Traffic Flow Prediction Method Based on Balanced Exponential Smoothing Method and Stacked Autoencoder

Abstract

The invention provides a traffic flow prediction method based on a balance index smoothing method and a stack self encoder. The method comprises the following steps of (1) generating a training sequence and a test sequence; (2) performing preprocessing on the training sequence and the testing sequence; (3) obtaining a balance index value sequence by using the balance index smoothing method; (4) calculating the normalization value of each value in the balance index value sequence of a complete training traffic flow sequence by using a minimum maximum normalization formula, and obtaining the normalization value sequence of the complete training and test traffic flow sequence; (5) building a stack self encoder neural network; (6) training the stack self encoder neural network; and (7) testingthe normalization value sequence of the complete test traffic flow data sequence. By using the method provided by the invention, the complicated nonlinear characteristics of the traffic flow data arealso considered on the basis of considering the high-random performance of the traffic flow data; and the prediction precision of the traffic flow is improved.

Description

technical field [0001] The invention belongs to the field of control, and further relates to a traffic flow prediction method based on a balanced exponential smoothing method and a stacked self-encoder in the field of intelligent traffic. The present invention can predict the current traffic flow by acquiring the historical traffic flow data of the expressway. Background technique [0002] Intelligent Transportation System (ITS) is a real-time, accurate and efficient intelligent transportation network management system, which effectively integrates advanced information technology, communication technology, sensor technology, control technology and computer technology, and is an all-round solution to traffic problems. Congestion and an effective means to ensure the safety of traffic network transportation. Constructing the traffic flow guidance subsystem in ITS is one of the most effective ways to solve urban traffic congestion and improve the efficiency of road network traf...

AI Technical Summary

Problems solved by technology

[0005] The purpose of the present invention is to address the above-mentioned deficiencies in the prior art, and propose a traffic flow prediction method of balanced exponential smoothing and stacked self-encoder, which solves the high randomness problem of traffic flow through balanced exponential smoothing, and uses stacked self-encoder Solving the complex nonlinear representation of traffic flow with neural network

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