Transit service quality analysis using cluster analysis and decision trees: a step forward to personalized marketing in public transportation

Abstract

A transit service quality study based on cluster analysis was performed to extract detailed customer profiles sharing similar appraisals concerning the service. This approach made it possible to detect specific requirements and needs regarding the quality of service and to personalize the marketing strategy. Data from various customer satisfaction surveys conducted by the Transport Consortium of Granada (Spain) were analyzed to distinguish these groups; a decision tree methodology was used to identify the most important service quality attributes influencing passengers' overall evaluations. Cluster analysis identified four groups of passengers. Comparisons using decision trees among the overall sample of all users and the different groups of passengers identified by cluster analysis led to the discovery of differences in the key attributes encompassed by perceived quality.

A Bus Service Evaluation Method from Passenger’s Perspective Based on Satisfaction Surveys: A Case Study of Beijing, China

“…Many studies have focused on the satisfaction evaluation and performance assessment of public transportation. Some related studies have been conducted on the topics of the index system construction [1,2], the method of evaluation [2,3] and the analysis of the influencing factors [4,5]. Table 1.…”

Comparing Transport Quality Perception among Different Travellers in European Cities through Co-Cluster Analysis

“…To clarify, while in the first case elements of the same cluster are expected to be as similar as possible, in the other one, elements from different clusters are requested to exhibit the highest dissimilarity [17]. Some works can be found in the literature that have applied this methodology in various ambits of transport engineering, such as transit quality evaluation [5,13] or tours classification [18]. Although some research has been carried out on the use of clustering to establish homogeneous groups of users with regards to service quality evaluation [11,13], to the authors' knowledge this topic has not yet been proposed with a European benchmarking perspective.…”

“…Some works can be found in the literature that have applied this methodology in various ambits of transport engineering, such as transit quality evaluation [5,13] or tours classification [18]. Although some research has been carried out on the use of clustering to establish homogeneous groups of users with regards to service quality evaluation [11,13], to the authors' knowledge this topic has not yet been proposed with a European benchmarking perspective. Moreover, in all these works the authors adopted standard k-means variants as the clustering method, but when data are heterogeneous and high-dimensional it fails in detecting high-quality and robust clusters [19].…”

“…As observed in [13], one of the problems with data acquired with personal and subjective surveys is the "subjective nature of such measurements, which consist of fuzzy and heterogeneous passenger assessments". Limited literature can be found on this topic, since it seems to be a rather innovative and challenging issue.…”

“…Moreover, various features can be taken into account in this stratification process: socio-economic attributes and travel attitudes [14], travel characteristics, trip satisfaction, trip practicality, familiarity and age [15], objective and subjective multimodal mobility levels, desired modal changes, and socio-demographic attributes [16]. Reference [13] showed that a possible solution could be the stratification of the sample of travellers according to their socio-economic characteristics or their travel attitude. According to the authors, the use of a data mining technique such as cluster analysis can be useful in transit service quality analysis, due to its ability in the segmentation process to, "reduce this heterogeneity, by segmenting the sample of passengers on groups that share some common characteristics, and that have more homogeneous perceptions about the service".…”