  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.
