Online car-hailing service demand prediction method based on deep neural network

Abstract

The invention discloses an online car-hailing service demand prediction method based on a deep neural network. The method comprises the following steps: S1, dividing a region of a city road network, and calculating and counting the network car-hailing demand of each region; S2, designing a prediction model of the demand of the network car-hailing: based on the deep neural network, learning and training the spatio-temporal characteristics of the demand of the network car-hailing, combining the influence factors of the weather factors on the willingness of the network car-hailing users, and predicting the regional demand of the next period of time; S3, according to the historical data, training the prediction model of regional car call demand, and then predicting the demand of network car call in each region by using the trained model. By combining external factors and spatio-temporal correlation, this method uses depth neural network to express the complex non-linear spatio-temporal correlation characteristics of the demand for network car-hailing, and can achieve high prediction accuracy.

Description

technical field [0001] The present invention relates to the cross-technical application field of deep learning and car-hailing demand forecasting, in particular to a method for forecasting online car-hailing demand based on a deep neural network. Background technique [0002] With the rapid development of location-based services and mobile Internet technologies, online car-hailing is gradually becoming an important alternative to urban travel. However, there is a mismatch between the demand and supply capacity of online car-hailing in the space-time dimension. For example, there is a phenomenon of "difficulty in getting a taxi" in the city center during rush hour, while there is excess capacity in the suburbs of the city. In addition, external factors such as weather will also affect people's willingness to travel. For example, the demand for car-hailing on rainy days will increase significantly. Therefore, how to use massive cross-domain data to predict car-hailing demand...

AI Technical Summary

Problems solved by technology

The disadvantage of these techniques is that it is difficult to express the complex nonlinear spatio-temporal correlation between car-hailing demand and external factors

With the excellent performance of the deep neural network in learning the complex feature correlation of massive data, people are encouraged to use deep learning methods for demand forecasting, but the current research methods consider relatively single factors, such as: only using convolutional neural networks (CNN ) considers the space factor, and only uses the long short-term memory network (LSTM) to consider the time factor, so the prediction accuracy needs to be improved

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