Traffic flow rate prediction method based on road clustering and two-layer two-way LSTM (long short-term memory)

Abstract

The invention discloses a traffic flow rate prediction method based on road clustering and two-layer two-way LSTM (long short-term memory). The method comprises the following steps of (1) providing a mode of using peripheral equalization on a loss value when the training data has a missing value, so that the missing data is filled; the prediction precision is improved; (2) providing a method of performing relevance clustering on the road according to the historical flow rate data; dividing the road into a plurality of groups; simultaneously utilizing the time information and the space information in the data preprocessing stage for improving the prediction precision; (3) designing a two-layer two-way LSTM deep neural network model for improving the prediction precision of the model; (4) providing a method of performing mass training and test on the network model; accelerating the training and test speed of the neural network model; (5) providing a multi-model fusion method for improving the prediction precision. The method provided by the invention has the advantages that the prediction speed and the prediction precision of the deep neural network model in an aspect of traffic flow rate prediction are accelerated and improved at the same time.

Description

A Traffic Flow Prediction Method Based on Road Clustering and Bidirectional LSTM technical field The present invention relates to technologies such as deep learning, traffic flow prediction, neural network design, and cluster training of neural network models, especially a deep neural network model based on road clustering and double-layer LSTM (Long Short-Term Memory, long-term short-term memory) The traffic flow forecasting method has guiding significance for improving the accuracy and speed of traffic flow forecasting. Background technique With the advent of the era of big data, real-time traffic network data in big cities is gradually increasing. As one of the most critical applications in the current popular unmanned driving technology, artificial intelligence traffic prediction can make reasonable predictions on traffic conditions after considering the relationship between time and space. , to help vehicles choose the most suitable route, especially in the case of ur...

AI Technical Summary

Problems solved by technology

Aiming at the current situation that the accuracy and speed of traffic flow prediction by neural network are difficult to improve and some deficiencies in existing designs, a traffic flow prediction method based on road clustering and double-layer bidirectional LSTM deep neural network model is proposed. Using the information hidden in the data and realizing the multiplexing of important information, the correlation clustering of roads improves the accuracy of traffic flow prediction, and avoids the communication and synchronization between nodes of the neural network model during cluster training, which improves the traffic flow prediction accuracy. Speed ​​of cluster training

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