The article is dedicated to the development of an automated system aimed at creating a program of works for the maintenance of road surfaces. The system is based on data from the diagnostics and assessment of the technical condition of roads, in particular data on the assessment of the International Roughness Index (IRI). The development of a program of works for the maintenance of road surfaces is carried out based on the analysis of the IRI assessment both in the short term and on the time horizon of the contractor's work under the contract. The system is developed on the principle of modular programming, where one of the modules uses polynomial regression to predict the IRI assessment for several years ahead. The analysis of the deviation of the predicted IRI value from the actual one is the basis for the selection of works included in the program. The financial module allows the system to comply with the budget framework limited by the contract and provides an opportunity to evaluate the effectiveness of planning by calculating the difference between the cost of road surface maintenance and the contract value. Practical studies demonstrate that the system is capable of effectively and efficiently planning road surface maintenance works in accordance with the established contract deadlines.

Keywords: road surface, automated system, modular programming, machine learning, recurrent neural network, road condition, international roughness index, road diagnostics, road work planning, road work program

One of the key directions in the development of intelligent transport networks (ITS) is the introduction of automated traffic management systems. In the context of these systems, special attention is paid to the effective management of traffic lights, which are an important element of automated traffic management systems. The article is devoted to the development of an automated system aimed at compiling an optimal program of traffic light signals on a certain section of the road network. The Simulation of Urban Mobility (SUMO) traffic modeling package was chosen as a modeling tool, BFGS (Broyden-Fletcher-Goldfarb-Shanno) optimization algorithm was used, gradient boosting was used as a machine learning method. The results of practical research show that the developed system is able to quickly and effectively optimize the parameters of phases and duration of traffic light cycles, which significantly improves traffic management on the corresponding section of the road network.

Keywords: intelligent transport network, traffic management, machine learning, traffic jam, traffic light, phase of the traffic light cycle, traffic flow, modeling of the road network, python, simulation of urban mobility