---
title: "Balancing Imbalance: Data-Scarce Urban Flow Prediction via Spatio-Temporal Balanced Transfer Learning"
authors:
  - "Xinyan Hao"
  - "Huaiyu Wan"
  - "Shengnan Guo"
  - "Youfang Lin"
doi: "10.24963/ijcai.2025/319"
url: "https://ijcai.org/proceedings/2025/319"
pdf: "https://ijcai.org/proceedings/2025/0319.pdf"
venue: "Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence"
year: 2025
pages: "2865-2873"
track: "Main Track"
keywords:
  - "Data Mining: DM: Mining spatial and/or temporal data"
---

# Balancing Imbalance: Data-Scarce Urban Flow Prediction via Spatio-Temporal Balanced Transfer Learning

Xinyan Hao, Huaiyu Wan, Shengnan Guo, Youfang Lin

*Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence*, pages 2865-2873. DOI: [10.24963/ijcai.2025/319](https://doi.org/10.24963/ijcai.2025/319)

## Abstract

Advanced deep spatio-temporal networks have become the mainstream for traffic prediction, but the widespread adoption of these models is impeded by the prevalent scarcity of available data. Despite cross-city transfer learning emerging as a common strategy to address this issue, it overlooks the inherent distribution imbalances within each city, which could potentially hinder the generalization capabilities of pre-trained models. To overcome this limitation, we propose a Spatio-Temporal Balanced Transfer Learning (STBaT) framework to enhance existing spatio-temporal prediction networks, ensuring both universality and precision in predictions for new urban environments. A Regional Imbalance Acquisition Module is designed to model the regional imbalances of source cities. Besides, to promote generalizable knowledge acquisition, a Spatio-Temporal Balanced Learning Module is devised to balance the predictive learning process. Extensive experiments on real-world datasets validate the efficacy of our proposed approach compared with several state-of-the-art methods.