Abstract
Improvement in the understanding of the main drivers of passenger satisfaction with public transportation (PT) is crucial for increasing the attractiveness of the PT. This study explores the main drivers of satisfaction with PT and their interactions by training the PT satisfaction forest. The results of the random forest in-depth analysis show PT passengers see the service attributes service frequency, departure time accuracy, safety at stops, vehicle cleanliness, seating capacity, and information provision before the journey as the most important followed by ticket seller behavior, time table information provision, driver behavior, and ticket price. Hence the policies aimed at achieving these attributes could potentially improve the PT satisfaction. Furthermore, the results of the PT service attributes interaction analysis showed the fact that there is no strong interaction between satisfaction with different PT service attributes. This suggests treating them individually in models rather than considering their interactions as well. Furthermore, a multiple linear regression PT satisfaction model has been developed and validated in the current study that could be used as a policy tool in PT decision making.
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Acknowledgements
The research reported in this paper was supported by the Higher Education Excellence Program of the Ministry of Human Capacities in the frame of Artificial Intelligence research area of Budapest University of Technology and Economics (BME FIKP-MI/FM).
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Zefreh, M.M., Hussain, B. & Sipos, T. In-Depth Analysis and Model Development of Passenger Satisfaction with Public Transportation. KSCE J Civ Eng 24, 3064–3073 (2020). https://doi.org/10.1007/s12205-020-1871-7
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DOI: https://doi.org/10.1007/s12205-020-1871-7