The Use of Multi-Sensor Video Surveillance System to Assess the Capacity of the Road Network

Abstract

Currently, in many cities around the world there is a significant increase in the number of vehicles, which leads to an aggravation of problems and contradictions in the road and transport system. This is especially true of traffic congestion, since the presence of the congestion leads to a number of negative consequences: an increase in travel time, additional fuel consumption and vehicle wear, stress and irritation of drivers and passengers, environmental poisoning and others. To solve the problem of congestion, it is necessary to have a reliable system for collecting information about the situation on the roads and a well-developed method for analyzing the collected information. The paper discusses the possibilities of collecting the required information using multi-touch video cameras and ways to improve them. A distinctive feature of this study is the registration of pedestrians crossing the road at the intersection. The aim of the work is to develop methods for collecting information using road sensor video surveillance systems in a traffic congestion and data processing using statistical methods such as: multiple regression analysis, cluster analysis, multidimensional scaling methods and others. The tasks were set: 1) to identify the most significant factors affecting the intensity of movement of vehicles at intersections in a congestion; 2) divide congestion into clusters with the identification of their characteristics; 3) to give a visual representation of multidimensional statistical information obtained with the help of multi-touch road video cameras.

Changes in the Environmental Sustainability of the Urban Transport System when Introducing Paid Parking for Private Vehicles

“…Environmental sustainability is one of the partial coefficients k e of the integral indicator of the transport system sustainability, estimated by the coefficient K i , [31], which was determined taking into account the degree of weight λ i (0…”

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Mobility and Road Safety Improvement by Optimizing Smart City Infrastructure Parameters: A Case Study

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Predicting and Managing Lane Capacity at a Signal-Controlled Intersection

“…Бурное развитие технологий в сфере распознавания объектов в реальном режиме времени нашло применение и в области регулирования трафика уличного движения [1,2]. С помощью методов обработки изображений и машинного обучения в режиме реального времени исследователи [3][4][5] предлагают эффективный способ оценки плотности трафика на перекрестке.…”