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2020•Вестник Южно-Уральского государственного университета. Серия: Экономика и менеджментOpen access

ROAD TRAFFIC PLANNING IN THE CONTEXT OF THE SUSTAINABLE URBAN TRANSPORT SYSTEM

Vladimir Shepelev, Zlata Almetova, Mikhail Korzan, Irakli Charbadze

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Abstract

One of the factors affecting the intersection capacity of nodes in the street-road network are cargo transport vehicles in the traffic flows. The existing methods for assessing the impact of cargo transport vehicles on the road traffic parameters are based on statistical data. The research is based on the use of neural networks to process big data (BIGDATA) from CCTV cameras in real time mode. As a result of the interpretation and analysis of big data, the patterns of changes in cargo transport vehicles during the day and its impact on the intersection capacity of nodes in the street-road network were established. The presented study allows to improve the decision-making efficiency while optimizing the road traffic planning.

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What this paper is about

One of the factors affecting the intersection capacity of nodes in the street-road network are cargo transport vehicles in the traffic flows. The existing methods for assessing the impact of cargo transport vehicles on the road traffic parameters are based on statistical data. The research is based on the use of neural networks to process big data (BIGDATA) from CCTV cameras in real time mode. As a result of the interpretation and analysis of big data, the patterns of changes in cargo transport vehicles during the day and its impact on the intersection capacity of nodes in the street-road network were established. The presented study allows to improve the decision-making efficiency while optimizing the road traffic planning.

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Available abstract

One of the factors affecting the intersection capacity of nodes in the street-road network are cargo transport vehicles in the traffic flows. The existing methods for assessing the impact of cargo transport vehicles on the road traffic parameters are based on statistical data. The research is based on the use of neural networks to process big data (BIGDATA) from CCTV cameras in real time mode. As a result of the interpretation and analysis of big data, the patterns of changes in cargo transport vehicles during the day and its impact on the intersection capacity of nodes in the street-road network were established. The presented study allows to improve the decision-making efficiency while optimizing the road traffic planning.

Key concepts: Transport engineering, Intersection (aeronautics), Big data, Context (archaeology), Computer science, Floating car data, Traffic management, Process (computing)

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