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Traffic flow prediction method based on corrosion de-noising deep belief network

A technology of deep belief network and prediction method, which is applied in the field of traffic flow prediction based on corrosion and denoising deep belief network, can solve the problem of over-fitting problem that has not been solved well, so as to improve the generalization ability, relieve traffic pressure, and reduce traffic flow. risk effect

Active Publication Date: 2019-01-01
NANJING UNIV
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Problems solved by technology

However, the problem of overfitting in the network is still not well solved

Method used

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  • Traffic flow prediction method based on corrosion de-noising deep belief network
  • Traffic flow prediction method based on corrosion de-noising deep belief network
  • Traffic flow prediction method based on corrosion de-noising deep belief network

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Embodiment Construction

[0030]The present invention utilizes the method of deep learning to transform the traditional deep belief network to further obtain more representative features, improve the generalization ability of the model, and effectively alleviate the problem of overfitting. The traditional deep belief network is constructed by stacked restricted Boltzmann machines. The innovation of the present invention is that a random corrosion layer is added to the input of each restricted Boltzmann machine during training, and the random corrosion layer The output of the layer is used as the new visible layer, and the hidden layer does not change.

[0031] Corrosion probability is a global hyperparameter. The smaller the corrosion probability, the more neurons are retained. When the corrosion probability is 0, the stochastic corrosion layer degenerates into an ordinary identity mirror layer, and the output simply copies the input; the larger the corrosion probability, the more neurons are lost Act...

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Abstract

Provided is a traffic flow prediction method based on a corrosion de-noising deep belief network. Traffic flow prediction refers to the prediction of the current and future traffic flow according to the traffic flow at historical moments. A random corrosion layer is proposed as a means of regularization to relieve the interdependence of some neurons, improve the generalization ability of the prediction model and reduce the risk of over-fitting. Meanwhile, a traffic flow prediction model is established based on specific application scenarios and the spatial correlation and temporal regularity of traffic flow, to realize accurate, reliable and real-time traffic flow prediction, optimize traffic scheduling, alleviate traffic pressure and improve the operation efficiency of the road network. The method is of great significance to the development of intelligent transportation.

Description

technical field [0001] The invention belongs to the technical field of artificial intelligence, relates to intelligent transportation and neural network learning system technology, and is a traffic flow prediction method based on a corrosion-denoising deep belief network. Background technique [0002] With the development of urbanization, traffic congestion has become a common phenomenon, which seriously affects the convenience of travel. Accurate, reliable, and real-time traffic flow forecasting is an urgent problem to be solved in intelligent transportation. This can effectively optimize traffic scheduling, relieve traffic pressure, improve the operating efficiency of the road network, and improve people's living comfort and enjoyment. In recent years, the development of artificial intelligence has also promoted the research process of traffic flow forecasting, and methods based on deep learning have begun to be applied to the field of intelligent transportation. The mode...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G08G1/01G06Q10/04G06Q50/30G06N3/04G06N3/08
CPCG06N3/08G06Q10/04G08G1/0129G08G1/0145G06N3/045G06Q50/40
Inventor 阮雅端张园笛葛嘉琦王麟皇曹小峰陈启美
Owner NANJING UNIV
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