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Solution of the Problems of Creating Intelligent Transport Systems Based on Neural Network Modeling

  • 2025
  • OriginalPaper
  • Chapter
Published in:
Authors
Olga Kharitonova
Veronika Bronskaya
Denis Balzamov
Tatiana Ignashina
Alsu Lubnina
Published in
Transportation Issues: From Energy Efficiency to Security and Traffic Automation
Publisher
Springer Nature Switzerland

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Abstract

This chapter delves into the creation of intelligent transport systems through neural network modeling, focusing on solving traffic congestion and optimizing flow. The main topics covered include the classification of traffic models, the application of neural networks to predict traffic density, and the implementation of a Windows Forms application for practical use. The chapter also discusses the theoretical basis of traffic flow phases and the advantages of neural network modeling over traditional methods. The developed neural network, implemented in C# using the AForge Neuro library, determines traffic density and intensity based on experimental data. The application includes features for loading training samples, setting training accuracy, and calculating theoretical values. The results demonstrate the effectiveness of the neural network model in predicting traffic flow, making it a valuable tool for transportation engineers and urban planners.
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previous chapter Lifecycle Architecture of Fault-Tolerant Supply Chains
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Title
Solution of the Problems of Creating Intelligent Transport Systems Based on Neural Network Modeling
Authors
Olga Kharitonova
Veronika Bronskaya
Denis Balzamov
Tatiana Ignashina
Alsu Lubnina
Copyright Year
2025
Book
Transportation Issues: From Energy Efficiency to Security and Traffic Automation

Print ISBN: 978-3-031-86820-7

Electronic ISBN: 978-3-031-86821-4

Copyright Year: 2025

DOI
https://doi.org/10.1007/978-3-031-86821-4_7
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