Abstract 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.

    Highlights Machine learning (ML) models obtained better prediction of passenger satisfaction rating scores. ML models produce better predictions of the ratings than choice-based (statistical) models. Random Forest (RF) is the best of the ML models tested. RF can identify value thresholds for improving the performance of waiting time, bus occupancy, and bus speed.


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    Title :

    Predicting passenger satisfaction in public transportation using machine learning models


    Contributors:


    Publication date :

    2024-02-03




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English




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