Post-secondary Students' Travel Behavior through the Lens of Urban and Rural Contexts

This four-manuscript research investigates the travel behavior of post-secondary students across urban and rural contexts through four interrelated studies. The research addresses gaps in understanding how activity choices, departure time decisions, and active transportation behaviors are shaped by contextual, demographic, and policy factors. Two studies utilize the StudentMoveTO dataset, a detailed activity-travel diary survey from the Greater Toronto and Hamilton Area (GTHA), to model multi-destination trip-based activity type choices and sequential departure time decisions. These models capture interdependencies across multi-destination trips, enabling a more realistic representation of student travel patterns in dense urban environments. The other two studies draw on a custom-designed revealed–stated preference (RP–SP) survey administered to post-secondary students in rural Virginia. This survey incorporates both actual travel behavior and hypothetical choice experiments to assess rural students' mode preferences and the mental health impacts of active transportation under varying infrastructure and service conditions. The research adopted advanced econometric and machine learning approaches to better understand post-secondary students travel behavior. The urban-focused studies employ the dynamic discrete choice models and deep learning architectures Gated Recurrent Units (GRU), Long Short-Term Memory (LSTM) networks, and Transformers to capture sequential decision-making and nonlinear dependencies in activity type and departure time choices. The rural-focused mode choice analysis estimates both RP–SP multinomial logit (MNL) and RP–SP mixed logit models, enabling the combination of actual revealed preference data with stated preference scenarios while also capturing unobserved taste heterogeneity across individuals. The MNL model provides a baseline understanding of average mode choice behavior, whereas the mixed logit model relaxes the independence of irrelevant alternatives (IIA) assumption and accounts for random variations in preferences influenced by rural context, demographics, and travel conditions. The mental health and active transportation study uses principal component analysis (PCA) for dimensionality reduction, followed by Random Forest and other interpretable machine learning methods to identify the most influential factors. This combined methodological framework leverages both behavioral realism and predictive accuracy, bridging traditional econometric analysis with modern data-driven approaches. The findings reveal that student travel decisions are strongly influenced by institutional schedules, socio-demographic characteristics, and built environment features, with notable differences between urban and rural contexts. Sequential modeling shows that earlier departure times for initial trips significantly constrain subsequent activity timing, while rural analyses highlight that infrastructure quality and service availability directly affect both mode choice and perceived mental health benefits of active travel. These insights provide valuable evidence for transportation planners and policymakers seeking to design targeted, context-sensitive strategies that enhance mobility options, support student well-being, and promote sustainable transportation in both urban and rural communities.

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