The COVID-19 pandemic is found to be one of the external stimuli that greatly affects mobility of people, leading to a shift of transportation modes towards private individual ones. To properly explain the change in people’s transport behavior, especially in pre- and post- pandemic periods, a tensor-based framework is herein proposed and applied to Pun Pun–the only public bicycle-sharing system in Bangkok, Thailand–where multidimensional trip data of Pun Pun are decomposed into four different modes related to their spatial and temporal dimensions by a non-negative Tucker decomposition approach. According to our computational results, the first pandemic wave has a sizable influence not only on Pun Pun but also on other modes of transportation. Nonetheless, Pun Pun is relatively more resilient, as it recovers more quickly than other public transportation modes. In terms of trip patterns, we find that, prior to the pandemic, trips made during weekdays are dominated by business trips with two peak periods (morning and evening peaks), while those made during weekends are more related to leisure activities as they involve stations nearby a public park. However, after the first pandemic wave ends, the patterns of weekday trips have been drastically changed, as the number of business trips sharply drops, while that of educational trips connecting metro/subway stations with a major educational institute in the region significantly rises. These findings may be regarded as a reflection of the ever-changing transport behavior of people seeking a sustainable mode of private transport, with a more positive outlook on the use of bicycle-sharing system in Bangkok, Thailand.

A modal shift from car to bicycling could produce broad benefits for transportation efficiency, energy consumption, environmental quality, public health, and quality of life. While major cities in the US have promoted bicycling through a combination of investments in infrastructure and soft programs, bicycling remains at less than 4% of trips across major US cities compared to more than 10% in many European cities. This research explores differences in characteristics between current bicyclists (including recreational bicyclists, utilitarian bicyclists, and bike enthusiasts), potential bicyclists (people who do not regularly bicycle but have positive attitudes toward biking), and unlikely bicyclists (people who do not have positive attitudes toward biking). The characteristics examined include socio-demographics, transportation resources, perceptions of transportation modes, and attitudinal factors. Data come from the California Millennials Dataset 2015, a statewide survey of residents in the Millennial Generation and Generation X. A multinomial regression model shows that potential bicyclists have more limited transportation resources than unlikely bicyclists and differ with respect to many attitudinal factors and perceptions. The results may be helpful in developing strategies for promoting an increase in bicycling as a mode of transportation.