that vary according to driver behavior. These variables show a strong relationship with driving style and its influence on fuel consumption efficiency. Additionally, the development of a machine learning model is proposed to classify driving style based on road conditions according to the RDE cycle, and its relationship with fuel consumption in each stage of the cycle: urban, rural, and highway. The obtained results indicate that a more aggressive driving style is associated with an increase in fuel consumption

Impact of driving behaviour on emissions and road network performance

This paper presents findings from a simulation based comparative evaluation of driving behaviours and their impacts on road safety, environmental quality and network efficiency. Driving behaviour was represented by driver speed, acceleration, lane changing and gap acceptance actions. A fourmode elemental emissions model was used to collect second-bysecond data on fuel consumption and CO2 emissions. Surrogate measures of safety, expressed in terms of the number of lane changes and severe decelerations, were used to describe the degree of safety in the simulation experiments. Aggressive drivers were found to be 35 times more likely to be involved in a crash on the motorway, and two times more likely to be involved in a crash on the urban network. The results for the motorway simulations also showed that aggressive drivers achieved only a 3.8 percent reduction in travel times (62 seconds on a 26 minute trip) at the expense of 85 percent more lane changes and 332 percent increase in fuel consumption and CO2 emissions. The reduction in travel times for urban conditions was lower at around 1.6 percent (7 seconds on a 434 second trip) at the expense of 300 percent more lane changes and 138 percent increase in fuel consumption and CO2 emissions. Sensitivity analysis of the impacts of varying proportions of drivers was also conducted. The results showed that the negative impacts of aggressive driving behaviour outweigh by a factor of three any benefits that can be obtained through reductions in travel times