铁道运输与经济2023，Vol.45Issue(12)：163-170,180,9.DOI:10.16668/j.cnki.issn.1003-1421.2023.12.21
基于LSBAS-BP模型的铁路车站人员物品危险性预测
Risk Prediction of Railway Station Personnel and Objects Based on the LSBAS-BP Model
摘要
Abstract
Security inspections,as a crucial entry point for passengers at railway,directly impact the safety of railway operations.To effectively identify potential safety hazards,a simple and accurate model for predicting the danger level of personnel and objects has been developed.Firstly,criteria for categorizing the danger levels of personnel and objects were proposed,classifying them into different levels.Secondly,by introducing the Local Search(LS),a BP neural network model based on the improved Local Search Beetle Antennae Search(LSBAS)was established to enhance the algorithm's search accuracy.Additionally,preventive and control measures for personnel and objects of different danger levels were provided.Finally,the LSBAS-BP model was simulated and tested.The results demonstrate that the improved LSBAS algorithm has higher search accuracy compared with the traditional BAS algorithm.Furthermore,the BP neural network model optimized using the LSBAS algorithm outperforms BP,BAS-BP,and GA-BP in terms of performance and robustness.关键词
安检/人员物品危险等级/LSBAS/BP神经网络/危险性预测Key words
Security Inspections/Risk Level of Personnel and Objects/LSBAS/BP Neural Network/Risk Prediction分类
交通工程引用本文复制引用
白伟,伍柳伊,吕晓军,毋健,杨帆,卫丽娟..基于LSBAS-BP模型的铁路车站人员物品危险性预测[J].铁道运输与经济,2023,45(12):163-170,180,9.基金项目
国家自然科学基金项目(61972016) （61972016）
中国铁道科学研究院集团有限公司科研项目(2021YJ183,2023YJ125) （2021YJ183,2023YJ125）
