This research is a comparative assessment of the operation of dual fuel engine vehicles (gas-diesel and diesel cycles) in the context of the...

This article focuses on the regulation of CO 2 emitted in the exhaust gases of gasoline vehicles. Based on comparing the world practices of restrictive measures on greenhouse gas emissions with Russian legislation, we conclude that there is a need to adjust the limits of CO 2 emission taking into account the negative impact of ambient temperature on CO 2 emission. The climatic conditions of many countries stipulate the use of vehicles in temperatures below zero. At the same time, the existing regulations fully take into account the temperature features of the various countries, which casts doubt on the existence of uniform emission standards for all countries. Here, we conduct an experiment on one of the most popular cars in Russia: the Mitsubishi Lancer 9. We establish that lower temperatures are correlated with larger concentrations of CO 2 in the exhaust gases. We draw a conclusion about the need to account for the actual operating conditions when establishing limit values on CO 2 emissions of vehicles.

Assessment of gas cylinder vehicles adaptability for operation at low ambient temperatureTyumen State oil and Gas University, Russian conditions I....

This article is devoted to increasing the use of natural gas in Russia as a measure to decrease the negative influence of motor transport on the environment. A brief analysis of the global fleet of natural gas vehicles is provided above. The documents accepted in Russia to promote public awareness of compressed natural gas in transport are submitted. The basic reasons keeping the growth of natural gas vehicle fleets in Russia consist of weak branching of refuelling stations; difficulty in determining the actual amount of compressed natural gas required; and control methods of the consumption of gas fuel. The offers promoting the growth of the fleet of natural gas vehicles are given.

The paper reviews a relevant problem of providing a higher quality of people's living in the northern regions by the application of the modern technologies within transport in order to improve its environmental performance and efficiency. The aim of the research is to substantiate methods of increasing the adaptability of vehicles to variable low-temperature operating conditions based on a neural control system of preheating and maintaining the optimum temperature of technological fluids by SHF radiation and improving diesel fuel properties by introducing a multifunctional additive. It has been found that SHF radiation influences the physical and chemical parameters of diesel fuel, as well as the engine output parameters. SHF radiation results in a reduction of fuel consumption to 7%, smoke from the exhaust to 15%, and nitrogen oxide emission to 30%. A conclusion is made that using SHF radiation for technological fuel preheating is essential. An approach has been developed that improves low-temperature properties of diesel fuels and increases engine operation efficiency under severe climatic conditions by introducing a multifunctional additive. The additive will enable a significant decrease in fuel consumption under low-temperature operating conditions and a complex improvement of fuel properties.

The article discusses the issues of spatial unevenness of transport demand in the city by various transport modes. It describes the creation of models using an artificial neural network to estimate the travel time and share by private and public transport in a large city that does not have off-street transport. The city transport macromodel in PTV Visum (V.18) was used as a data source, from which data were obtained on 50 basic parameters taken into account in the specialized software during the development of the transport model. In total, 50 factors act as independent variables that do not have linear relationships with each other and with the dependent variable, which significantly complicates the use of other models. These models allow assessing the influence degree of the most important factors. Further, the article shows the uneven spatial distribution of the shares of trips by private and public transport across the areas of a city. Using machine learning methods, the transport areas of Tyumen were clustered into nine classes belonging to the central sector, where the share of public transport is significantly higher than at the city border. The dependence of the trip share by cars and shuttle buses on the average travel time and distance by private and public transport for each class of transport areas has been established. The research results can be used when creating new transport areas in the city macromodel and when adjusting transport planning documents. The methods used for analyzing big data on the operation of the transport complex can be implemented in the digital twin of the city and the Intelligent Transport System.