Drivers of trip satisfaction over time: A case study across rapid transit systems in Montreal, Canada

As public transit agencies struggle to win back riders lost during the COVID-19 pandemic, understanding what drives user satisfaction has become critical to retention and ridership recovery. While most research on transit satisfaction relies on cross-sectional data, this paper adopts a longitudinal approach, integrating the hierarchy of transit needs framework to assess whether satisfaction determinants shift over time and across modes using Montreal, Canada, as a case study. Drawing on panel data from the Montreal Mobility Survey, the analysis focuses on 354 rapid transit commuters surveyed in 2023 and 2024, including users of the metro and the newly launched Bus Rapid Transit (BRT) and Light Rail Transit (LRT) systems. We estimated mode-specific weighted Bayesian models and compared the relative importance of different factors across hierarchical layers. Results indicate that satisfaction with the metro remains stable and increasingly driven by comfort, while the BRT shows improving satisfaction over time with strong reliance on functional factors such as waiting time. For the LRT, both functional and hedonic attributes play central roles in satisfaction, particularly travel times and information availability. This research highlights the importance of sequencing investments to match service maturity, prioritizing foundational needs, such as service reliability, before higher-order features, and investing early in perception-building strategies for new systems. These results provide empirical evidence on the temporal dynamics of satisfaction and offer guidance for transit agencies on strategies to retain current riders and attract new ones in a post-pandemic context.