The possibilities of collecting the necessary information using multi-touch cameras and ways to improve road traffic data collection are considered. An increase in the number of vehicles leads to traffic jams, which in turn leads to an increase in travel time, additional fuel consumption and other negative consequences. To solve this problem, it is necessary to have a reliable information collection system and apply modern effective methods of processing the collected information. The technology considered in the article allows taking into account pedestrians crossing the intersection. The purpose of this article is to determine the most important traffic characteristics that affect the traffic capacity of the intersection, in other words, the actual number of passing cars. Throughput is taken as a dependent variable. Based on the results of the regression analysis, a model was developed to predict the intersection throughput taking into account the most important traffic characteristics. Besides, this model is based on the fuzzy logic method and using the Fuzzy TECH 5.81d Professional Edition computer program.

The article discusses the issues of improving the collection of traffic information using video cameras and the statistical processing of collected data. The aim of the article was to identify the main patterns of traffic at intersections in traffic congestion and to develop an analysis technique to improve traffic management at intersections. In modern conditions, there is a sharp increase in the number of vehicles, which leads to negative consequences, such as an increase in travel time, additional fuel consumption, increased risk of traffic accidents and others. To solve the problem of improving traffic control at intersections, it is necessary to have a reliable information collection system and apply modern effective methods of processing the collected information. The purpose of this article is to determine the most important traffic characteristics that affect the throughput of intersections. As a criterion for the cross-pass ability of the intersection, the actual number of passing cars during the permission signal of the torch light is taken. Using multivariate regression analysis, a model was developed to predict intersection throughput taking into account the most important traffic characteristics. Analysis of the throughput of intersections using the fuzzy logic method confirmed the correctness of the developed model. In addition, based on the results of processing information collected at 20 intersections and including 597 observations, a methodology was developed for determining the similarity of traffic intersections. This allows us to identify homogeneous types of intersections and to develop typical traffic management techniques in the city, instead of individually managing each node of the city’s transport network individually. The results obtained lead to a significant reduction in costs for the organization of rational traffic flows.

This study deals with the problem of rea-time obtaining quality data on the road traffic parameters based on the static street video surveillance camera data. The existing road traffic monitoring solutions are based on the use of traffic cameras located directly above the carriageways, which allows one to obtain fragmentary data on the speed and movement pattern of vehicles. The purpose of the study is to develop a system of high-quality and complete collection of real-time data, such as traffic flow intensity, driving directions, and average vehicle speed. At the same time, the data is collected within the entire functional area of intersections and adjacent road sections, which fall within the street video surveillance camera angle. Our solution is based on the use of the YOLOv3 neural network architecture and SORT open-source tracker. To train the neural network, we marked 6000 images and performed augmentation, which allowed us to form a dataset of 4.3 million vehicles. The basic performance of YOLO was improved using an additional mask branch and optimizing the shape of anchors. To determine the vehicle speed, we used a method of perspective transformation of coordinates from the original image to geographical coordinates. Testing of the system at night and in the daytime at six intersections showed the absolute percentage accuracy of vehicle counting, of no less than 92%. The error in determining the vehicle speed by the projection method, taking into account the camera calibration, did not exceed 1.5 km/h.

This study deals with the problem of rea-time obtaining quality data on the road traffic parameters based on the surveillance camera data. The purpose of the paper is to develop a system to collect data on the traffic flow structure and to determine the traffic flow speed and direction in real-time. Our solution is based on the use of the YOLOv3 neural network architecture and open-source tracker SORT. To increase the accuracy of detection and classification, we used multi-scale prediction with an increased number of anchors. To determine the speed, we used a matrix of the perspective transformation of the source image to geographical coordinates. To train the neural network, we marked over 6,000 images and performed augmentation, which allowed us to increase the dataset to 60,000 images. Checking the system at night and in the day showed an absolute percentage accuracy of counting vehicles of no less than 92%. The error in determining the vehicle speed by the projection method, taking into account the camera calibration, did not exceed 2.74 m/s. The presented study allows us to generate big data for the intelligent transport systems decision-making system in real-time and to lower the requirements for peripheral equipment.

The issues of improving the methodological foundations on urban planning based on the requirements of “smart” planning and managing sustainable development of territories are considered. The concepts of “smart urban planning”, “urban planning information platform”, “digital twin” of a territory and “cyber physical urban development system” are defined. A brief review of the world experience in adopting “smart” technologies for urban development is given. The paper sets the long-term problems associated with theoretical and practical development in the field. Finally, the authors propose the concept of “smart urban planning”, aimed at providing comfortable and safe living conditions. It provides fundamental principles of goal-setting, planning and adopting the sustainable territorial development using information and communication technologies; a theoretical model of the urban planning information platform based on automation methods, adaptation conditions, as well as describing the “digital twin” layers of an urban development object.