Subway pedestrian flow prediction method and system based on space-time parallel grid neural network

Abstract

The invention provides a subway pedestrian flow prediction method and system based on a space-time parallel grid neural network, and the method comprises the steps: providing a grid neural network to learn the time relation of subway pedestrian flow, and capturing the short-term time correlation of subway pedestrian flow; further capturing the long-term time correlation of the subway pedestrian flow; measuring spatial correlation between subway stations through an index based on transfer flow, and modeling a subway system into a weighted directed graph based on the index; based on construction of a subway weighted directed graph, combining a propagation graph neural network with a grid neural network, and learning dynamic spatial correlation of metro pedestrian flow; and executing the learning processes of the long and short term correlation and the dynamic spatial correlation in parallel, and fusing the results of the two to obtain a final subway pedestrian flow prediction result. According to the method, a space-time parallel learning framework is adopted, the long-term and short-term time correlation and the dynamic space correlation of the subway pedestrian flow can be effectively learned, and the learned knowledge is applied to prediction.

Description

technical field [0001] The invention relates to the fields of spatio-temporal data mining and urban computing, in particular to a method and system for predicting subway passenger flow based on spatio-temporal parallel grid neural network. Background technique [0002] At present, China's subway system construction is still in a stage of vigorous development. Shanghai plans to build 9 new subway lines between 2018 and 2023; Guangzhou started construction of 6 subway lines in 2018, with a total length of 110 kilometers and 73 stations. The accurate prediction of the flow of people in a subway station can play a key role in assisting in planning the scale of station construction, assisting in scheduling subway shifts, and rationally arranging subway staff. It is of great practical significance to meet the daily travel needs of Chinese people. [0003] So far, there have been many works on the prediction of traffic conditions such as vehicle speed, traffic flow, and people flo...

AI Technical Summary

Problems solved by technology

However, geographical characteristics often cannot properly describe the transfer flow between subway stations. The transfer flow between two subway stations is often not determined by the distance between them. Inability to express spatial correlation between subway stations

In addition, the dynamic characteristics of the spatial correlation between subway stations make it difficult to represent it with static spatial characteristics

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