Enhanced public transport management employing AI and anonymous data collection

Abstract

The paper proposes a simple, economic and expandable solution for enhancing the data collection process used in public transport and transport demand management. A non-intrusive and anonymous method is employed to collect an estimative number of passengers in vehicles and public transport stops, along with other, relevant data. Machine learning and specific algorithms are used to improve the data collection process. No specific infrastructure equipment is required.