A road traffic flow forecasting method

Abstract

The invention discloses a road traffic flow prediction method, which comprises the following steps: (1) road traffic flow collection; (2) data processing and cleaning; (3) chaos initialization population particle parameters to be optimized; (4) after optimization The parameters of are used for LSVVM sample training; (5) To achieve the accuracy, the prediction result is required to be output. The invention adopts the improved particle swarm optimization algorithm to optimize the road traffic flow prediction method of the least squares support vector machine, and greatly improves the global optimal performance of the particles through the chaotic ergodicity and variable weight combination model, so that the support vector machine model parameters are relatively optimal. Optimization, so that the accuracy and speed of the prediction model are improved to a certain extent, and the generalization prediction error of the model for new data is lower, which improves the generalization performance.

Description

technical field [0001] The invention relates to a road traffic flow prediction method, which belongs to the technical field of road traffic flow prediction. Background technique [0002] At present, there are many methods for short-term traffic flow prediction, and the predecessors have done a lot of fruitful work: using the fruit fly algorithm to optimize the least squares support vector machine for traffic prediction, but the fruit fly algorithm is prone to "premature" and convergence Strange circle with low accuracy; use the improved BP neural network to predict the short-term traffic flow of a street, and the BP neural network adds momentum to the system to prevent the system from "slipping" the local solution, thereby avoiding system oscillation , but it still does not solve the problem that may fall into local optimum; based on the hybrid model of non-parametric regression and support vector machine regression, using the K nearest neighbor search mechanism to reconstru...

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Problems solved by technology

[0002] At present, there are many methods for short-term traffic flow prediction, and the predecessors have done a lot of fruitful work: using the fruit fly algorithm to optimize the least squares support vector machine for traffic prediction, but the fruit fly algorithm is prone to "premature" and convergence Strange circle with low accuracy; use the improved BP neural network to predict the short-term traffic flow of a street, and the BP neural network adds momentum to the system to prevent the system from "slipping" the local solution, thereby avoiding system oscillation , but it still does not solve the problem that may fall into local optimum; based on the hybrid model of non-parametric regression and support vector machine regression, using the K nearest neighbor search mechanism to reconstruct the state similar to the current traffic flow time series, support vector machine parameters and K The parameters adopt the grid division method, and it is difficult to find the global optimal solution or satisfactory solution; the wavelet neural network model is used to predict and analyze the road traffic flow, which combines the strong nonlinear ability of the neural network and the characteristics of wavelet local analysis. Frequency and window decompose the signal in the frequency domain and time domain, but its generalization ability is not high

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