Fostering happiness among public transit users: Analyzing customer satisfaction surveys through non-traditional approaches
Bibliographic record
Abstract
Customer satisfaction surveys are one of the most heavily utilized tools within the public transit industry to gain insight into the perceptions, attitudes and behaviours of customers. The efficacy of policies and service improvement strategies derived from satisfaction data are presently limited by the methodologies that are used to analyze this data. The overarching goal of this dissertation is to expand the understanding of public transit customer satisfaction through incorporating personal, spatial and contextual factors. This research goal will be achieved through answering the following research question: How can customer satisfaction data be effectively analyzed and utilized to generate targeted service quality improvements? This dissertation consists of four research objectives which are as follows: 1.To show differences in perceptions of service quality across different socioeconomic neighbourhoods in a highly competitive and well-monitored transit market; 2.To develop a transit market segmentation approach that includes personal, spatial and contextual factors; 3.To understand the extent to which transfers influence trip satisfaction; 4.To expand our understanding of how public transit performance measures can be integrated into satisfaction analyses to better predict overall satisfaction.The four research objectives each correspond to an analysis chapter comprising this manuscript-based dissertation. These chapters build on one another, and collectively aim to advance existing methods of analyzing customer satisfaction data for better knowledge of the transit market. The first two chapters of this dissertation present spatial methods of analyzing customer satisfaction data. Chapter two examines satisfaction with bus service across neighbourhoods of varying socio-economic status in London, UK. This spatial method allows agencies to identify areas for improvement at a more disaggregate level than previous research. The third chapter presents a new market segmentation approach that incorporates spatial and contextual factors that have not previously been incorporated into the practice of segmenting the transit market. This new method is demonstrated using a sample of commuter rail users in the Greater Toronto and Hamilton Area, Canada. The remaining two chapters demonstrate how contextual and operational data can be incorporated into satisfaction analyses. Chapter four explores the relationship between transferring and trip satisfaction using a survey of transit commuters to McGill University. In Chapter 5, satisfaction levels among users of a local and a limited-stop bus service in Vancouver, Canada are studied, while controlling for operational characteristics describing the service these users experienced, such as crowding. A concluding chapter consolidates the findings of these chapters and presents policy and research implications to support a better understanding of satisfaction. More specifically, this dissertation contributes to the knowledge in the following four ways:m •Identifies important shortcomings regarding how customer satisfaction data is analyzed; •Develops reproducible methodologies to both integrate spatial data into the analysis of satisfaction levels, as well as to apply spatial analysis techniques to examine satisfaction with service at a local scale (i.e. the route or neighbourhood level); •Demonstrates how detailed trip data can be applied to understand how specific service characteristics influence satisfaction levels; •Shows how transit performance data can be integrated into satisfaction analyses to provide a more complete understanding of passenger satisfaction levels. As customers are the most important judges of service quality, this dissertation demonstrates how transit agencies can more effectively analyze customer perceptions of service as stated in satisfaction surveys and generate policies for service improvements that will have the strongest impact on riders.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".