A Combined Prediction Model Composed of the GM (1,1) Model and the BP Neural Network for Major Road Traffic Accidents in China

Abstract

This paper compared the expected accuracy of the gray GM (1, 1) model and the combined GMBP model using a data set for major road traffic accidents. A combined GMBP prediction model composed of the very first parameter gray model GM (1, 1) is able to make exact predictions for forecasting dreary type of processes, and BP (back-propagation) neural network for a major traffic accident is proposed to overcome the limitations of a single prediction model for a major traffic accident. The method first obtains predicted data using the gray GM (1, 1) model, then trains the BP neural network using the GM (1, 1) model’s predicted data and the original data as input and output data, respectively, and finally the trained BP neural network can be considered a combined GMBP prediction model. The predicted data of the digit of major traffic accidents, the digit of fatalities, and the digit of damages from 2008 to 2020 were obtained using the combined GMBP prediction model, and it is compared with the expected data of the single gray GM (1, 1) model. The grades showed that the exactitude of the combined GMBP prediction model was significantly higher than that of the single gray GM (1, 1) model. Finally, from 2021 to 2033, the combined GMBP prediction model is used to forecast the number of significant road traffic incidents, mortalities, and damages. The prediction results show that the number of accidents, faculties, and injuries is on the decline in the future years.

Research on the Prediction of Wax Deposition Thickness on Pipe Walls Based on the Optimal Weighted Combination Model

“…The first 1 within the brackets represents that the differential equation is first-order, and the second 1 represents that there is one variable in the equation. The modeling method is as follows [14,15,19,22]:…”

“…The gray system theory was proposed by Professor Deng in the 1980s, and is a system engineering discipline based on mathematical theory [13]. The GM (1,1) is the main model in gray system theory, which can be used to predict the change rules of data sequences [14]. For the GM (1,1), it has the advantage of requiring fewer samples and has been applied in many fields, such as environmental science, water conservation projects, ecological science, energy science, etc.…”

“…In addition to the gray model and the improved gray model, some scholars have established the combination prediction model and achieved good application results [14,23]. Deng established an improved combination model based on the GM (1,1) and linear regression model, and applied it to distortion inspections.…”

“…Guo et al established a combination model based on the GM (1,1) and the BP neural network model to predict major road traffic accidents in China. The authors pointed out that the combined prediction model takes into account both the advantages of the GM (1,1) (better prediction performance with small sample data) and the BP neural network model (nonlinear approximation), so the accuracy of the combined model is higher than that of the GM (1,1) and BP neural network model [14].…”

Geographical spatial analysis and risk prediction based on machine learning for maritime traffic accidents: A case study of Fujian sea area

“…As accident prediction is a basis for making a scientific safety decision, it is essential for accident prevention. Accident prediction is usually conducted by predicting future safety conditions of a system based on the past and present safety information of the system through a series of scientific methods (Guo et al, 2022). The spatial distribution characteristics of maritime accidents and accident prediction results can provide maritime authorities a more intuitive understanding of the traffic safety conditions of ships within their jurisdiction so that they can take targeted measures to reduce maritime accidents and ensure navigation safety (Wang et al, 2022).…”