Predicting passenger satisfaction in public transportation using machine learning models

Enhancing the understanding of passenger satisfaction in public transportation is crucial for operators to refine transit services and to establish and elevate quality standards. While many researchers have tackled this issue using diverse tools and methods, the prevalent approach involves surveys with discrete choice models or structural equations. However, a common limitation of these models lies in their inherent assumptions and predefined relationships between dependent and independent variables. To address these limitations, we introduce a novel perspective by harnessing machine learning (ML) models to gauge and predict passenger satisfaction. ML models are advantageous when dealing with complex, non-linear relationships and massive datasets, and do not rely on predefined assumptions. Thus, in this paper, we evaluate four ML models for the prediction of ratings of the quality of transit service. These models were calibrated using data from the Transantiago bus system in Chile. Among the ML models, the Random Forest model emerges as the most effective, showcasing its ability to analyze and predict passengers' satisfaction levels. We delve deeper into its capabilities by examining the impact of three pivotal variables on passengers' score ratings: waiting time, bus occupation, and bus speed. The Random Forest model is able to capture threshold values for these variables that significantly influence or have no effect on passenger preferences.