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彭振仁, 杨莉, 刘勇, 张海英, 陈世艺, 尹晔, 覃莉. 南宁市2000-2009年道路交通伤害时间序列分析[J]. 中国公共卫生, 2012, 28(5): 574-575. DOI: 10.11847/zgggws2012-28-05-03
引用本文: 彭振仁, 杨莉, 刘勇, 张海英, 陈世艺, 尹晔, 覃莉. 南宁市2000-2009年道路交通伤害时间序列分析[J]. 中国公共卫生, 2012, 28(5): 574-575. DOI: 10.11847/zgggws2012-28-05-03
PENG Zhen-ren, YANG LI, LIU Yong, . Time series analysis on road traffic injury in Nanning city, 2000-2009[J]. Chinese Journal of Public Health, 2012, 28(5): 574-575. DOI: 10.11847/zgggws2012-28-05-03
Citation: PENG Zhen-ren, YANG LI, LIU Yong, . Time series analysis on road traffic injury in Nanning city, 2000-2009[J]. Chinese Journal of Public Health, 2012, 28(5): 574-575. DOI: 10.11847/zgggws2012-28-05-03

南宁市2000-2009年道路交通伤害时间序列分析

Time series analysis on road traffic injury in Nanning city, 2000-2009

  • 摘要: 目的 建立广西自治区南宁市道路交通伤害的预测模型,掌握南宁市道路交通伤害的发生和变化趋势,为预防和控制南宁市道路交通伤害提供参考依据.方法 收集南宁市2000-2009年道路交通伤害资料,进行时间序列分析,建立自回归求和移动平均模型(ARIMA模型),对南宁市2010-2011年道路交通伤害发生情况进行预测.结果 建立了南宁市道路交通伤害事故发生次数、死亡人数、受伤人数和直接经济损失各自的ARIMA模型,模型拟合与预测效果良好,预测模型均为ARIMA(1,0,0),2010年各指标的预测值依次为472次、145人、562人、157.0436万元;2011年各指标的预测值依次为464次、141人、527人、161.1209万元.结论 ARIMA模型在道路交通伤害预测中具有较好的应用价值.

     

    Abstract: Objective To establish predictive models for road traffic injury(RTI) in Nanning city,and to elucidate the trend of RTI for prevention and controll of RTI in Nanning.Methods The RTI data from 2000 to 2009 in Nanning city were collected and the autoregressive integrated moving average(ARIMA) model was used to analyze and predict the trend of RTI from 2010 to 2011 in Nanning.Results A series of predictive equations on RTI were established based on ARIMA model.The model fitting was effcetive and the predictive data on RTI were close to the true statistical data,and all the predictive models were ARIMA(1,0,0).The predictions for number of accident,number of people dead and injured due to injury,and direct economic lost were 472,145 and 562 persons,and 1 570 436 RMB yuan for the calendar year of 2010 and 464,141 and 527 persons,and 1 611 029 RMB yuan for 2011 based on the models.Conclusion The ARIMA model could be applied in RTI prediction effectively.

     

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