Evgeniy Sergeevich Kozin

This paper discusses the spatial and temporal unevenness of traffic flows on a city’s road network. It demonstrates the uneven distribution of vehicle traffic throughout the day, both at the intersection as a whole and in individual directions, using a key intersection in Tyumen as an example. Machine learning methods are used to cluster vehicle traffic flow rate values based on travel directions. The results reveal four distinct classes, excluding nighttime, each of which requires the development of a separate traffic signal plan. Cluster boundaries are not synchronous with the beginning of each hour, and practical implementation requires the configuration of new traffic monitoring system database templates. Adjusting the timing of signal plan switches to account for hourly unevenness in traffic flow rates can reduce vehicle delays by up to 6.3% during certain periods. The results of the study of traffic unevenness can be used to select methods for improving the efficiency of traffic management at intersections by adjusting signal plans, the order of vehicle movements by direction, and the number of traffic light phases. Traffic unevenness analysis is essential for improving integrated traffic management plans and developing plans to reduce road accidents. The methods for analyzing traffic flow irregularity discussed in the article can be integrated into the city’s digital twin and the Intelligent Transport System.

Effective management of the service station’s production area load is a key factor in improving customer service quality and operational efficiency. The proposed approach to optimizing vehicle distribution among service stations is based on scheduling theory and takes into account station specialization, customer priorities, and waiting time minimization. A dynamic task assignment algorithm, implemented in Python, is developed using a greedy strategy focused on early resource release. A software application with a web interface and a schedule visualization module has been created for practical application. This software product may be of interest to automotive and specialized equipment service companies for managing service station loads. The proposed approach provides a balance between computational efficiency and schedule quality, making it suitable for real-world application in urban service stations.

This article examines the spatial and temporal unevenness of the transport complex operation in a large city with a population of about 0.9 million people and without off-street transport. The patterns of changes in the number of passengers transported in the city are described by a harmonic model, and seasonal unevenness with different numbers of peak values is noted. All routes can be divided into three groups based on the trend in passenger volume. The largest number of routes exhibited a downward trend in passenger volume. A downward trend in passenger volume is observed in the total number of passengers on all routes, despite an increase in the city’s population. Parking occupancy rates also show seasonal fluctuations. A downward trend in paid parking occupancy rates is emerging in the city’s central administrative and business district. The results of the study are relevant for choosing methods for managing the transport behavior model. Analysis of uneven passenger numbers on individual routes is necessary for improving the route network and determining the optimal number and passenger capacity of buses. Analyzing uneven occupancy rates in paid parking lots allows for the development of differentiated rates. The methods used in this article can be integrated into a city’s digital twin to improve forecasting accuracy.

The study considers a methodical approach to control of production processes for maintenance and repair of cars at technical service enterprises. The approach is based on considering the enterprise as a complex organizational or cybernetic system. For the main process implemented by this system, the number of control points during the shift, the size and degree of delay of control actions are determined. To simulate the system operation, a simulation model is used that takes into account the gradual increase in the error of performers and its elimination when applying the control action. Taking into account the established indicator and the efficiency criterion, the optimal number of control system elements is determined depending on the intensity of car arrival in the service zone. Models of the established patterns are obtained, which are described by single-factor regression equations. Models of patterns of influence of control system parameters on the relative throughput of the system are developed.Purpose – to improve the efficiency of management of automobile transport enterprises by establishing patterns of influence of the number of vehicles arriving in the technical service area on the parameters of the enterprise's production process control system.Methodology. The study uses the method of correlation-regression analysis, the methodology of experiment planning, simulation modeling, and system analysis.Results. Patterns of influence of the number of vehicles arriving in the technical service area on the number of control points during a shift, on the magnitude of the control action, and on the degree of delay in the implementation of the control action were established. Patterns of influence of the number of control points during a shift, the magnitude of the control action, and the degree of delay in the implementation of the control action on the relative throughput of the system were established.Practical implications. The results of the study can be used by the management of enterprises for technical maintenance and repair of vehicles in the operational management of technological processes.

The study describes the use of evolutionary methods or genetic algorithms for the technological design of newly constructed or modernized car service stations. Genetic algorithms are one of the types of machine learning models and are actively used to solve multifactor optimization problems. A task of this type is to search for the technical parameters of a car service enterprise under which the economic indicators of its activities will correspond to the profit or capital cost restrictions set by the user. The paper presents the parameters of the developed model, fitness functions, and also provides an assessment of the effectiveness of using the method of genetic algorithms relative to the method of simply enumerating different options for combinations of initial factors. Purpose. Increasing the efficiency of management of road transport enterprises by using the method of genetic algorithms for strategic planning tasks. Methodology. The research uses the method of genetic algorithms to solve a multi-criteria reverse optimization problem in the technological design of a car service station Results. The use of the method of genetic algorithms for the design of service stations and enterprises for the maintenance and repair of vehicles is justified, taking into account the restrictions or targets established at the beginning of the design. Practical implications. The results of the research can be used by the management of enterprises for the maintenance and repair of vehicles in their technological design, strategic planning of activities and modernization.

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.

The article discusses the development of a remote-control system for an all-terrain vehicle. One of the elements of the designed system is the gearbox control u The article discusses the development of a remote-control system for an all-terrain vehicle. One of the elements of the designed system is the gearbox control u

The paper presents a methodology for implementing a decision support system for managing a car service workshop. The system operates with current technical indicators and financial results of the enterprise. The user of the system has the opportunity to evaluate the possible parameters of the enterprise when changing one or more target indicators. Different management decisions are offered by the system and ranked according to possibility of their implementation. The result of the system operation is a list of recommended decisions. These decisions will allow to achieve the goal set by the user related to achieving economic benefits or reducing the costs for the operation of the enterprise.