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 (OSM) 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.

Quantifying intra-person variability in travel choices is essential for the comprehension of activity–travel behaviour. Due to a lack of empirical studies, there is limited understanding of how an individual's travel pattern evolves over months and years. We use two high-resolution user-labelled datasets consisting of billions of GPS track points from ∼3800 individuals to analyse individuals’ activity–travel behaviour over the long term. The general movement patterns of the considered population are characterised using mobility indicators. Despite the differences in the mobility patterns, we find that individuals from both datasets maintain a conserved quantity in the number of essential travel mode and activity location combinations over time, resulting from a balance between exploring new choice combinations and exploiting existing options. A typical individual maintains ∼15 mode–location combinations, of which ∼7 are travelled with a private vehicle every 5 weeks. The dynamics of this stability reveal that the exploration speed of locations is faster than the one for travel modes, and they can both be well modelled using a power-law fit that slows down over time. Our findings enrich the understanding of the long-term intra-person variability in activity–travel behaviour and open new possibilities for designing mobility simulation models.

Location graphs, compact representations of human mobility without geocoordinates, can be used to personalise location-based services. While they are more privacy-preserving than raw tracking data, it was shown that they still hold a considerable risk for users to be re-identified solely by the graph topology. However, it is unclear how this risk depends on the tracking duration. Here, we consider a scenario where the attacker wants to match the new tracking data of a user to a pool of previously recorded mobility profiles, and we analyse the dependence of the re-identification performance on the tracking duration. We find that the re-identification accuracy varies between 0.41% and 20.97% and is affected by both the pool duration and the test-user tracking duration, it is greater if both have the same duration, and it is not significantly affected by socio-demographics such as age or gender, but can to some extent be explained by different mobility and graph features. Overall, the influence of tracking duration on user privacy has clear implications for data collection and storage strategies. We advise data collectors to limit the tracking duration or to reset user IDs regularly when storing long-term tracking data.