CSP-AIT-Net: A contrastive learning-enhanced spatiotemporal graph attention framework for short-term metro OD flow prediction with asynchronous inflow tracking

TL;DR

This paper introduces CSP-AIT-Net, a novel framework that improves short-term metro OD flow prediction by integrating asynchronous inflow tracking, advanced station semantics, and contrastive learning, leading to more accurate and efficient metro system management.

Contribution

The paper presents a new spatiotemporal graph attention model that incorporates asynchronous inflow tracking and contrastive learning for enriched station semantics, advancing OD flow prediction accuracy.

Findings

Improved prediction accuracy over state-of-the-art methods.

Effective modeling of asynchronous passenger flows.

Enhanced station semantics representation through contrastive learning.

Abstract

Accurate origin-destination (OD) passenger flow prediction is crucial for enhancing metro system efficiency, optimizing scheduling, and improving passenger experiences. However, current models often fail to effectively capture the asynchronous departure characteristics of OD flows and underutilize the inflow and outflow data, which limits their prediction accuracy. To address these issues, we propose CSP-AIT-Net, a novel spatiotemporal graph attention framework designed to enhance OD flow prediction by incorporating asynchronous inflow tracking and advanced station semantics representation. Our framework restructures the OD flow prediction paradigm by first predicting outflows and then decomposing OD flows using a spatiotemporal graph attention mechanism. To enhance computational efficiency, we introduce a masking mechanism and propose asynchronous passenger flow graphs that integrate inflow and OD flow with conservation constraints. Furthermore, we employ contrastive learning to extract high-dimensional land use semantics of metro stations, enriching the contextual understanding of passenger mobility patterns. Validation of the Shanghai metro system demonstrates improvement in short-term OD flow prediction accuracy over state-of-the-art methods. This work contributes to enhancing metro operational efficiency, scheduling precision, and overall system safety.