Data-driven approaches have emerged as a popular tool for addressing challenges in urban computing. However, current research efforts have primarily focused on limited data sources, which fail to capture the complexity of urban data arising from multiple entities and their interconnections. Therefore, a comprehensive and multifaceted dataset is required to enable more extensive studies in urban computing. In this paper, we present CityNet, a multi-modal urban dataset that incorporates various data, including taxi trajectory, traffic speed, point of interest (POI), road network, wind, rain, temperature, and more, from seven cities. We categorize this comprehensive data into three streams: mobility data, geographical data, and meteorological data. We begin by detailing the generation process and basic properties of CityNet. Additionally, we conduct extensive data mining and machine learning experiments, including spatio-temporal predictions, transfer learning, and reinforcement learning, to facilitate the use of CityNet. Our experimental results provide benchmarks for various tasks and methods, and also reveal internal correlations among cities and tasks within CityNet that can be leveraged to improve spatiotemporal forecasting performance. Based on our benchmarking results and the correlations uncovered, we believe that CityNet can significantly contribute to the field of urban computing by enabling research on advanced topics.

翻译：数据驱动方法已成为解决城市计算挑战的主流工具。然而，当前研究主要聚焦于有限数据源，未能充分捕捉由多实体及其互联关系构成的城市数据复杂性。因此，亟需综合性、多层面的数据集来支撑更广泛的城市计算研究。本文提出CityNet——一个涵盖七座城市的多模态城市数据集，包含出租车轨迹、交通速度、兴趣点（POI）、路网、风速、降雨、温度等多种数据。我们将这些综合数据归纳为三大流：移动数据、地理数据与气象数据。首先详述CityNet的生成流程与基本属性，继而开展包括时空预测、迁移学习与强化学习在内的大规模数据挖掘与机器学习实验，以促进CityNet的应用。实验结果为各类任务与方法提供了基准，同时揭示了CityNet内部城市间与任务间的内在关联，这些关联可用于提升时空预测性能。基于基准测试结果与发现的关联，我们认为CityNet将通过支持前沿课题研究，为城市计算领域做出重要贡献。