Although many studies have revealed that both air quality and walking activity are dominant contributors to public health, little is known about the relationship between them. Moreover, previous studies on this subject have given little consideration to the day-to-day atmospheric conditions and floating populations of surrounding areas even though most pedestrian count surveys are not conducted on a single day. Against this backdrop, using the 2015 Pedestrian Volume Survey data and quasi-real-time weather, air quality, and transit ridership data in Seoul, this study investigates the relationship between particulate matter (PM)10 and pedestrian street volumes empirically. The regression results suggest that PM10 concentration determines people’s intention to walk and affects the volume of street-level pedestrians. The three regression models, which adopted different spatial aggregation units of air quality, demonstrated that PM10 elasticity of pedestrian volume is the largest in the borough-level (the smallest spatial unit of air quality alert) model. This means that people react to the most accurate information they can access, implying that air quality information should be provided in smaller spatial units for public health. Thus, strengthening air quality warning standards of PM is an effective measure for enhancing public health.

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The preference for walking and the resulting pedestrian activities have been considered key success factors for streets, neighborhoods, and cities alike. Although micro- and meso-scale built environment factors that encourage walking have been investigated, the role of macroscopic factors such as regional centrality in explaining street-level pedestrian volume is often neglected. Against this backdrop, this study examines the relationship between built environments and street-level pedestrian volume using Smart Card and pedestrian volume survey data from Seoul after controlling for transport ridership as a proxy for regional centrality. As a preliminary study, we analyzed 36 regression models applying different sets of transit ridership variables and found that the combination of bus ridership within 400 m and subway ridership within 300 m best explained the variation in pedestrian volume on a street. Then, the effects of the 3D variables (density, diversity, and design) on pedestrian volume were compared before and after controlling for ridership within this spatial range. The results demonstrated that, after taking transit ridership into account, the influence of built environment variables is generally reduced, and the decrease is more pronounced among walkshed-level 3D variables than street-level variables. Particularly, while the effect of “design” (street connectivity) on pedestrian volume appeared to be negatively significant in the constrained model, it was found to be insignificant in the unconstrained model which controlled for transit ridership. This suggests that the degree of street connectivity is influenced by regional centrality, and accordingly, the coefficient of the “design” variable in our constrained model might be biased. Thus, to accurately understand the effect of the meso-scale 3D variables on pedestrian volume, both micro- and macro-scale built environmental factors should be controlled.