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The development of urban strategies for the reduction of environmental impacts and decarbonization requires ongoing monitoring from the local scale and further deployment of actions to improve transport demand (user characteristics and modal choice) and supply (infrastructure and services). The analysis of pollution sources and the evaluation of possible scenarios are preliminary to the mitigation of impacts. In particular, the study of geometrical and functional characteristics of infrastructures through micro-simulation allows understanding of which schemes can support the reduction of emissions and guarantee high levels of service (LOS), reducing the problem of vehicular congestion in urban areas. The present work focuses on the small-scale analysis of vehicular traffic emissions at a multi-lane roundabout road intersection and the comparison of geometric schemes (current and design) and use with a turbo roundabout scheme as traffic volumes changes. These volumes have plummeted due to the current COVID-19 pandemic. The results show that the geometric-functional modification of the roundabout intersection from a multi-lane to a turbo-roundabout intersection allows a reduction of up to 30% of the emissions considering the current composition of the traffic fleet in the city of Rzeszow in Poland. The proposed comparative analysis methodology can contribute to the drafting of sustainable urban mobility plans (SUMPs) proposing a set of investments for new road works and considering a number of scenarios with interventions that can be implemented in the medium and long term that can provide the incentive to reduce road congestion and vehicular emissions. [ABSTRACT FROM AUTHOR]

In the last decades, the Italian road transport system has been characterized by severe and consistent traffic congestion and in particular Rome is one of the Italian cities most affected by this problem. In this study, a Leven berg Marquardt (LM) artificial neural network heuristic model was used to predict the traffic flow of non-autonomous vehicles. Traffic datasets were collected using both inductive loop detectors and video cameras as acquisition systems and selecting some parameters including vehicle speed, time of day, traffic volume and number of vehicles. The model showed a training, test and regression value (R² ) of 0.99892, 0.99615 and 0.99714 respectively. The results of this research add to the growing body of literature on traffic flow modelling and help urban planners and traffic managers in terms of the traffic control and the provision of convenient travel routes for pedestrians and motorists. [ABSTRACT FROM AUTHOR]

Among the solutions that make it possible to reduce CO2 emissions in the transport sector, particularly in urban traffic conditions, are hybrid vehicles. The share of driving performed in electric mode for hybrid vehicles is highly dependent on motion resistance. There are different methods for determining the motion resistance function during chassis dynamometer testing, leading to different test results. Therefore, the main objective of this study was to determine the effect of the chassis dynamometer load function on the energy demand and CO2 emissions of a full-hybrid passenger car. Emissions tests according to the New European Driving Cycle (NEDC) were carried out on a chassis dynamometer for three different methods of determining the car's resistance to motion. The study showed that adopting the motion resistance function according to different methods, results in differences in CO2 emissions up to about 35% for the entire cycle. Therefore, the authors suggest that in the case of tests carried out with chassis dynamometers, it is necessary to also provide information on the chassis dynamometer loading function adopted for the tests. [ABSTRACT FROM AUTHOR]