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Optimization Algorithm of Urban Rail Transit Network Route Planning Using Deep Learning Technology.

Yaqi Ma1

  • 1School of Transportation, Soochow University, Suzhou 215131, China.

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This study introduces a deep learning (DL) algorithm for optimizing urban rail transit network route planning. The developed method enhances real-time traffic flow prediction accuracy, improving network efficiency and passenger experience.

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Area of Science:

  • Transportation Engineering
  • Urban Planning
  • Artificial Intelligence

Background:

  • Optimizing existing urban rail transit networks is a current focus in transit development.
  • Efficient route planning is crucial for enhancing urban mobility and network performance.

Purpose of the Study:

  • To develop and validate a deep learning (DL) based algorithm for urban rail transit network route planning optimization.
  • To improve the accuracy of real-time traffic flow prediction within urban rail transit systems.

Main Methods:

  • Construction of an optimization algorithm for urban rail transit network route planning using deep learning (DL).
  • Development of suitable rail transit network line forms based on urban layout and planning.
  • Classification of urban rail transit station types by function and calculation of effective paths using the K-shortest path algorithm.
  • Allocation of passenger flow to calculated paths using a developed model.

Main Results:

  • The algorithm achieved a real-time traffic flow prediction accuracy of 94.98%, an improvement of approximately 9% over existing methods.
  • Demonstrated effective optimization of urban rail transit network route planning.
  • Verified the algorithm's effectiveness in achieving real-time, rational, and optimal line planning.

Conclusions:

  • The proposed DL-based algorithm significantly enhances the optimization of urban rail transit network route planning.
  • The algorithm provides accurate real-time traffic flow prediction, leading to improved network efficiency and planning.
  • Implementation of this algorithm facilitates better real-time performance, rationality, and optimality in urban rail transit line planning.