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

A technology of neural network and prediction method, applied in the field of traffic flow prediction based on bidirectional nested LSTM neural network, which can solve the problem of low prediction accuracy

Active Publication Date: 2021-01-01
ZHEJIANG UNIV OF TECH
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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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  • A Traffic Flow Prediction Method Based on Bidirectional Nested LSTM Neural Network
  • A Traffic Flow Prediction Method Based on Bidirectional Nested LSTM Neural Network
  • A Traffic Flow Prediction Method Based on Bidirectional Nested LSTM Neural Network

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Embodiment Construction

[0075] The present invention will be further described below in conjunction with accompanying drawing.

[0076] refer to Figure 1 ~ Figure 3 , a traffic flow prediction method based on a bidirectional nested LSTM neural network, comprising the following steps:

[0077] (1) Obtain the road traffic correlation matrix based on the state correlation of road traffic flow, the process is as follows;

[0078] 1.1 Select a certain area of ​​the road traffic network as the research object to obtain road traffic flow data;

[0079] 1.2 Calculation of road traffic flow correlation matrix

[0080] Select the traffic flow data of different road sections in the same historical stage in the study area, and calculate the regional road section correlation matrix based on the traffic flow data. Define the traffic flow data of road segment i in the period a-b as X i ={x ia ,x ia+1 ,...,x ib}, the traffic flow data of section j in the period a-b is X j ={x ja ,x ja+1 ,...,x jb}, calcu...

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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 ...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G08G1/01G06Q10/04G06Q50/26G06N3/04
CPCG08G1/0129G06Q10/04G06Q50/26G06N3/045
Inventor 徐东伟彭鹏王永东戴宏伟宣琦
Owner ZHEJIANG UNIV OF TECH
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