Recovering Missing Passenger Flow Data in Subway Stations via an Enhanced Generative Adversarial Network

Abstract

To address the challenges posed by incomplete data in passenger flow prediction and organizational tasks, this paper proposes ProbSparse self-attention conditional generative adversarial imputation net (ProbSA-CGAIN), a novel imputation model framework built on the enhanced generative adversarial network (GAN). The model leverages conditional GANs for controlled data generation using external conditional information. It adopts a denoising autoencoder structure for reconstructing and estimating missing passenger flow data. The integration of an efficient ProbSparse self-attention mechanism captures spatiotemporal evolution features, reducing computational complexity. Additionally, the model incorporates auxiliary conditional information to enhance data imputation accuracy by learning interdependencies among multiple data variables. Further, the model integrates local positional encoding and multi-layer global temporal encoding, offering diverse perspectives on spatiotemporal information. Experimental evaluations with real passenger flow data demonstrate the model's superiority over advanced baseline models across various missing patterns and rates. Notably, it exhibits high stability in data restoration, particularly for datasets with higher missing rates, affirming its effectiveness in predicting and inferring missing passenger flow data based on auxiliary data and multi-view positional information, ensuring reliable imputation. The experiments also assess the model's proficiency in attributing different spatiotemporal features, confirming its commendable training and restoration efficiency.