摘要
道路旅客运输在交通客运中扮演着重要角色,在大数据时代背景下,解决行业安全管理定量化指标及抓手问题,对实现智能安全管控尤其重要。该文结合上海市交通委员会安全监管大数据平台和重点企业调研结果,建立安全指标体系,从人、车、环、企、管5类指标出发采用贝叶斯网络方法构造模型,能够有效诊断企业安全隐患、违法事故及安全标准化影响因素。结果表明:“营运车辆平均技术等级”“省际包车客运”“应急预案制定情况”等对系统安全性存在显著影响。模型可为道路旅客运输行业在安全运营和管理阶段提供量化评估和改善策略。
Road passenger transport plays an important role in passenger transportation.In the big data era,it is particularly important to solve the problem of quantified indicators and grips for industry safety management in order to achieve intelligent safety control.This paper,combining the results of the Shanghai Municipal Commission of Transport's safety supervision big data platform and key enterprises'investigation results,establishes a safety index system and constructs a model using Bayesian network methods from five categories of indicators:people,vehicles,environment,enterprises and management.It can effectively diagnose the hidden dangers,illegal accidents and safety standard influencing factors of enterprises.The results show that"average technical level of operating vehicles","interprovincial chartered passenger transport"and"development of emergency plans"have significant impacts on system safety.The model can provide quantitative assessment and improvement strategies for the road passenger transport industry in the safety operation and management stages.
作者
杨军
吕通通
张奕菁
Yang Jun;Lyu Tongtong;Zhang Yijing
出处
《交通与港航》
2022年第4期66-73,共8页
Communication & Shipping
关键词
道路旅客运输
安全诊断模型
贝叶斯网络
K2算法
Road passenger transport
Safety diagnostic model
Bayesian network
K2 algorithm
