Bus station passenger flow volume prediction method based on GCN-LSTM combination model

Abstract

The invention discloses a bus station passenger flow volume prediction method based on a GCN-LSTM combination model, and belongs to the field of traffic prediction. According to the method, spatial correlation of passenger flow volume data is extracted by using a graph convolutional neural network, the data with spatial correlation information is input into a long-term and short-term memory cycleunit for time feature extraction, and finally, a prediction result is obtained through a full connection layer. According to the method, the spatiotemporal features of the traffic data are simultaneously concerned, so that the defect that the spatiotemporal features of the traffic data cannot be fully extracted by the existing method is overcome, the passenger flow volume data prediction precisionis improved, and the problem of short-time passenger flow volume prediction can be better solved.

Description

technical field [0001] The invention belongs to the technical field of traffic forecasting, in particular to a neural network-based forecasting method for short-term bus station passenger flow. Background technique [0002] With the continuous advancement of the urbanization process, the number of motor vehicles has increased rapidly, and the ensuing traffic congestion, frequent traffic accidents, and environmental pollution have become increasingly serious. The public transportation system is an important part of urban transportation, and public transportation has more prominent advantages compared with other transportation methods in terms of land saving, consumption reduction, emission reduction, and promotion of fairness and harmony. The good development of public transportation can alleviate urban traffic congestion. , reducing air pollution, improving people's travel patterns, and optimizing urban traffic structures are of great significance. Whether the public transp...

AI Technical Summary

Problems solved by technology

Due to the high degree of uncertainty in the traffic characteristics, only considering the time series relationship cannot give a good understanding of the law of traffic data changes.

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