数据驱动的铁道行车安全预警模型构建及在运输组织中的应用

Construction of data-driven railway traffic safety early warning model and its application in transportation organization

摘要

随着铁路运输密度和速度不断增加，以往依靠人工巡检和经验判断的安全管理模式，已无法适应高密度、高速度运营的要求。本文提出一种依托多源数据融合的铁道行车安全预警模型，借助融合列车运行监控记录、轨道状态检测数据、环境气象信息和历史事故数据，搭建了基于机器学习与统计分析的综合预警体系。模型借助改进后的随机森林算法开展特征重要性评估，将时序异常检测与空间关联分析相结合，达成对潜在安全隐患的实时识别与分级预警，某繁忙铁路干线的试点应用证明，该模型可提前发出预警，增强对重大风险事件的识别精度，大幅缩短调度响应时间并降低事故发生率，为铁路运输安全与效率协同提升提供可行途径。

Abstract

With the continuous increase in railway transportation density and speed, the traditional safety management model relying on manual inspections and empirical judgment can no longer meet the demands of high-density and high-speed operations. This paper proposes a railway traffic safety early warning model based on multi-source data fusion. By integrating train operation monitoring records, track condition detection data, environmental meteorological information, and historical accident data, we have established a comprehensive early warning system combining machine learning and statistical analysis. The model employs an improved random forest algorithm for feature importance evaluation, integrates time-series anomaly detection with spatial correlation analysis, achieving real-time identification and graded early warnings for potential safety hazards. Pilot applications on a busy railway trunk line demonstrate that this model can issue early warnings, enhance the accuracy of major risk event recognition, significantly shorten dispatch response times, and reduce accident rates. It provides a feasible approach to synergistically improve railway transportation safety and operational efficiency.

关键词

铁道行车安全 / 数据驱动 / 预警模型 / 运输组织 / 机器学习 / 多源数据融合

Key words

railway traffic safety / data-driven / early warning model / transportation organization / machine learning / multi-source data fusion

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