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Traffic index prediction method and device based on sequence-to-sequence learning model

A traffic index and learning model technology, applied in the software field, can solve problems such as difficulty in solving, difficult training, and inability to capture the relationship between data, and achieve the effect of improving prediction accuracy and improving nonlinear changes.

Inactive Publication Date: 2019-12-27
BEIJING UNIV OF CIVIL ENG & ARCHITECTURE
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Problems solved by technology

However, this type of method also has disadvantages such as difficulty in solving and training.
For example, the BP model is prone to overfitting, and for complex data, the model cannot capture the relationship between the data and has poor predictive ability

Method used

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  • Traffic index prediction method and device based on sequence-to-sequence learning model
  • Traffic index prediction method and device based on sequence-to-sequence learning model
  • Traffic index prediction method and device based on sequence-to-sequence learning model

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Embodiment Construction

[0038] The present invention will be further described in detail below in conjunction with the accompanying drawings, so that those skilled in the art can implement it with reference to the description.

[0039] Such as figure 1 As shown, the present invention provides a traffic index prediction method based on a sequence-to-sequence learning model, including:

[0040] Step 101, obtain the input time series of traffic index. In step 101, traffic index data can be obtained from relevant traffic departments.

[0041] Step 102, the traffic index input time series is used as the input object of the sequence-to-sequence learning model; wherein, the sequence-to-sequence learning model is composed of an encoder network and a decoder network, and the encoder network uses LSTM units as The LSTM network of the basic cycle unit, the decoder network is an LSTM network with the LSTM unit as the basic cycle unit.

[0042]In step 102, the sequence-to-sequence learning model used is a pre-...

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Abstract

The invention discloses a traffic index prediction method and device based on a sequence-to-sequence learning model. The method comprises the steps of obtaining a traffic index input time sequence; taking the traffic index input time sequence as an input object from the sequence to a sequence learning model, wherein the sequence-to-sequence learning model is composed of an encoder network and a decoder network, the encoder network is an LSTM network taking an LSTM unit as a basic cycle unit, and the decoder network is an LSTM network taking an LSTM unit as a basic cycle unit; performing feature extraction on the traffic index input time sequence by using an encoder network to obtain a time change feature vector; and processing the time change feature vector by using a decoder network, andtaking a traffic index output time sequence obtained by processing as a prediction result. According to the method, the prediction precision of the traffic index is improved, and the nonlinear changeof the traffic index can be better described.

Description

technical field [0001] The invention relates to the field of software, in particular to a traffic index prediction method and device based on a sequence-to-sequence learning model. Background technique [0002] Traffic is equivalent to the blood of the city, which continuously delivers productivity to the city. The traffic index is a conceptual index value used to measure the current traffic congestion situation. The value of the traffic index is between 0-10. The higher the value, the current traffic congestion situation The more serious it is, the traffic index forecast can provide data support for decision makers to find problems and formulate strategies. At the same time, it can also help the public plan their travel plans reasonably. [0003] The methods of traffic forecasting at home and abroad can be roughly divided into two categories. The first category is the forecasting method based on a certain mathematical model. For example, Han Chao et al. based on the differe...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q10/04G06Q50/26G06N3/04G06N3/08
CPCG06Q10/04G06Q50/26G06N3/08G06N3/044G06N3/045
Inventor 徐志洁张健钦张悦颖
Owner BEIJING UNIV OF CIVIL ENG & ARCHITECTURE
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