Physical distancing and its association with travel behavior in daily pre-pandemic urban life: An analysis utilizing lifelogging images and composite survey and mobility data

This study analyzed physical distancing in people’s daily lives and its association with travel behavior and the use of transportation modes before the COVID-19 outbreak. We used data from photographic images acquired automatically by lifelogging devices every 5 seconds, on average, from 170 participants of a 2-day wearable camera study, in order to identify their physical distancing status throughout the day. Using deep-learning computer vision algorithms, we developed three measures which provided a near-continuous quantification of the proportion of time spent without anyone else within a distance of approximately 13 meters, as well as the proportion of time spent without others within approximately 2 meters. These measures are then used as outcomes in beta regression and multinomial logit models to explore the association between the participant’s physical distancing and travel behavior and transportation choices. The multidisciplinary research approach to understand these associations accounted for a number of social, economic, and cultural factors that potentially influenced their physical isolation levels. We found that participants spend a significant amount of time physically separated from others, without anyone else within 2 meters. The use of public transportation, automobiles, active travel, and an increase in trip frequency, including trips to transportation facilities, reduced the extent of physical distancing, with public transportation having the most significant impact. Higher incomes, strong social networks, and a sense of belonging to the community reduced the tendency for physical distancing. In contrast, factors such as age, obesity, dog ownership, intensive use of the Internet, and being knowledgeable about climate change issues increased the likelihood of physical distancing. The paper addresses a crucial gap in our understanding of how these factors intersect to create the dynamics of physical distancing in non-emergency situations and highlights their planning and operational implications while showcasing the use of unique person-based physical distancing measures derived from autonomously collected image data.