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.
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[0023] The embodiments of the present invention will be described in detail below with reference to the accompanying drawings and examples.
[0024] The present invention is a C-GRU-based online car-hailing supply and demand forecasting method, such as figure 1 shown, the steps are as follows:
[0025] 1. Preprocess the travel data of online car-hailing to obtain the characteristics that affect the forecast of supply and demand;
[0026] 2. Then use the convolutional neural network (CNN) to train the data to extract features and achieve dimensionality reduction to obtain a low-dimensional feature map;
[0027] 3. Input the low-dimensional feature map into the threshold cycle (GRU) neural network model to predict the supply and demand of online car-hailing.
[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 each day's...
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