Bayesian network-based multi-data fusion algorithm

Abstract

The invention belongs to the road safety field and relates to a Bayesian network-based multi-data fusion algorithm. The algorithm involves linearity, traffic volume, climate environment, historical accidents, roadside features, roadside traffic accident occurrence probability, and monitoring opinions, wherein the linearity includes an average curve and a longitudinal gradient, the traffic volume includes daily average traffic volume and the proportion of trucks, the climatic environment includes the frequencies of rain, snow and fog climate and the frequencies of other natural disasters, the historical accidents include wounding accidents, fatal accidents and property loss accidents, the roadside features include roadside depth, discrete hazardous materials, continuous hazardous materialsand clear zone conditions, and the monitoring opinions are variables. According to the algorithm of the present invention, the Bayesian network, serving as a reliability analysis means, processes complex logic relationships, and therefore, multi-state variables and related lines between the variables can be processed conveniently, and uncertainty relationships between the variables can be well expressed. The algorithm can have high real-time performance and is scientific and reasonable.

Description

technical field [0001] The invention relates to the field of road safety, in particular to a Bayesian network-based multi-data fusion algorithm. Background technique [0002] my country is one of the countries with the most road traffic safety accidents in the world today, and the death rate of traffic accidents is more than 10 times higher than that of developed countries such as Europe and the United States. Roadside traffic accidents account for about 30% of road traffic accidents. Among the serious and serious traffic accidents with more than 3 deaths, about accounted for half of the proportion. At present, the implementation of safety on the roadside of the highway still mainly relies on the experience and judgment of engineers and technicians to identify dangerous points and sections, resulting in certain human factors in safety monitoring, lack of scientific investment decision-making, and a high accident rate. [0003] In the prior art, the degree of roadside safet...

AI Technical Summary

Problems solved by technology

[0003] In the prior art, the degree of roadside safety is divided according to grades. This division is based on the main factors that affect the degree of roadside safety. They are all qualitative expressions. Because there are many factors that affect the degree of roadside danger, and the differences between factors Interrelated, so that the specific roadside characteristics are still inseparable from the design and the empirical judgment of researchers to a large extent

Although the existing roadside hazard classification has played a certain role in the specific construction time, there are still obvious problems in the following aspects: first, there are few elements to consider; second, the boundaries between the grades are blurred, and there are intersections and gaps ; The third is the lack of underlying theoretical and methodological foundation

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