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Urban agglomerations close to road infrastructure are particularly exposed to harmful exhaust emissions from motor vehicles and this problem is exacerbated at road intersections. Roundabouts are one of the most popular intersection designs in recent years, making traffic flow smoother and safer, but especially at peak times they are subject to numerous stop-and-go operations by vehicles, which increase the dispersion of emissions with high particulate matter rates. The study focused on a specific area of the city of Rzeszow in Poland. This country is characterized by the current composition of vehicle fleets connected to combustion engine vehicles. The measurement of the concentration of particulate matter (PM2.5 and PM10) by means of a preliminary survey campaign in the vicinity of the intersection made it possible to assess the impact of vehicle traffic on the dispersion of pollutants in the air. The present report presents some strategies to be implemented in the examined area considering a comparison of current and project scenarios characterized both by a modification of the road geometry (through the introduction of a turbo roundabout) and the composition of the vehicular flow with the forthcoming diffusion of electric vehicles. The study presents an exemplified methodology for comparing scenarios aimed at optimizing strategic choices for the local administration and also shows the benefits of an increased electric fleet. By processing the data with specific tools and comparing the scenarios, it was found that a conversion of 25% of the motor vehicles to electric vehicles in the current fleet has reduced the concentration of PM10 by about 30% along the ring road, has led to a significant reduction in the length of particulate concentration of the motorway, and it has also led to a significant reduction in the length of the particulate concentration for the access roads to the intersection. [ABSTRACT FROM AUTHOR]

Road transport contributes to almost a quarter of carbon dioxide emissions in the EU. To analyze the exhaust emissions generated by vehicle flows, it is necessary to use specialized emission models, because it is infeasible to equip all vehicles on the road in the tested road sections with the Portable Emission Measurement System (PEMS). However, the currently used emission models may be inadequate to the investigated vehicle structure or may not be accurate due to the used macroscale. This state of affairs is especially related to full hybrid vehicles, since there are none of the microscale emission models that give estimated emissions values exclusively for this kind of drive system. Several automakers over the past decade have invested in hybrid vehicles with great opportunities to reduce costs through better design, learning, and economies of scale. In this work, the authors propose a methodology for creating a CO2 emission model, which takes relatively little computational time, and the models created give viable results for full hybrid vehicles. The creation of an emission model is based on the review of the accuracy results of methods, such as linear, robust regression, fine, medium, coarse tree, linear, cubic support vector machine (SVM), bagged trees, Gaussian process regression (GPR), and neural network (NNET). Particularly in the work, the best fit for the road input data for the CO2 emission model creation was the GPR method. PEMS data was used, as well as model training data and model validation. The model resulting from this methodology can be used for the analysis of emissions from simulation tests, or they can be used for input parameters for speed, acceleration, and road gradient. [ABSTRACT FROM AUTHOR]