Public Transport Service Quality assessment
Summary
Assessments of public transport service quality have evolved to encompass multifaceted dimensions that reflect both operational performance and user experience. Core metrics include reliability (punctuality and adherence to schedules), responsiveness (frequency and real-time information), tangibles (vehicle condition and station facilities), assurance (safety and security measures) and empathy (staff courtesy and passenger support). Contemporary frameworks often integrate sustainability and innovation, recognising the need to align service improvements with environmental goals and digitalisation. Methodological approaches range from survey-based instruments such as SERVQUAL and Importance Performance Analysis to advanced multivariate techniques including structural equation modelling and decision tree algorithms. Recent efforts emphasise global comparability, embedding service quality indicators within Sustainable Development Goal targets and tailoring them to diverse contexts, from densely populated urban corridors to peripheral regional networks. Practical applications of these assessments inform policy-making, infrastructure investment and real-time operational adjustments, ensuring that transit systems remain attractive, equitable and efficient.
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Public Transport Service Quality assessment publication trend
The graph below shows the total number of articles in public transport service quality assessment across all publications each year (not limited to Nature Index journals).
Technical terms
SERVQUAL: A survey-based framework that measures service quality across five dimensions: tangibles, reliability, responsiveness, assurance and empathy.
Importance Performance Analysis (IPA): A method that plots service attributes on a grid to prioritise improvements based on their perceived importance and performance.
Partial Least Squares Structural Equation Modelling (PLS-SEM): A multivariate technique used to model complex relationships among latent variables when data do not meet strict distributional assumptions.
Necessary Condition Analysis (NCA): An analytical approach that distinguishes between conditions that must be present for an outcome to occur and those that are merely sufficient to influence it.
Factor Analysis: A statistical method for reducing a large number of observed variables into a smaller set of underlying factors based on shared variance.
References
- Users’ perception for innovation and sustainability management: evidence from public transport. Review of Managerial Science (2023).
- Necessary and sufficient conditions for attractive public Transport: Combined use of PLS-SEM and NCA. Transportation Research Part A Policy and Practice (2022).
- Passenger perception of commuter line service quality in Indonesia. International Journal of Data and Network Science (2023).
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