The present invention provides a street space
vitality prediction method based on
deep learning, which relates to the technical field of regional
pedestrian flow prediction, and aims to effectively combine fitting and
deep learning to achieve complementary shortcomings, including dividing a year into N periodic submodules, establishing a fitting model for estimating the
pedestrian flow of the street for each periodic submodule, constructing an objective function that minimizes the fitting error to obtain the parameters of the fitting model, and obtaining the first
pedestrian flow
estimation result of the target day through the fitting model; collecting a variety of real-time
impact data and combining them with the first
pedestrian flow estimation result, and then outputting the second
pedestrian flow estimation result through the trained
error correction model for prediction and correction, and the real-time
impact data includes natural change
impact data and artificial change impact data. The present invention has the advantages of strong adaptability, low computing
power consumption and high precision in spatial
vitality prediction.