Abstract
Since traffic is a time-dependent and complicated non-linear system. Chaos Theory has been specifically designed to identify chaotic behaviour and properties for such systems. Previous researches have been restricted to single traffic parameter, which fails to get actual behaviour of the traffic systems. In this work, we have devised the multi-parameters chaos prediction method to describe the tendency of traffic from different aspects for prediction of single parameter chaotic time series. The fusion method is considered the relationship of different traffic parameters according to the phase space reconstruction. Taking the exit ramp of Xishanping expressway in Beibei district, Chongqing Municipality as the example, the feasibility and reliability of traffic state prediction were tested. The trail results indicates that more features of realistic traffic conditions are reflected by fusing multiple traffic parameters, and gain real accuracy improvements in traffic prediction. Compared with three single-parameter time series prediction methods, the mean absolute relative error of the multi-parameter prediction method decreased by 2.42, 2.39 and 0.8 respectively, the mean absolute relative error decreased by 2.33, 3.25 and 1.27, and the equal coefficient reached 0.9528 with a slight increase. Proposed method will definitely generates the opportunity for next generation of traffic control that are better able to detect the dynamic states of traffic, and therefore more effectively prevent the traffic congestion and pollution in the urban areas worldwide.
Highlights
With the upward development of the economy, as the vehicle ownership increased rapidly, results in an increase in the road traffic on daily basis
Several literatures demonstrate that traffic state predictions can enhance the stability of traffic flow [4]
The common traffic state prediction time series [6] uses neural network[7], support vector machine [8] and the grey theory [9]. These are based on single traffic parameter prediction model which cannot completely reflect the intricate movements of transport system [10]
Summary
With the upward development of the economy, as the vehicle ownership increased rapidly, results in an increase in the road traffic on daily basis. The common traffic state prediction time series [6] uses neural network[7], support vector machine [8] and the grey theory [9] These are based on single traffic parameter prediction model which cannot completely reflect the intricate movements of transport system [10]. Literature [16] uses the time series of three traffic indicators to reconstruct the phase space, and combines the multi-source data with the Bayesian estimation theory to design the traffic flow prediction neural network based on RBF, which greatly improves the accuracy of the short-term traffic flow prediction. On the basis of chaotic model, an increase in the amount of reconstruction phase space systems’ information makes trajectories closer to the real traffic availability in the phase space phase point This increase impacts the establishment of more traffic parameters. The results of this experiment show that this method has high prediction accuracy after predicting one or more steps for the integration of new parameters
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