Traffic analysis is crucial for urban operations and planning, while the availability of dense urban traffic data beyond loop detectors is still scarce. We present a large-scale floating vehicle dataset of per-street segment traffic information, Metropolitan Segment Traffic Speeds from Massive Floating Car Data in 10 Cities (MeTS-10), available for 10 global cities with a 15-minute resolution for collection periods ranging between 108 and 361 days in 2019-2021 and covering more than 1500 square kilometers per metropolitan area. MeTS-10 features traffic speed information at all street levels from main arterials to local streets for Antwerp, Bangkok, Barcelona, Berlin, Chicago, Istanbul, London, Madrid, Melbourne and Moscow. The dataset leverages the industrial-scale floating vehicle Traffic4cast data with speeds and vehicle counts provided in a privacy-preserving spatio-temporal aggregation. We detail the efficient matching approach mapping the data to the OpenStreetMap road graph. We evaluate the dataset by comparing it with publicly available stationary vehicle detector data (for Berlin, London, and Madrid) and the Uber traffic speed dataset (for Barcelona, Berlin, and London). The comparison highlights the differences across datasets in spatio-temporal coverage and variations in the reported traffic caused by the binning method. MeTS-10 enables novel, city-wide analysis of mobility and traffic patterns for ten major world cities, overcoming current limitations of spatially sparse vehicle detector data. The large spatial and temporal coverage offers an opportunity for joining the MeTS-10 with other datasets, such as traffic surveys in traffic planning studies or vehicle detector data in traffic control settings.

翻译：交通分析对于城市运营与规划至关重要，然而除环形线圈检测器外，高密度城市交通数据仍十分稀缺。我们提出一个大规模浮动车辆数据集——基于10个城市海量浮动车数据的大都市路段交通速度数据集（MeTS-10），该数据集覆盖全球10个城市，时间分辨率为15分钟，采集周期为2019-2021年间的108至361天，每个大都市区覆盖面积超过1500平方公里。MeTS-10包含安特卫普、曼谷、巴塞罗那、柏林、芝加哥、伊斯坦布尔、伦敦、马德里、墨尔本和莫斯科所有街道等级（从主干道到地方道路）的交通速度信息。该数据集利用工业级浮动车数据Traffic4cast，以保护隐私的时空聚合形式提供速度和车辆计数。我们详细介绍了将数据映射到OpenStreetMap路网图的高效匹配方法。通过与公开的固定车辆检测器数据（柏林、伦敦、马德里）以及优步交通速度数据集（巴塞罗那、柏林、伦敦）进行比较，我们评估了该数据集。比较结果凸显了各数据集在时空覆盖范围以及由分箱方法导致的交通报告差异。MeTS-10使对全球十大主要城市的出行和交通模式进行新颖、全城范围的分析成为可能，克服了当前空间稀疏的车辆检测器数据的局限性。其大范围时空覆盖为MeTS-10与其他数据集（如交通规划研究中的交通调查数据或交通控制场景中的车辆检测器数据）的联合使用提供了机遇。