A Method of Short-term Traffic Flow Prediction Based on Gray Elm Neural Network

Abstract

The invention provides a short-term traffic flow forecasting method based on a gray ELM neural network. The method comprises the steps that collected data are grouped to acquire equal dimension innovation sequences, and then accumulating is carried out to acquire an accumulated equal dimension innovation sequence; the accumulated equal dimension innovation sequence is processed to acquire the input matrix and the target output matrix of a network; the weight and threshold of the network are randomly generated; the network parameters are set; the generated input matrix set and target output matrix set of the network are input into the neural network, and the network is trained; test data are input to acquire the forecasted output result of the network; and the network forecast result minus the cumulative value of the equal dimensional innovation sequences to acquire an actual forecast result to complete the forecast. The method provided by the invention has the advantages that the input data are processed by a gray model; the difference is smaller; and the forecasting accuracy of the gray ELM neural network is greatly improved.

Description

technical field [0001] The invention relates to the technical field of short-term traffic flow prediction, in particular to a method for short-term traffic flow prediction based on gray ELM neural network. Background technique [0002] With the development of the economy, the demand for automobiles continues to increase, and the traffic flow on the highway also increases, which brings a series of traffic problems. Without changing the current road network, it is an effective way to solve traffic problems through the intelligent traffic control system to realize the dredging and control of the road network. Accurate traffic flow prediction is the basis of traffic flow guidance and control, and is an important part of intelligent traffic management system. [0003] Traffic flow itself has strong uncertainty, is complex, changeable, susceptible to random disturbance, and the regularity is not obvious, with the introduction of different forecasting methods, there are many predi...

AI Technical Summary

Problems solved by technology

[0003] Traffic flow itself has strong uncertainty, is complex, changeable, susceptible to random disturbance, and the regularity is not obvious, with the introduction of different forecasting methods, there are many predictions of short-term traffic flow. Forecasting models, but existing forecasting methods require high data volatility and are prone to distortion

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