Traffic health prediction method based on naive Bayes

Abstract

The invention relates to a traffic health prediction method based on naive Bayes, and provides a new space-time dimension congestion clustering method from space-time feature congestion sub-groups of congestion to describe space-time evolution of congestion, and adopts a naive Bayes method to identify congestion scale increase distribution under different conditions, growth prediction of congestion sub-groups is realized through Markov chain and Monte Carlo simulation. The method can predict scale change of congestion gathering in an area, and traffic health prediction based on a naive Bayes method is finally realized in combination with a control theory method.

Description

technical field [0001] The invention relates to the technical field of traffic supervision, in particular to a naive Bayesian-based traffic health prediction method and system. Background technique [0002] Rapid urbanization and the rapid increase in the number of private cars have led to serious urban traffic congestion, which cannot give full play to the service capacity of the transportation system, affecting the efficiency of urban operation and the quality of life of residents, the expansion of urban scale and the diversity of travel needs in cities It also makes the urban transportation system increasingly complex, and the sharply increased travel demand has led to serious urban traffic congestion, which will lead to high transportation costs, pollution and economic losses. In order to reduce these cost losses caused by traffic congestion, a reliable traffic health prediction method is urgently needed, and the full-cycle growth prediction of urban spatio-temporal cong...

AI Technical Summary

Problems solved by technology

However, urban congestion has the characteristics of dynamic propagation, cascading failure, and multiple failures. The existing congestion estimation and prediction methods do not consider these characteristics of urban congestion, resulting in inaccurate congestion estimation and prediction.

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