Hajimoradi, Moloud2025-09-242025-09-242025-09-242025-08-08https://hdl.handle.net/10012/22543Long‑distance (LD) travel comprises a disproportionately large share of total passenger-kilometers despite representing a small fraction of trip counts. Yet LD travel remains underexamined in Canada’s vast geographic context. This thesis develops and applies a comprehensive modeling framework to analyze LD trip generation and mode choice for Canadian residents, leveraging data from Statistics Canada’s National Travel Survey (NTS) (January 2018–February 2020) and a new national multimodal transportation network construct for this thesis. The network integrates geospatial centroids for Census Subdivisions with travel-time estimates for automobile, air, intercity rail, and bus modes. Trip generation was examined through both disaggregate (person‑level hurdle and zero‑inflated count models) and aggregate (origin‑destination zone‑pair hurdle models) approaches, incorporating socioeconomic variables (age, income, gender), trip attributes (distance, season), and accessibility measures. Results indicate that accessibility, rather than traditional demographics, may be an important variable in predicting whether a LD trip occurs: with lower local accessibility and greater distance to airports increasing the likelihood of at least one trip in the given month. However, once the trip “hurdle” is crossed, trip counts are less sensitive to accessibility, underscoring behavioral impacts. Even with the very large dataset, models are very weak suggesting that travel surveys are a weak method for understanding LD travel. Mode choice was analyzed using a Multinomial Logit (MNL) model alongside Machine Learning (ML) classifiers (Decision Trees, Random Forests, Support Vector Machines, Neural Networks). While MNL yields interpretable elasticities, with intercepts confirming preference for the driving mode and positive income effects for air travel, ML methods achieve superior predictive power. Feature importance from Random Forests highlights travel time (especially driving) as the dominant determinant, followed by accessibility, with sociodemographic and seasonal factors playing secondary roles. Mode choice models with alternative specific travel times are viable with publicly available data and these results support the need to seriously consider use of ML in LD mode choice even though understanding the influence of individual behavioral factors becomes more limited. Long-distance passenger travel demands models are not typically available in Canada despite their utility for infrastructure, service and environmental planning. This thesis research demonstrates models are viable with existing publicly available data.enMaster Thesis