Mobile location-based service (LBS) data can give airport planners a large-scale view of how travelers move through space and time. But the data often has gaps: airport sightings may be missing before or after a trip, making it hard to infer which journeys are air trips.
The paper introduces a two-stage hybrid framework designed to infer air trips even when such sightings are incomplete. By combining multiple methods in sequence, the framework aims to reconstruct the missing links between mobile device observations and actual flights. The abstract does not detail the inner workings of either stage, but the overall goal is to recover air travel patterns from imperfect location data.
If effective, this approach could help planners rely on passively collected mobile data rather than more intrusive or costly surveys. It also highlights the potential of LBS data for transportation research, despite its inherent gaps.