Global Positioning System (GPS) data acquisition devices have proven to be useful tools for collecting real-world motion data. The data collected by these devices provide valuable information when studying the vehicle movement parameters. For vehicle modeling, this data is invaluable for analyzing fuel consumption and vehicle performance. The study presents a methodology for developing the driving cycle of special cars, during which the speed profile of a particular type of vehicle is studied, loaded and processed, and noisy data is filtered for the purity of the experiment. The test data for severe operating conditions are analyzed. A city driving cycle has been developed for a special truck concrete mixer truck in the conditions of the city of Tyumen. Estimated fuel economy of the specified vehicle is estimated.

Keywords: driving cycle, fuel efficiency of concrete mixer truck, noisy data, data filtering, GLONASS/GPS

Transportation is one of the most important stages in the management of cement delivery, which accounts for 60% of total costs. Thus, a slight improvement in the collection and processing of data on the operation of concrete mixers can have a significant impact on the overall savings spent on the work of companies engaged in transportation. On the other hand, the movement of heavy vehicles carrying cement mixture leads to the spread of air pollution and damage to the road surface in case of excessive loading. Therefore, the issue of determining the route of the vehicle to achieve this goal is very important. In this study, the routing process of concrete mixers was modeled using random routing problems and genetic algorithms. The simulation results showed that the genetic algorithm converges to the optimal answer. On the other hand, the volume of the cost function decreases from 1,177,500 rubles to 158,900 rubles. due to the optimization of the mentioned model, and the performance result led to the appearance of the shortest possible path. With the help of the algorithm, all the parameters of sustainable development management are achieved, including reducing air pollution, reducing fuel consumption and reducing the destruction of the road surface. Finally, thanks to the integration of the GLONASS/GPS software, the output data of the algorithm was mapped to the map.

Keywords: optimization of routes, fuel efficiency, reduction of emissions, preservation of the integrity of the road surface, optimization, logistics, modeling, special car, urban operation