Short-time traffic flow prediction method considering diffusion process

A technology of short-term traffic flow and prediction method, which is applied in the fields of computer and mathematics, and intelligent transportation. It can solve the problem that the dynamic globality of the prediction method is not comprehensive enough, and achieve the effect of eliminating incompleteness, improving accuracy and robustness.

Active Publication Date: 2019-07-23
SHENZHEN GRADUATE SCHOOL TSINGHUA UNIV
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Problems solved by technology

[0003] The main purpose of the present invention is to overcome the deficiencies of the prior art, to propose a short-term traffic flow prediction method considering the diffusion process, by adding features such as road importance,

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  • Short-time traffic flow prediction method considering diffusion process
  • Short-time traffic flow prediction method considering diffusion process
  • Short-time traffic flow prediction method considering diffusion process

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Embodiment Construction

[0028] The present invention will be further described below in conjunction with the accompanying drawings and specific embodiments. Explanation of terms: LSTM (LongShort-TermMemory, long-short-term memory network), CNN (ConvolutionalNeuralNetworks, convolutional neural network).

[0029] refer to figure 1 , the specific embodiment of the present invention provides a short-term traffic flow prediction method considering the diffusion process, including: obtaining the historical traffic flow sequence O={x of the current road section 1 ,x 2 ,...,x m}, and take 5 to 30 minutes as a sampling interval, carry out the smoothing operation according to the mean value, and obtain the smoothed traffic flow sequence F={X 1 ,X 2 ,...,X t-1}; where X n Represents the traffic flow of the current road segment at time n after smoothing the original historical traffic flow sequence O, n=1,2,...,t-1; the LSTM-CNN model is used to capture the current traffic flow sequence F from the traffic...

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Abstract

The invention discloses a short-time traffic flow prediction method considering a diffusion process, the method comprises the following steps: obtaining a historical traffic flow sequence of the current road section and performing a smoothing operation to obtain a smoothed traffic flow sequence F = {X1, X2 - X<t-1>}; adopting an LSTM-CNN model to capture a depth spatial-temporal feature from the smoothed traffic flow sequence; performing a diffusion process of digital description through a class PageRank algorithm to obtain a road importance feature of the current road section from the smoothed traffic flow sequence, wherein the road importance feature and road auxiliary information are combined to form a one-dimensional vector serving as a breadth feature; and fusing the depth spatial-temporal feature and the breadth feature to obtain a traffic flow prediction value Xt of the current road section at the t moment.

Description

technical field [0001] The invention relates to a short-term traffic flow prediction method considering the diffusion process, which belongs to the cross field of intelligent traffic, computer and mathematics. Background technique [0002] With the rapid development of social economy and the acceleration of urbanization, effective traffic control and guidance has become an urgent problem to be solved, and real-time and accurate traffic flow forecasting is a crucial step. However, the current research does not fully consider the dynamic changes of traffic flow on the whole road network, resulting in the prediction accuracy not meeting the requirements. Contents of the invention [0003] The main purpose of the present invention is to overcome the deficiencies of the prior art, to propose a short-term traffic flow prediction method considering the diffusion process, by adding features such as road importance, to carry out accurate short-term traffic flow prediction, to solve...

Claims

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Application Information

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IPC IPC(8): G08G1/01G08G1/065
CPCG08G1/0129G08G1/0133G08G1/065
Inventor 张凯赵雪芳董宇涵
Owner SHENZHEN GRADUATE SCHOOL TSINGHUA UNIV
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