ArticleScientific reports2025
The research of predictive models for road traffic fatalities in Shandong Province, China.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- Spatio-temporal distribution and aggregation analysis of road traffic fatalities in Shandong Province, China, 2012-2022.Frontiers in public health · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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).
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.