Traffic index time sequence identification method based on autoregressive differential moving average-convolutional neural network

Abstract

The invention relates to a traffic index time sequence identification method based on autoregressive differential moving average-convolutional neural network, and the method comprises the steps: obtaining a traffic index original data set, and generating the traffic index time sequence; converting the traffic index time sequence into a stable sequence, and fitting an autoregressive differential moving average algorithm model according to a Bayesian information criterion matrix to achieve traffic index prediction; generating a training traffic index time sequence and a test traffic index time sequence according to the traffic index original data set, extracting traffic index feature information according to the training traffic index time sequence, and obtaining an optimal convolutional neural network model, and integrating the traffic index feature information into a one-dimensional feature vector by using the optimal convolutional neural network model, determining a pattern category of the one-dimensional feature vector according to a Softmax classifier, and further identifying a category to which a test traffic index time sequence belongs.

Description

technical field [0001] The invention relates to a method for identifying traffic index time series based on autoregressive differential moving average-convolutional neural network. Background technique [0002] Traffic is the lifeblood of a city. With the rapid development of urban economy, the problem of traffic congestion is becoming more and more serious, and the more developed the economy is, the more prominent it is. In order to cope with the complex and changeable traffic conditions in the city and alleviate the urban traffic pressure, the traffic management department has issued a series of traffic laws and regulations to restrict driving regulations, and scientific research institutions have also used technologies such as the Internet of Things to assist the traffic control department in monitoring road congestion. Although various industries have achieved certain results in reducing the risk of traffic congestion in various aspects, the negative impact of traffic c...

AI Technical Summary

Problems solved by technology

Although both types of pattern recognition methods can obtain good classification results under specific conditions, they are affected by multi-party traffic factors, and there are certain distortions and deformations in the traffic index time series data itself. Therefore, the traditional distance-based and feature-based methods The recognition method still has some deficiencies in the pattern recognition ability of traffic index time series data

Images