Spatio-temporal analysis of public transportation ridership: leveraging APC data for a comprehensive evaluation of usage rates

The emphasis on the efficient utilization of public transportation resources has become particularly relevant in recent years due to the post-pandemic fluctuation in public transportation usage and the rise in operational costs. The analysis of transportation usage rates provides valuable insights into the efficiency of the service, offering an indicator that integrates actual demand with the capacity. This study aims to develop a methodology for analyzing the occupancy rate from large-scale datasets to identify gaps between supply and demand in public transportation. Leveraging the spatio-temporal granularity of data from Automatic People Counting (APC) systems and relying on the Generalized Linear Mixed Effects Model and the Generalized Mixed-Effect Random Forest, in this study we propose a methodology for analyzing factors determining low occupancy rates. The model’s results are examined at both the segment and ride levels. Initially, the analysis focuses on identifying segments more likely associated with low occupancy rates, understanding factors influencing the probability of having low occupancy rates, and exploring their relationships. Subsequently, the analysis extends to the temporal distribution of low-occupancy-rate situations, encompassing its impact on the entire journey. The proposed methodology is applied to analyze APC data, provided by the company responsible for public transport management in Milan, on a radial route of the surface transportation network.