Latest revision as of 20:20, 22 March 2021

Abstract

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.

Document type: Article

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The different versions of the original document can be found in:

• https://journalofbigdata.springeropen.com/track/pdf/10.1186/s40537-020-00324-7 under the license https://creativecommons.org/licenses/by/4.0

• http://link.springer.com/article/10.1186/s40537-020-00324-7,

https://doaj.org/toc/2196-1115 under the license cc-by

• http://link.springer.com/content/pdf/10.1186/s40537-020-00324-7.pdf,

http://link.springer.com/article/10.1186/s40537-020-00324-7/fulltext.html,

http://dx.doi.org/10.1186/s40537-020-00324-7

• https://journalofbigdata.springeropen.com/articles/10.1186/s40537-020-00324-7,

https://dblp.uni-trier.de/db/journals/jbd/jbd7.html#ShepelevAGS20,

https://link.springer.com/article/10.1186/s40537-020-00324-7,

https://doi.org/10.1186/s40537-020-00324-7,

https://academic.microsoft.com/#/detail/3040140180 under the license https://creativecommons.org/licenses/by/4.0