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Short-term traffic flow prediction method based on improved LSTM

A technology of short-term traffic flow and forecasting method, which is applied in the new generation of information field, can solve the problems that the calculation speed and prediction accuracy need to be improved, and achieve the effect of improving the calculation speed

Inactive Publication Date: 2021-04-27
广州市交通规划研究院
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Problems solved by technology

[0033] The calculation speed and prediction accuracy of the traditional LSTM method need to be improved to meet the technical needs of traffic management decision-making, traffic planning, route guidance, etc.

Method used

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  • Short-term traffic flow prediction method based on improved LSTM
  • Short-term traffic flow prediction method based on improved LSTM
  • Short-term traffic flow prediction method based on improved LSTM

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Embodiment Construction

[0065] The present invention will be further described below with reference to the accompanying drawings.

[0066] A short-term traffic flow prediction method based on improved LSTM, which specifically includes the following steps:

[0067] (1) Determine the forget gate:

[0068] Take the input data x at state t t and the output h at the previous state t-1 t-1 As the input of this layer, the sigmoid activation function is used to realize the retention and deletion of the storage unit information of the previous layer; the formula is as follows:

[0069]

[0070] Among them, W f and b f are the corresponding weight matrix and bias, respectively;

[0071] (2) Determine the input gate:

[0072] Also take the input data x at state t t and the output h at the previous state t-1 t-1 As the input of this layer, the sigmoid activation function is applied to decide which information can be stored in the storage unit; the formula is as follows:

[0073]

[0074] Among the...

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Abstract

The invention discloses a short-term traffic flow prediction method based on improved LSTM. The method comprises the following steps: (1) determining a forgotten door; (2) determining an input gate; (3) determining an output gate and a to-be-reserved information vector; (4) updating the cell state c; (5) obtaining a hidden layer output value; and (6) obtaining an output value of the storage unit. An output gate of the LSTM mainly uses a sigmoid activation function to determine which data is used for outputting, tanh function activation is a result of downward translation and contraction of the sigmoid function, the characteristics of the two activation functions are basically the same, and the tanh activation function is researched to replace the activation function of the output gate, so that two steps of determining a vector with reserved information and determining the output gate can be combined, that is to say, the calculation of two parameters can be reduced by replacing zt, and the operation speed is improved.

Description

technical field [0001] The invention relates to a new generation of information technology, in particular to a short-term traffic flow prediction method based on improved LSTM. Background technique [0002] With the development of a new generation of information technology, building a smart city has become the future development direction of many countries and cities. As the main artery of urban development, transportation has become an important part of smart cities. Therefore, the construction of smart cities mainly depends on the development of intelligent transportation. The deployment of 5G technology provides a high-quality network environment for intelligent transportation and plays a key role in the development of intelligent transportation. [0003] The core of intelligent transportation is the integration of technologies such as data processing, data mining, information transmission, and display of transportation big data, and transportation data is the basis for ...

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Application Information

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IPC IPC(8): G06Q10/04G06Q50/30G08G1/01G06N3/04G06N3/08
CPCG06Q10/04G06N3/049G06N3/08G08G1/0125G06N3/048G06Q50/40
Inventor 甘勇华江雪峰胡劲松张薇顾宇忻黄启乐刘佳辉欧阳剑林晓生沈文韬郑贵兵雷玲玲谷裔凡何琪海
Owner 广州市交通规划研究院
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