Articlegreen2016

Extraction of attribute importance from satisfaction surveys with data mining techniques: a comparison between neural networks and decision trees

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Transportation Planning and Optimization Customer Service Quality and Loyalty Data Mining and Machine Learning Applications

Abstract

When a public transport manager conducts a customer satisfaction survey (CSS), the goal is to determine the overall satisfaction of passengers with the service, as well as their satisfaction with specific aspects (e.g. frequency, speed, and comfort). Another fundamental objective is to assess the importance to customers of each attribute individually. Asking directly about this importance involves a number of drawbacks; therefore, most studies extract this importance from surveys that ask questions only about global satisfaction and specific satisfaction regarding each attribute. This paper investigates the capability and performance of two emerging data mining methods, namely, decision trees and neural networks, for extracting the importance of attributes from CSS. A total of 858 surveys about the metropolitan bus service in Granada (Spain) were used to model estimation and evaluation. The main advantages and disadvantages of each method are studied from the standpoint of public transport managers.

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References

2010The quality of service desired by public transport users 2008Public transit user satisfaction: Variability and policy implications 2014Quality of Service in Public Transport Based on Customer Satisfaction Surveys: A Review and Assessment of Methodological Approaches 2010Modelling user perception of bus transit quality 2003Work Travel Mode Choice Modeling with Data Mining: Decision Trees and Neural Networks 2012A classification tree approach to identify key factors of transit service quality 2009Evaluation of voluntary travel behaviour change: Experiences from three continents 2002Best practices in identifying customer-driven improvement opportunities

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2020Exploring Influencing Factors of Passenger Satisfaction toward Bus Transit in Small-Medium City in China 2020Weighted Variables Using Best-Worst Scaling in Ordered Logit Models for Public Transit Satisfaction 2018Transit quality evaluation: processes conducted by managers and operators

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