Enoch, MarcusKyriakopoulos, KostasBeechey, MattGhulman, Mayyar2026-08-062026https://hdl.handle.net/20.500.14154/79800Background: Rapid urbanisation and population growth have increased the demand for mobility in cities, leading to congestion and environmental issues such as air and noise pollution. Public transportation, particularly buses, is vital for accessibility, economic participation, and reducing social isolation, especially in areas with limited rail services. Despite these benefits, bus usage in many developed countries has stagnated at best and, in some cases, declined, while car travel is on the rise, underscoring the need to enhance bus services, make them more attractive, and increase patronage. A key challenge is the reliance on passenger perception surveys to assess bus service quality. Objective measures, such as temperature and crowding, are often collected in a fragmented manner, creating a gap in the evidence available to operators and policymakers. Without integrating perceived service quality (PSQ) with delivered service quality (DSQ), identifying practical service improvements to boost passenger satisfaction becomes obscure. Purpose: This thesis aims to investigate the impact of perceived service quality and actual delivered service quality—measured through real-time bus performance indicators— on bus passenger satisfaction and loyalty. Additionally, it evaluates predictive models that leverage real-time operational data from onboard bus sensors to predict passenger satisfaction. Methods: A quantitative research design was adopted that combined passenger perceptions with measured bus performance. Data were collected from two bus services in the UK (East Midlands): Service 90 (Nottingham–Newark) and Novus Direct (Leicester–New Lubbesthorpe). Perceived service quality was captured through an in-person, web-based customer satisfaction survey, which yielded 208 valid responses, while delivered service quality was measured using an onboard sensor system that recorded five operational indicators: punctuality, busload, speed, vibration, and temperature. The data analysis followed three stages: (1) principal component analysis reduced the perceived service quality indicators to latent dimensions; (2) the hypotheses were tested using PLS-SEM, together with multi-group analysis (MGA) to compare passenger groups; and (3) supervised machine learning was used to predict satisfaction from the operational indicators, with performance evaluated using standard classification metrics. Findings: Perceived service quality dimensions, namely pre-trip experience, on-bus environment, driver-related factors, and safety, were found to significantly contribute to predicting satisfaction, with the onboard environment being the most significant factor. Bus crowding and comfort: driving style, steering, and driver behaviour were among the most important attributes, with the highest loadings. There were no significant differences in these predictions between captive and choice riders, or between work and non-work trip groups, based on MGA. However, separate analyses of each group’s sample produced very few differences. On the other hand, in terms of delivered service quality, only actual bus load and actual speed were found to have a significant, albeit small, negative impact on satisfaction. The Random Forest classifier achieved the strongest predictive performance among the supervised classification models, with temperature, bus load, and punctuality identified as the most important predictors of passenger satisfaction. Contribution: This thesis presents a comprehensive investigation that significantly contributes to the field of public transport satisfaction through data collection and analysis. It is one of the few studies to utilise both subjective survey-based data and objective sensor-derived bus operational data to assess and predict passenger satisfaction with bus travel. Methodologically, the thesis demonstrates a practical approach for capturing, synchronising, and merging high-frequency onboard sensor data with passenger trip-level survey responses. While the integration of subjective and objective measures is established in other domains, this thesis is among the few to apply this approach to bus passenger satisfaction, using a purpose-built onboard sensor system to capture service quality at the individual-trip level and link it directly to passengers’ survey responses. This dual approach generates insights into both satisfaction and loyalty, as well as actionable operational predictions. Conclusion: This study provides bus operators and transport authorities with evidence-based methods for real-time service monitoring and targeted interventions to improve passenger satisfaction and loyalty. This research contributes to the existing body of knowledge, not only regarding buses but also for the public transport sector as a whole.298enPublic TransportService QualitySatisfactionPLS-SEMMachine LearningOnboard SensorsThesis