Traffic state prediction method based on chaos theory and device thereof

Abstract

The invention discloses a traffic state prediction method based on the chaos theory and a device thereof. The traffic state prediction method comprises the steps that data stream of traffic roads are acquired so that time sequences of multiple traffic parameters are obtained; multi-parameter phase space reconstruction is performed according to the time sequences of the traffic parameters and multi-parameter phase space is obtained, and optimal fusion of phase points is performed through combination of the Bayes estimation theory in the multi-parameter phase space so that fused phase space corresponding to the multiple traffic parameters is obtained; chaos analysis is performed on the time sequences in the fused phase space, and chaos prediction is performed on the traffic roads through combination of an RBF neural network when the time sequences of the fused phase space present chaos characteristics through analysis. Compared with a conventional single-parameter time sequence prediction method, a better prediction effect can be acquired by the traffic state prediction method so that predictability and precision of the traffic state prediction method are relatively high.

Description

technical field [0001] The invention relates to the technical field of intelligent transportation, in particular to a traffic state prediction method based on chaos theory and a device thereof. Background technique [0002] As the traffic problem becomes more and more severe, the pressure on the road traffic system brought by the sharp increase of motor vehicles can no longer be relieved only by improving the road infrastructure and hardware facilities, and the problem of traffic congestion is becoming more and more prominent. Therefore, how to accurately predict the change trend of traffic parameters corresponding to the time series in a short period of time is one of the foundations for formulating measures to alleviate traffic congestion. Traffic guidance and control is an important part of the intelligent transportation system (Intelligent Transportation System, ITS), the short-term real-time accurate time series prediction of traffic parameters is the premise and key to...

AI Technical Summary

Problems solved by technology

However, the traffic system is essentially an open, nonlinear, and pan-temporal complex system. It is difficult for a single traffic parameter to accurately describe the intricate movement process of the traffic system, nor can it adapt to the current time-varying and complex road traffic environment.

Images