Abstract
Mobility surveys normally present a high level of detail, but their sample size tends to be bounded due to high sampling cost. Meanwhile, anonymised mobile network data present a significant sample size, but may lack detailed travellers’ trip information (e.g., trip purpose, mode choice, etc.). This paper uses airport passenger surveys to develop a set of machine learning models able to estimate the passengers travel purpose and access modal choice in order to reconstruct the door-to-door trip information obtained from mobile network data in future studies. The proposed approach is demonstrated and evaluated for Madrid-Barajas and Palma de Mallorca airports.
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