Abstract
Long-term urban mobility predictions play a crucial role in the effective management of urban facilities and services. Conventionally, urban mobility data has been structured as spatiotemporal videos, treating longitude and latitude grids as fundamental pixels. Consequently, video prediction methods, relying on Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs), have been instrumental in this domain. In our research, we introduce a fresh perspective on urban mobility prediction. Instead of oversimplifying urban mobility data as traditional video data, we regard it as a complex multivariate time series. This perspective involves treating the time-varying values of each grid in each channel as individual time series, necessitating a thorough examination of temporal dynamics, cross-variable correlations, and frequency-domain insights for precise and reliable predictions. To address this challenge, we present the Super-Multivariate Urban Mobility Transformer (SUMformer), which utilizes a specially designed attention mechanism to calculate temporal and cross-variable correlations and reduce computational costs stemming from a large number of time series. SUMformer also employs low-frequency filters to extract essential information for long-term predictions. Furthermore, SUMformer is structured with a temporal patch merge mechanism, forming a hierarchical framework that enables the capture of multi-scale correlations. Consequently, it excels in urban mobility pattern modeling and long-term prediction, outperforming current state-of-the-art methods across three real-world datasets.
Abstract (translated)
长期城市流动性预测在有效管理城市设施和服务方面发挥着关键作用。通常，城市流动性数据被结构化为栅格状的时空视频，将经度纬度网格视为基本像素。因此，依赖于卷积神经网络（CNNs）和视觉变换器（ViTs）的视频预测方法在领域内发挥了重要作用。在我们的研究中，我们提出了一个关于城市流动性预测的新视角。我们没有将城市流动性数据简单地视为传统视频数据，而是将其视为一个复杂的多变量时间序列。这个观点包括将每个通道中每个网格的时间变化值视为独立的时间序列，这就需要对时间动态、跨变量相关性和频域见解进行深入的审查，以确保精确和可靠的预测。为了解决这个挑战，我们提出了超级多维城市流动性变换器（SUMformer），它利用专门设计的注意力机制计算时空和相关性，并降低由于大量时间序列而产生的计算成本。SUMformer还采用低频滤波器来提取长期预测所需的关键信息。此外，SUMformer采用时间补丁合并机制，形成了一个层次结构，可以捕捉多尺度相关性。因此，它在城市流动性模式建模和长期预测方面表现出色，超越了当前最先进的方法，在三个真实世界数据集上取得了较好的成绩。
URL
https://arxiv.org/abs/2312.01699
