A Data-Driven ‘What-If’ Analysis to Foster Mode Shift from Private to Public Transport Services

Urban expansion and rising mobility demand challenge transport planning, which requires more and more accurate data on mobility patterns to propose effective solutions. This study contributes to tackle this challenge by proposing a ‘What-If’ analysis model to support MaaS (Mobility as a Service) system design by integrating crowdsourced and public transport data. Focusing on the city of Cagliari, it uses the data obtained from the crowdsoucing IoPollicino project to analyze mobility patterns. The study introduces a Distance Metric, combining spatial and temporal factors to compare PT (Public Transport services) with private trips, and an H-index to identify key PT routes for improvement. Findings reveal PT gaps in spatial coverage and timing, showing that many private trips could shift to PT, providing better accessibility and frequency. The model simulates alternative scenarios to optimize PT, laying the foundations for sustainable urban mobility that leverage big data analysis technologies.