Influence of the capacity of regulated crossings on the quantity of exhaust gases from vehicles

The ability to monitor the condition of individual road sections in the urban environment in order to manage traffic congestion more effectively is becoming increasingly important. At present, complex traffic management systems are being developed that can process data from both static and mobile sensors and provide traffic information for the road network. In addition to typical traffic data such as traffic flow, density and average speed, there is currently a great interest in environmental factors such as greenhouse gases, pollutant emissions and fuel consumption. In this work, monitoring using surveillance cameras of street surveillance with the help of convolutional neural networks is proposed. Testing of the system under various conditions showed an absolute percentage accuracy of detection and classification of vehicles, at least 92 %. As a result of the research, the most significant factors have been identified, by controlling which it is possible to effectively influence the traffic lane capacity. A method for calculating the time of passing-by of the queue of non-group vehicles is proposed and the effect of acceleration on throughput is investigated, which will reduce the likelihood of congestion at regulated intersections. Studies have been conducted on the influence of the speed of passage by motor vehicles of the nodes of the road network on the amount of emissions of pollutants into the atmosphere.