Estimating traffic flows from vehicle trajectories based on sparse mobile phone geolocation data

Poster (konferens), 2025

Large-scale empirical traffic flow data are useful in spatial planning, transport research, and data-driven decision making, but existing data, collected using traffic sensors or counts, lack spatio-temporal coverage and granularity. Mobile phone geolocation data are promising for capturing traffic flows at scale given their size, granularity, and coverage, but their potential for such cases remains unexplored. We investigate how traffic flows with high spatial and temporal coverage and granularity can be estimated from vehicle trajectories based on sparse mobile phone geolocation data.