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Modelling user satisfaction in public transport systems considering missing information

Author

Listed:
  • Eneko Echaniz

    (University of Cantabria)

  • Chinh Ho

    (The University of Sydney Business School)

  • Andres Rodriguez

    (University of Cantabria)

  • Luigi dell’Olio

    (University of Cantabria)

Abstract

Collecting data to obtain insights into customer satisfaction with public transport services is very time-consuming and costly. Many factors such as service frequency, reliability and comfort during the trip have been found important drivers of customer satisfaction. Consequently, customer satisfaction surveys are quite lengthy, resulting in many interviews not being completed within the aboard time of the passengers/respondents. This paper questions as to whether it is possible to reduce the amount of information collected without a compromise on insights. To address this research question, we conduct a comparative analysis of different Ordered Probit models: one with a full list of attributes versus one with partial set of attributes. For the latter, missing information was imputed using three different methods that are based on modes, single imputations using predictive models and multiple imputation. Estimation results show that the partial model using the multiple imputation method behaves in a similar way to the model that is based on the full survey. This finding opens an opportunity to reduce interview time which is critical for most customer satisfaction surveys.

Suggested Citation

  • Eneko Echaniz & Chinh Ho & Andres Rodriguez & Luigi dell’Olio, 2020. "Modelling user satisfaction in public transport systems considering missing information," Transportation, Springer, vol. 47(6), pages 2903-2921, December.
    • Handle: RePEc:kap:transp:v:47:y:2020:i:6:d:10.1007_s11116-019-09996-4
    DOI: 10.1007/s11116-019-09996-4
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    References listed on IDEAS

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    1. Nadav Shalit & Michael Fire & Eran Ben-Elia, 2023. "A supervised machine learning model for imputing missing boarding stops in smart card data," Public Transport, Springer, vol. 15(2), pages 287-319, June.

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