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Optimization and Application of Communication Resource Allocation Algorithm for Urban Rail Transit Planning.
This study optimizes communication resource allocation for urban rail transit planning, improving network reliability and reducing errors by 58.6%. The new algorithm enhances the safety and efficiency of high-speed rail systems.
Area of Science:
- Engineering
- Computer Science
- Operations Research
Background:
- China's rail transit system is rapidly expanding, increasing train speeds and mileage.
- This expansion presents significant challenges to the safety and reliability of rail transit systems.
- Effective network planning evaluation is crucial for the success of urban rail transit projects.
Purpose of the Study:
- To study the optimization of communication resource allocation algorithms for urban rail transit planning.
- To comprehensively evaluate the application of these optimized algorithms.
- To address the difficulties in scientifically evaluating urban rail transit information resource network planning.
Main Methods:
- Utilized a Complex-Valued Neural Network (CVNN) structure, an extension of Recurrent Neural Network (RVNN).
- Employed a Hopfield Neural Network (HNN), a fully connected recurrent neural network, for combinatorial optimization problems.
- Integrated neural networks with genetic algorithms and a simulated annealing mechanism.
Main Results:
- The proposed optimization scheme achieved an average error reduction rate of 58.6%.
- Demonstrated an optimal bit error rate accuracy of 52.4% in practical applications.
- The CVNN and HNN approaches showed effectiveness in resource allocation and optimization.
Conclusions:
- The developed communication resource allocation algorithm effectively optimizes urban rail transit planning.
- The integration of neural network techniques offers a promising direction for enhancing rail transit network reliability.
- The findings contribute to improving the safety and efficiency of high-speed rail development.

