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中国道路交通伤害的时间序列分析
引用本文:文进,袁萍,邓振华,刘宽林,张跃康,刘力克,孔斌,黄思兴.中国道路交通伤害的时间序列分析[J].四川大学学报(医学版),2005,36(6):866-869.
作者姓名:文进  袁萍  邓振华  刘宽林  张跃康  刘力克  孔斌  黄思兴
作者单位:1. 四川大学华西医院,中国循证医学中心,成都,610041
2. 四川大学华西公共卫生学院,流行病学教研室
3. 四川大学华西基础与法医学院,病理学教研室
4. 成都市公安局交通管理局事故预防处理处
5. 四川大学华西医院,脑外科
基金项目:四川省科技厅重点科技项目(2000-169)资助.
摘    要:目的 建立我国道路交通伤害的预测模型,以期从宏观上掌握我国道路交通伤害的发生和变化趋势,为控制我国道路交通伤害提供参考依据。方法 收集我国1951-2003年的道路交通伤害资料.进行时间序列分析,建立自回归-求和-移动平均模型(ARIMA模型)。结果 建立了我国道路交通伤害事故数、万车死亡率和10万人死亡率各自的ARIMA模型方程,显示预测值与实际值接近。结论 时间序列模型在道路交通伤害预测中具有较好的应用价值。

关 键 词:道路交通伤害  时间序列分析  自回归-求和-移动平均模型
收稿时间:2005-01-12
修稿时间:2005-04-03

Time-series Analysis on Road Traffic Injury in China
WEN Jin,YUAN Ping,DENG Zhen-hua,LIU Kuan-lin,ZHANG Yue-kang,LIU Li-ke,KONG Bin,HUANG Si-xing.Time-series Analysis on Road Traffic Injury in China[J].Journal of West China University of Medical Sciences,2005,36(6):866-869.
Authors:WEN Jin  YUAN Ping  DENG Zhen-hua  LIU Kuan-lin  ZHANG Yue-kang  LIU Li-ke  KONG Bin  HUANG Si-xing
Institution:Chinese Center for Evidence-Based Medicine, West China Hospital, Sichuan University, Chengdu 610041, China.
Abstract:Objective To establish the predictive models of road traffic injury(RTI) in China,to know the trend of RTI,and to provide the reference data for controlling RTI in China.Methods The China RTI data from 1951 to 2003 were collected,and in view of the problem of missing values,the method of intrapolation was adopted.The Box-Jenkins technique was used to analyze and predict the trend of RTI in China.Following the process for stationary time-series analysis,model identification,parameter estimation and model diagnosis,the predictive equation for RTI would be established.Results A series of predictive equations on RTI were finally established based on ARIMA models.The curve fitting is effective and the predictive data of RTI in 2003 are close to the true statistical data.Conclusion The time-series model thus established proves to be of significant usefulness in RTI prediction.
Keywords:Road traffic injury(RTI) Time-series analysis Autoregressive integrated moving average models(ARIMA models  Box-Jenkins models)
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