驶向智能未来:深度学习在轨道交通革新中的应用

doi:10.11896/jsjkx.240300099

Abstract

Abstract: Nowadays,rail transit plays a crucial role in urban transportation due to its convenience and efficiency.However,the operation of existing rail transit systems faces complex challenges.Processes such as passenger flow prediction and train scheduling still rely on manual methods,leading to low efficiency and accuracy,which has a certain impact on the system’s performance.In recent years,with the flourishing development of deep learning,its powerful feature extraction and image recognition capabilities provide more possibilities for the automation and intelligence of rail transit.This paper first outlines the challenges faced by current rail transit in various real-life scenarios,and then analyzes the main applications of deep learning in the rail transit field,including perception tasks,prediction tasks,optimization tasks,etc.Finally,the future development direction of deep learning in rail transit is prospected from four aspects:high-precision and robust safety detection,lightweight rail transit models,fully automated intelligent operation of rail transit,and efficient information processing through cloud computing and big data.

Key words: Deep learning, Rail transit, Perception, Prediction, Optimization

CLC Number:

TP181

SUN Yumo, LI Xinhang, ZHAO Wenjie, ZHU Li, LIANG Ya’nan. Driving Towards Intelligent Future:The Application of Deep Learning in Rail Transit Innovation[J].Computer Science, 2024, 51(8): 1-10.

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