Multi-source vehicle speed fusion method for optimizing radial basis function neural network based on hunting algorithm

Abstract

The invention discloses a multi-source vehicle speed fusion method for optimizing a radial basis function neural network based on a hunting algorithm, and the method comprises the steps: obtaining the site vehicle speed of a certain section of a target road segment through three site vehicle speed collection modes, and dividing the site vehicle speed into a fusion group and a control group; clustering is carried out on the fusion group by adopting K-means clustering, and an optimal clustering center number K is determined through elbow method; taking the optimal clustering center number K as the number of neurons of the radial basis function neural network; according to the number of neurons, adopting a Gaussian function as a kernel function to construct a radial basis function neural network; taking the fusion group as the input of a radial basis function neural network, comparing the output of the neural network with a corresponding control group, and taking a comparison result as a fitness function; and (S5) optimizing the parameters of the radial basis function neural network by using a hunting algorithm and a fitness function to obtain each parameter of the radial basis function neural network, and performing multi-source vehicle speed fusion by using the radial basis function neural network. According to the method, the location and the vehicle speed can be accurately and efficiently fused.

Description

technical field [0001] The invention relates to the field of traffic information and control, in particular to a multi-source vehicle speed fusion method based on a hunting algorithm to optimize a radial basis neural network. Background technique [0002] With the development of the economy, the number of motor vehicles in urban residents has increased steadily, and the urban road traffic control has become increasingly complex. Acquiring accurate traffic data is the basis for urban traffic control. The vehicle speed includes the location speed, driving speed, travel speed, etc. The location speed represents the congestion degree of the target road section, and the change of the vehicle speed indicates the future traffic flow and density of the road section. It is of great significance to estimate the traffic conditions of the target road section and adjust the timing of traffic signals. At present, more and more new technologies are applied to the measurement of vehicle sp...

AI Technical Summary

Problems solved by technology

However, in the calculation process, it is easy to fall into the problem of local optimum. At the same time, some particles involved in the calculation tend to maintain the current state after several rounds of iterations in the middle iteration process, thereby reducing the calculation speed and causing a waste of computing resources. The hunting algorithm can effectively solve the problem

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