Metropolitan Segment Traffic Speeds from Massive Floating Car Data in 10 Cities

TL;DR

This paper introduces MeTS-10, a large-scale, high-resolution floating vehicle dataset covering 10 major cities, enabling detailed city-wide traffic analysis beyond traditional sparse detector data.

Contribution

The paper presents a novel, extensive floating vehicle dataset for 10 global cities, with detailed street-level traffic speeds at 15-minute intervals, filling a critical data gap in urban traffic analysis.

Findings

The dataset covers over 1500 km² per city with 15-minute resolution.

Comparison with stationary detectors and Uber data shows differences in coverage and reported speeds.

MeTS-10 enables comprehensive analysis of urban mobility patterns.

Abstract

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.