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<p>To predict and analyze road traffic crash-related mortality rates across different groups in Shandong Province using the GM (1,1) model and the GM-BP joint model. We also sought to establish the optimal model and provide a theoretical basis for road traffic crash prevention and control. Herein, road traffic fatality-related data between 2012 and 2022 in Shandong Province were collected using the ICD-10 codes in the Population Death Information Registration Management System of the Chinese Center for Disease Control and Prevention. After cleaning and collating the data, the Grey Modeling Software was used to construct the GM (1,1) model and the SPSSPRO software was used to train the BP neural network model to build the GM-BP joint model based on relevant information from the GM (1,1) model. The two predictive models were evaluated using Mean Squared Error (MSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Root Mean Squared Error (RMSE). Between 2012 and 2022, there were 176,129 victims of road traffic crash-related fatalities in Shandong Province, with an average age of 52.65 ± 17.94 years. The gender ratio was around 2.77:1, and the average standardized mortality rate for the total population was 18.59/100,000 persons. According to the model evaluation results, compared to the GM-BP joint model, the GM (1,1) model had smaller MSE, MAE, MAPE, and RMSE values for the total population and motorized drivers but larger values for pedestrians, non-motorized drivers, and passengers. In cases of slightly changing data, the GM-BP joint model can effectively leverage the advantages of the GM (1,1) model and the BP neural network model, improving the prediction accuracy and reliability. Our findings could provide critical supportive data and decision-making references for road traffic management authorities, facilitating the development of prevention and control programs for road traffic safety tailored for specific groups of road users (e.g., non-motorized drivers).</p>

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The research of predictive models for road traffic fatalities in Shandong Province, China

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年4月26日

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  • Tao Wang,
  • Jie Chu,
  • Zhi-ying Yao,
  • Xiao-Lei Guo,
  • Yu-Bin Ma,
  • Zi-Long Lu,
  • Cun-Xian Jia

摘要

To predict and analyze road traffic crash-related mortality rates across different groups in Shandong Province using the GM (1,1) model and the GM-BP joint model. We also sought to establish the optimal model and provide a theoretical basis for road traffic crash prevention and control. Herein, road traffic fatality-related data between 2012 and 2022 in Shandong Province were collected using the ICD-10 codes in the Population Death Information Registration Management System of the Chinese Center for Disease Control and Prevention. After cleaning and collating the data, the Grey Modeling Software was used to construct the GM (1,1) model and the SPSSPRO software was used to train the BP neural network model to build the GM-BP joint model based on relevant information from the GM (1,1) model. The two predictive models were evaluated using Mean Squared Error (MSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Root Mean Squared Error (RMSE). Between 2012 and 2022, there were 176,129 victims of road traffic crash-related fatalities in Shandong Province, with an average age of 52.65 ± 17.94 years. The gender ratio was around 2.77:1, and the average standardized mortality rate for the total population was 18.59/100,000 persons. According to the model evaluation results, compared to the GM-BP joint model, the GM (1,1) model had smaller MSE, MAE, MAPE, and RMSE values for the total population and motorized drivers but larger values for pedestrians, non-motorized drivers, and passengers. In cases of slightly changing data, the GM-BP joint model can effectively leverage the advantages of the GM (1,1) model and the BP neural network model, improving the prediction accuracy and reliability. Our findings could provide critical supportive data and decision-making references for road traffic management authorities, facilitating the development of prevention and control programs for road traffic safety tailored for specific groups of road users (e.g., non-motorized drivers).