Short-time traffic flow prediction method based on spatial-temporal correlation

Abstract

The invention discloses a short-time traffic flow prediction method based on spatial-temporal correlation. The method comprises the steps: selecting a road segment, the traffic flow of which needs tobe predicted, and interruption points in the road segment; selecting the short-time traffic flow historical data of all interruption points in the selected road segment, determining a prediction timeperiod of short-time traffic flow prediction, and verifying whether the historical traffic flow data of the interruption points is periodic or not; dividing a data set into a training data set and a test data set after the normalization of the traffic flow data through a normalization method; performing the prediction analysis of the test data set through an SARIMA model to obtain an initial prediction result; taking the prediction result as an input feature, and substituting the input feature into a random forest model to obtain a final prediction result; comparing the test data set with final prediction data, and analyzing an error. The method enables the flow data to be decomposed into a periodic part with an apparent trend and a random fluctuation part for analysis, and improves the prediction precision of the traffic flow data.

Description

technical field [0001] The invention relates to technical fields such as machine learning methods and traffic flow forecasting, and in particular to a short-term traffic flow forecasting method based on spatio-temporal correlation. Background technique [0002] With the continuous acceleration of the modernization process of today's society and the continuous improvement of the level of urbanization, the number of vehicles has also increased rapidly, and the existing road network conditions are difficult to meet the growing traffic demand. In the early 20th century, the concept of Intelligent Transportation System (ITS) also emerged. In ITS, real-time and accurate short-term traffic flow prediction plays a vital role. It not only affects people's control and induction of traffic flow, but also is the key to the system from passive response to active control. [0003] With the deepening of short-term traffic flow analysis and forecasting, researchers have proposed many model...

AI Technical Summary

Problems solved by technology

However, most of the combined models do not consider the characteristics of traffic flow, but are simply combined randomly, which leads to no significant improvement in the prediction effect of the model, and even increases the complexity of the model

Obviously, it is difficult for a single prediction model to take into account the inherent characteristics of traffic flow data, as well as the external influences caused by seasons, climate or human factors, so it is difficult to reflect the inherent complex characteristics of traffic flow data, and it is impossible to fully consider the impact of external spatial correlation on prediction. research impact and other deficiencies

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