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为准确预测交通事故死亡人数,选取人、车、路和经济发展水平作为主要因素,建立GM(1,1)和Verhulst模型,进行事故预测和精度分析。结合马尔科夫方法,对已建立的模型进行修正,构建GM(1,1)-Markov,GM(1,3)-Markov和Verhulst-Markov模型。应用上述模型预测安徽省2012—2014年交通事故死亡人数。分析结果表明:应用GM(1,1)-Markov模型,3年预测值的相对误差分别为-8.4%,-12.81%和-13.18%;应用GM(1,3)-Markov模型,3年预测值的相对误差分别为-31.86%,-44.66%和-57.50%;应用Verhulst-Markov模型,3年预测值的相对误差分别为-2.68%,-2.88%和-2.42%。Verhulst-Markov模型的预测精度更高,可用来预测今后的道路交通事故死亡人数。
In order to accurately predict the number of deaths from traffic accidents, select the people, vehicles, roads and economic development level as the main factors to establish the GM (1,1) and Verhulst models for accident prediction and accuracy analysis. Combined with Markov method, the established model is modified to construct GM (1,1) -Markov, GM (1,3) -Markov and Verhulst-Markov models. Applying the above model to predict the number of fatal traffic accidents in Anhui province in 2012-2014. The analysis results show that the relative error of the three-year prediction is -8.4%, -12.81% and -13.18%, respectively, with GM (1,1) -Markov model. The GM (1,1) The relative errors of the three values were -31.86%, -44.66% and -57.50%, respectively. The relative errors of the three-year predictions were -2.68%, -2.88% and -2.42% respectively using the Verhulst-Markov model. The Verhulst-Markov model has higher prediction accuracy and can be used to predict the number of road traffic fatalities in the future.