Short-time traffic flow prediction method based on space-time correlation and convolutional neural network

A convolutional neural network and short-term traffic flow technology, which is applied in the field of short-term traffic flow prediction, can solve the problem that it is difficult to make full use of the time and space correlation of traffic flow data, and achieve the effect of accurately predicting changes and improving prediction accuracy

Inactive Publication Date: 2019-05-14
ENJOYOR CO LTD +1
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Problems solved by technology

[0004] In order to overcome the deficiencies in the prior art, the present invention provides a short-term traffic flow prediction method based on spatio-temporal correlation and conv

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  • Short-time traffic flow prediction method based on space-time correlation and convolutional neural network
  • Short-time traffic flow prediction method based on space-time correlation and convolutional neural network
  • Short-time traffic flow prediction method based on space-time correlation and convolutional neural network

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[0030] The present invention will be further described below in conjunction with specific examples, but the present invention is not limited to these specific implementations. Those skilled in the art will realize that the present invention covers all alternatives, modifications and equivalents as may be included within the scope of the claims.

[0031] see figure 1 , the present embodiment provides a short-term traffic flow prediction method based on spatio-temporal correlation and convolutional neural network, the steps are as follows:

[0032] (1) Select the road section that needs traffic flow prediction and the vehicle detection points in the road section, and obtain the short-term traffic flow historical data of the selected road section and its upstream and downstream vehicle detection points;

[0033] In this implementation example, the traffic flow data is collected by coils, and the traffic flow data obtained is the number of vehicles passing by a specific vehicle d...

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Abstract

The invention discloses a short-time traffic flow prediction method based on space-time correlation and a convolutional neural network. The method comprises: mining space-time correlation in the traffic flow data; traffic flow data is converted into a two-dimensional matrix with space-time traffic flow information, the space-time characteristic information of traffic flow in the matrix is extracted, processed and learned by utilizing the convolutional neural network, a prediction result is finally obtained, and in addition, optimal input data is screened out by adopting a space-time characteristic selection algorithm. The randomness and uncertainty of the traffic flow data are deeply mined, and the space-time correlation in the traffic flow data is fully considered, so that the predictionprecision of the traffic flow data is effectively improved.

Description

technical field [0001] The invention belongs to the field of traffic control and relates to a short-term traffic flow prediction method based on spatio-temporal correlation and a convolutional neural network. Background technique [0002] The changing trend of future traffic has always been a concern of traffic management departments and travelers. Reliable traffic forecast information can provide a reference for traffic management departments to formulate management plans and rationally allocate traffic resources; at the same time, travelers can plan their trips reasonably based on traffic forecast information. Accurate and timely traffic flow data can be used to help alleviate road congestion, reduce vehicle emissions, and improve road efficiency. With the deployment of intelligent transportation systems, the research on short-term traffic flow prediction models is of great importance. The acquisition of real-time and accurate short-term traffic flow prediction data is ch...

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Application Information

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IPC IPC(8): G06Q10/04G06Q50/30G06N3/04
Inventor 张伟斌余英豪郭海锋戚湧
Owner ENJOYOR CO LTD
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