Abstract
Accurate population flow prediction is essential for urban planning, transportation management, and public health. Yet existing methods face key limitations: traditional models rely on static spatial assumptions, deep learning models struggle with cross-city generalization, and Large Language Models (LLMs) incur high computational costs while failing to capture spatial structure. Moreover, many approaches sacrifice resolution by clustering Points of Interest (POIs) or restricting coverage to subregions, limiting their utility for city-wide analytics. We introduce UrbanPulse, a scalable deep learning framework that delivers ultra-fine-grained, city-wide OD flow predictions by treating each POI as an individual node. It combines a temporal graph convolutional encoder with a transformer-based decoder to model multi-scale spatiotemporal dependencies. To ensure robust generalization across urban contexts, UrbanPulse employs a three-stage transfer learning strategy: pretraining on large-scale urban graphs, cold-start adaptation, and reinforcement learning this http URL on over 103 million cleaned GPS records from three metropolitan areas in California, UrbanPulse achieves state-of-the-art accuracy and scalability. Through efficient transfer learning, UrbanPulse takes a key step toward making high-resolution, AI-powered urban forecasting deployable in practice across diverse cities.
Abstract (translated)
准确的人口流动预测对于城市规划、交通管理和公共卫生至关重要。然而，现有的方法面临一些关键限制：传统模型依赖于静态空间假设，深度学习模型在跨城市泛化方面遇到困难，而大型语言模型（LLMs）则因计算成本高昂且无法捕捉空间结构而受限。此外，许多方法通过聚类兴趣点（POIs）或仅限于次区域来牺牲分辨率，这限制了它们在整个城市的分析中的实用性。 我们引入了一种可扩展的深度学习框架UrbanPulse，它能够进行超精细的城市范围内的OD流量预测，并将每个POI视为一个独立节点。该框架结合了一个时间图卷积编码器和基于变压器的解码器来建模多尺度时空依赖性。为了确保在不同城市环境中的鲁棒泛化能力，UrbanPulse采用了一种三阶段的迁移学习策略：大规模城市图预训练、冷启动适应以及通过强化学习进行微调。 基于加州三个都会区超过1.03亿条经过清洗的GPS记录，UrbanPulse实现了最先进的准确性和可扩展性。通过高效的迁移学习，UrbanPulse为将高分辨率的人工智能驱动的城市预测在各种城市中实际部署迈出了关键一步。
URL
https://arxiv.org/abs/2507.17924
