A Traffic Flow Prediction Method Based on Bidirectional Nested LSTM Neural Network

Abstract

A traffic flow prediction method based on bidirectional nested LSTM neural network, the method obtains the traffic flow data of the predicted road section and the K most relevant road sections based on the road traffic flow correlation matrix, constructs the road traffic flow spatiotemporal matrix data set and performs data sequence Then build a two-way nested LSTM neural network prediction model, define the prediction model loss function, and combine the training set data to complete the model training; finally, the test set data is used as the input of the trained model to realize the real-time prediction of the test set traffic flow state And define the model evaluation standard, carry on the error analysis. The invention improves the temporal feature extraction capability of road traffic flow data by improving the time level effect of LSTM units and considering the connection between the future, historical traffic flow state and the existing state, thereby improving the prediction accuracy of road traffic flow.

Description

technical field [0001] The invention belongs to the field of traffic prediction and relates to a traffic flow prediction method based on a bidirectional nested LSTM neural network. Background technique [0002] With the continuous advancement of urban modernization, people's living standards continue to improve, and the number of vehicles per capita continues to increase. The existing urban road traffic network construction is difficult to meet the growing demand for road traffic travel. Road congestion under road traffic construction. As an important part of the intelligent transportation system, the road traffic flow prediction can not only assist the road traffic management department to control and guide the road traffic flow, but also provide a basis for people to make more reasonable travel decisions. [0003] Existing road traffic flow forecasting methods can be divided into three categories: The first category is traditional forecasting models based on mathematical ...

AI Technical Summary

Problems solved by technology

[0004] In order to overcome the shortcomings of the low prediction accuracy of existing traffic flow prediction methods, the object of the present invention is to provide a traffic flow prediction method based on bidirectional nested LSTM neural network

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