Spatio-Temporal Analysis of Public Transportation Undercrowding: Leveraging APC Data for a Comprehensive Evaluation of Usage Rates

TL;DR

This paper develops a methodology using APC data and advanced statistical models to analyze public transportation undercrowding, identifying factors and temporal patterns affecting occupancy rates.

Contribution

It introduces a novel approach combining spatio-temporal data and mixed-effects models to evaluate undercrowding in public transit systems.

Findings

Identified segments with higher likelihood of undercrowding.

Analyzed factors influencing undercrowding probability.

Explored temporal distribution of undercrowding throughout journeys.

Abstract

The analysis of the transportation usage rate provides opportunities for evaluating the efficacy of the transportation service offered by proposing an indicator that integrates actual demand and 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) 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 undercrowding. The results of the model are examined at both the segment and ride levels. Initially, the analysis focuses on identifying segments more likely associated with undercrowding, understanding factors influencing the probability of undercrowding, and exploring their relationships. Subsequently, the analysis extends to the temporal distribution of undercrowding, 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.