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:对城市业务和规划而言,交通分析至关重要,而环形探测器以外的密集城市交通数据仍然缺乏。我们展示了10个城市(METS-10)大规模移动汽车数据(METS-10)的《10个城市大规模漂浮汽车数据》中的大都市路段交通速度,收集时间为2019-2021年108天至361天,覆盖每个大都会地区1 500多平方公里。MTS-10显示安特、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从主要通道到当地街道的交通速度信息。数据集利用工业规模的漂浮车辆流量数据,以保存隐私的保时速汇总方式提供速度和车辆清点数据。我们详细介绍了为OpenStreetMap路图绘制数据的有效匹配方法。我们通过将数据集与公开提供的固定车辆检测数据(柏林、伦敦和马德里)以及Uber交通速度数据集(巴塞罗那、柏林、伦敦、伦敦、伦敦、墨尔本、墨尔本和莫斯科)的交通流量数据。比较显示工业规模的浮动交通流量数据,使主要城市的交通和快速交通流量变化数据得以进行数据分析。比较显示全球范围,数据显示10号范围范围。