<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>The paper considers an approach to building various mathematical models for homogeneous groups of intersections manifested through the use of clustering methods. This is because of a significant spread in their traffic capacity, as well as the influence of several random factors. The initial data on the traffic flow of many intersections was obtained from real-time recorders of the convolutional neural network. As a result of the analysis, we revealed statistically significant differences between the groups of intersections and compiled their linear regression models as a basis for the subsequent formation of generic management decisions. To demonstrate visually the influence of random factors on the traffic capacity of intersections, we built distribution fields based on the fuzzy logic methods for one of the clusters consisting of 14 homogeneous intersections. Modeling was based on the Gaussian type of membership functions as it most fully reflects the random nature of the pedestrian flow and its discontinuity.</p>
</abstract>

The paper considers an approach to building various mathematical models for homogeneous groups of intersections manifested through the use of clustering methods. This is because of a significant spread in their traffic capacity, as well as the influence of several random factors. The initial data on the traffic flow of many intersections was obtained from real-time recorders of the convolutional neural network. As a result of the analysis, we revealed statistically significant differences between the groups of intersections and compiled their linear regression models as a basis for the subsequent formation of generic management decisions. To demonstrate visually the influence of random factors on the traffic capacity of intersections, we built distribution fields based on the fuzzy logic methods for one of the clusters consisting of 14 homogeneous intersections. Modeling was based on the Gaussian type of membership functions as it most fully reflects the random nature of the pedestrian flow and its discontinuity.

Development of Reliable Models of Signal-Controlled Intersections

South Ural State University (national research university) Chelyabinsk

Transport and Telecommunication Journal

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

{"article-title":"Development of Reliable Models of Signal-Controlled Intersections"}

The paper considers an approach to building various mathematical models for homogeneous groups of intersections manifested through the use of clustering...

Development of Reliable Models of Signal-Controlled Intersections

Online veröffentlicht: 20. Nov. 2021

Seitenbereich: 417 - 424

DOI: https://doi.org/10.2478/ttj-2021-0032

Schlüsselwörter
Signal-controlled transport network, right turn, clustering of intersections, statistical significance of differences, regression analysis, fuzzy logic

© 2021 Alexandr Glushkov et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Development of Reliable Models of Signal-Controlled Intersections

Online veröffentlicht: 20. Nov. 2021

Seitenbereich: 417 - 424

DOI: https://doi.org/10.2478/ttj-2021-0032

SchlüsselwörterSignal-controlled transport network, right turn, clustering of intersections, statistical significance of differences, regression analysis, fuzzy logic

© 2021 Alexandr Glushkov et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Schlüsselwörter
Signal-controlled transport network, right turn, clustering of intersections, statistical significance of differences, regression analysis, fuzzy logic