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Online car-hailing supply and demand prediction method based on C-GRU

A forecasting method and car-hailing technology, applied in forecasting, biological neural network models, instruments, etc., can solve problems such as inability to obtain forecasts, achieve good development and application prospects, good accuracy, and improve efficiency.

Active Publication Date: 2019-11-15
INNER MONGOLIA UNIV OF TECH
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Problems solved by technology

[0004] Traditional online car-hailing supply and demand forecasting methods include random forest, support vector machine, BP neural network, recurrent neural network, etc. Most of these supply and demand forecasting methods only extract some features, while ignoring other features that affect the forecast, such as weather features, Temperature characteristics, traffic congestion characteristics, etc., so that more accurate predictions cannot be obtained

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  • Online car-hailing supply and demand prediction method based on C-GRU
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  • Online car-hailing supply and demand prediction method based on C-GRU

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Embodiment Construction

[0023] The implementation of the present invention will be described in detail below in conjunction with the drawings and examples.

[0024] A C-GRU-based network car supply and demand prediction method of the present invention, such as figure 1 As shown, the steps are as follows:

[0025] 1. Preprocess the online car-hailing travel data to obtain the characteristics that affect the supply and demand forecast;

[0026] 2. Then use the convolutional neural network (CNN) to train the data to extract features and achieve dimensionality reduction to obtain low-dimensional feature maps;

[0027] 3. Input the low-dimensional feature map into the gate cycle (GRU) neural network model to predict the supply and demand of online car-hailing vehicles.

[0028] Specifically, in step 1, the preprocessing method for the online car-hailing travel data is as follows:

[0029] Divide a city into n non-overlapping square areas D={d 1 , d 2 ,...,d i ,...,d n}, divide the 24 hours of each ...

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Abstract

The invention discloses an online car-hailing supply and demand prediction method of a neural network. The online car-hailing supply and demand prediction method includes the steps: collecting variousonline car-hailing travel data, utilizing the convolutional neural network to extract features and reduce dimensions, inputting the extracted feature map into the gated recurrent neural network to perform online car-hailing supply and demand prediction, adjusting the model in the training process, and finally, predicting the online car-hailing supply and demand difference through the adjusted model. According to the online car-hailing supply and demand prediction method, the online car-hailing supply and demand difference data can be deeply mined and analyzed, so that the online car-hailing supply and demand prediction is more accurate, and the performance is better.

Description

technical field [0001] The invention belongs to the technical field of online car-hailing supply and demand forecasting, and particularly relates to a method for forecasting online car-hailing supply and demand based on C-GRU (Convolutional Gated Recurrent Unit, C-GRU for short). Background technique [0002] In recent years, with the rapid development of the online car-hailing platform, a large amount of data will be generated and the demand for data processing will increase day by day. Faced with such a huge amount of data, by choosing an appropriate deep learning model, the operation of online car-hailing will be more efficient. , to ease the pressure of traffic travel in urban areas, and provide convenience and speed to customers. In order to make online car-hailing travel more efficient, the supply and demand forecast of online car-hailing cars is a problem to be solved. [0003] For the supply and demand forecasting problem of online car-hailing, the forecasting algor...

Claims

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Application Information

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IPC IPC(8): G06Q10/04G06N3/04G06Q50/30
CPCG06Q10/04G06N3/044G06N3/045G06Q50/40
Inventor 田永红吴琪张悦张晴晴张鹏
Owner INNER MONGOLIA UNIV OF TECH
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