SHORT-TERM TRAFFIC FLOW PREDICTION METHOD BASED ON SPATIO-TEMPORAL CORRELATION

摘要

A short-term traffic flow prediction method based on spatio-temporal correlation. The method comprises the following steps: selecting a road section requiring traffic prediction and break points in the road section, acquiring short-term traffic flow historical data of all of the break points in the selected road section, determining a prediction time period of the short term traffic flow prediction, and verifying whether the historical traffic flow data of the prediction break points has periodicity; after using a normalisation method to normalise the traffic flow data, dividing the data set into a training data set and a testing data set; using a SARIMA model to perform predictive analysis on the testing data set to obtain an initial prediction resu using the prediction result as an input feature, entering same into a random forest model to obtain a final prediction resu comparing the testing data with the final prediction data and analysing errors. The present method breaks down flow data into periodic parts with evident trends and random fluctuation parts for analysis, increasing the precision of traffic flow data prediction.