%0 Journal Article
%T Intelligent Optimization Strategy for Shunting Operation Efficiency at Railway Stations Based on Deep Reinforcement Learning and Infrastructure Upgrading
%A Siyuan Liu
%A Huibing Cheng
%A Shaohan Zhuo
%A Aiping He
%A Jincheng Zhou
%A Yueyu Guo
%A Dengheng Zheng
%J Open Access Library Journal
%V 13
%N 4
%P 1-15
%@ 2333-9721
%D 2026
%I Open Access Library
%R 10.4236/oalib.1115150
%X Shunting operation is the core link of railway station transportation organization, whose efficiency directly determines the turnover speed of trains, the utilization rate of station facilities and the overall operational efficiency of the railway network. With the rapid growth of rail freight and passenger transport volume, the traditional shunting operation mode, which relies on manual experience and backward equipment, has been difficult to adapt to the demand of modern railway intelligent development. To solve the problems of low efficiency, high labor intensity and poor real-time adaptability in current shunting operations, this paper systematically analyzes the key influencing factors of shunting efficiency from four dimensions: operation organization, equipment status, personnel quality and intelligent technology application. Based on the real operation data of 12 typical railway stations (6 freight stations, 4 passenger stations, 2 marshalling stations) in China, Kazakhstan and Uzbekistan surveyed from January 2024 to June 2024 and typical railway stations at home and abroad, combined with deep reinforcement learning (DRL), 5G   Beidou positioning and other advanced technologies, this paper proposes a multi-dimensional integrated optimization strategy for shunting operation efficiency. The experimental verification is carried out through live deployment for 6 months at Huanghua Port Station (China) and a large freight train depot in Kazakhstan. The results show that the proposed strategy can effectively reduce the number of shunting hooks, shorten the operation time and improve the utilization rate of shunting locomotives. The research results provide a practical and feasible technical path for the intelligent upgrading of shunting operations and the improvement of operation efficiency, which has important theoretical and engineering application value for promoting the high-quality development of railway transportation.<br />
%K Railway Station
%K Shunting Operation
%K Efficiency Improvement
%K Deep Reinforcement Learning
%K Intelligent Upgrading
%K Infrastructure Optimization
%U http://www.oalib.com/paper/6892469