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A supply and demand forecasting method for online car-hailing based on c-gru

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

Active Publication Date: 2020-12-22
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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  • A supply and demand forecasting method for online car-hailing based on c-gru
  • A supply and demand forecasting method for online car-hailing based on c-gru
  • A supply and demand forecasting method for online car-hailing based on c-gru

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[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

A neural network-based online car-hailing supply and demand forecasting method collects various online car-hailing travel data, uses convolutional neural networks to extract features and reduce dimensions, and then inputs the extracted feature maps into the gated recurrent neural network To predict the supply and demand of online car-hailing, adjust the model during the training process, and finally predict the balance between supply and demand of online car-hailing through the adjusted model. The method of the present invention can carry out more in-depth excavation and analysis on the supply and demand balance data of online car-hailing, so it is more accurate and better in performance when predicting the supply and demand of online car-hailing.

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...

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Application Information

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