Road network traffic flow space-time prediction method for intelligent traffic and intelligent driving

Abstract

The invention discloses a road network traffic flow space-time prediction method for intelligent traffic and intelligent driving. The method comprises the steps of obtaining, encoding, decoding and predicting, and therefore the traffic flow of a highway network within the predicted period of time is obtained according to predicted picture signals. On one hand, due to the fact that a reverse toothshaped diffusion convolution circulation module is constructed in the encoding step and the decoding step separately, a state variable is reserved in a convolution layer, the maintained state variablenumber is decreased, in the convolution diffusion process from reverse tooth shaped circulation to the optimal state variable, two-time state updating operation can be performed at the same moment inthe reconstructed diffusion convolution circulation processing process, and the time short-term dependency can be enhanced; on the other hand, due to the fact that in the encoding and decoding steps,day periodogram signals and week periodogram signals are adopted as considering factors for decoding processing, basis information is not single, noise possibly introduced in the accumulative learning process can be effectively restrained, and therefore the prediction accuracy of predicted picture signs is improved.

Description

technical field [0001] The invention relates to the fields of intelligent transportation, intelligent driving and artificial intelligence, in particular to a spatio-temporal prediction method of road network traffic flow oriented to intelligent transportation and intelligent driving. Background technique [0002] With the sharp increase in car ownership, the limited road network capacity can no longer meet the blowout traffic demand, and traffic congestion has become a common phenomenon in modern transportation. Traffic congestion not only wastes people's precious daily time, but also causes a series of chain problems, such as frequent traffic accidents, deterioration of social environment, waste of energy resources and so on. To this end, on the one hand, it is necessary to develop an intelligent transportation system (Intelligent Transportation System, ITS), to scientifically dispatch traffic from a global perspective, to reasonably disperse traffic pressure, and to maximi...

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Problems solved by technology

Among them, the expressway network is relatively small in scale compared with the urban dense road network, and it is easy to obtain public data resources. Therefore, the research on the short-term traffic flow and multi-time prediction of the expressway network has been relatively mature. Some scholars have proposed the road network The spatio-temporal prediction model can already capture the dynamic characteristics of time and space, but there are still some problems. This kind of spatio-temporal prediction model only considers the spatial local correlation and the short-term dependence of time, and does not consider the long-term dependence of time and periodic characteristics, making the highway network There are some inaccurate situations in the traffic flow prediction results, and the prediction performance needs to be further improved

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