Baek, KwanghoKhani, Alireza2024-11-202024-11-202024-10https://hdl.handle.net/11299/267839To improve transit service for off-peak travelers, an essential yet often underrepresented group, and promote social equity, this study examines off-peak transit commutes and transfers, with a focus on the transitway system in the Twin Cities. The research contrasts off-peak and peak travel behaviors using an onboard survey (OBS) from 2016 and automatic fare collection (AFC) data from 2018 to 2023. The initial analysis involved clustering trips from OBS into 16 regional zones and creating origin-destination matrices to explore spatial travel patterns. Key findings include longer peak-time trips (8.51 miles) compared to off-peak trips (5.74 miles) and a higher concentration of non-work trips during off-peak times. The study also reveals that off-peak trips are more dispersed geographically. In the second phase, path choice sets were generated for each respondent from OBS, and logistic regression models were used to analyze preferences for transitway versus bus-only routes. The results indicated a strong preference for transitways, with 60% of passengers opting for them over buses when travel times were equal. Finally, AFC data was integrated with OBS using machine learning techniques to examine long-term trends, including the impact of the COVID-19 pandemic. Post-pandemic data show an increase in off-peak commutes and transit trips with transfers despite an overall decline in transfers. This study provides insights into evolving transit usage behaviors and highlights the importance of the transitway system in facilitating efficient travel.Transitway Impacts Research ProgramSmart cardsOff peak periodsTransfersBus rapid transitLight rail transitMachine learningCluster analysisReport