Traffic forecasting is crucial for smart cities and intelligent transportation initiatives, where deep learning has made significant progress in modeling complex spatio-temporal patterns in recent years. However, current public datasets have limitations in reflecting the ultra-dynamic nature of real-world scenarios, characterized by continuously evolving infrastructures, varying temporal distributions, and temporal gaps due to sensor downtimes or changes in traffic patterns. These limitations inevitably restrict the practical applicability of existing traffic forecasting datasets. To bridge this gap, we present XXLTraffic, the largest available public traffic dataset with the longest timespan and increasing number of sensor nodes over the multiple years observed in the data, curated to support research in ultra-dynamic forecasting. Our benchmark includes both typical time-series forecasting settings with hourly and daily aggregated data and novel configurations that introduce gaps and down-sample the training size to better simulate practical constraints. We anticipate the new XXLTraffic will provide a fresh perspective for the time-series and traffic forecasting communities. It would also offer a robust platform for developing and evaluating models designed to tackle ultra-dynamic and extremely long forecasting problems. Our dataset supplements existing spatio-temporal data resources and leads to new research directions in this domain.

翻译：交通预测对于智慧城市和智能交通建设至关重要，近年来深度学习在建模复杂时空模式方面取得了显著进展。然而，现有公开数据集在反映现实场景的超动态特性方面存在局限，这些特性包括持续演进的基础设施、变化的时序分布，以及由传感器停机或交通模式改变导致的时序间隙。这些局限不可避免地制约了现有交通预测数据集的实际适用性。为弥补这一缺口，我们提出了XXLTraffic——当前可用的最大规模公开交通数据集，其具备最长时间跨度，且在数据观测的多年期间传感器节点数量持续增长，旨在支持超动态预测研究。我们的基准测试既包含基于小时与日聚合数据的典型时序预测设定，也引入了包含数据间隙及对训练规模进行降采样的新颖配置，以更好地模拟实际约束条件。我们预期XXLTraffic将为时序与交通预测领域提供全新视角，并为开发和评估针对超动态及超长预测问题的模型提供稳健平台。本数据集将补充现有时空数据资源，并引领该领域新的研究方向。