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Cities exposed their vulnerabilities during the COVID-19 pandemic. Unprecedented policies restricted human activities but left a unique opportunity to quantify anthropogenic effects on urban air pollution. This study aimed to explore the underlying urban development issues behind these restrictions and support a sustainable transition. The data from ground stations and Sentinel-5P satellite were used to assess the temporal and spatial anomalies of NO2. Beijing China was selected for a case study because this mega city maintained a "dynamic zero-COVID" policy with adjusted restrictions, which allowed for better tracking of the effects. The time-series decomposition and prediction regression model were employed to estimate the normal NO2 levels in 2020. The deviation between the observations and predictions was identified and attributed to the policy interventions, and spatial stratified heterogeneity statistics were used to quantify the effects of different policies. Workplace closures (54.8%), restricted public transport usage (52.3%), and school closures (46.4%) were the top three restrictions that had the most significant impacts on NO2 anomalies. These restrictions were directly linked to mismatched employment and housing, educational inequality, and long-term road congestion issues. Promoting the transformation of urban spatial structures can effectively alleviate air pollution. [ABSTRACT FROM AUTHOR]

The emergence of dockless bike-sharing (DBS) has made it more convenient for residents to travel within the city. The integration of public transit and DBS provides an effective method to sustainable transportation development. While some studies have accounted for heterogeneity of travel behavior, very few have considered how attitudes and preferences towards DBS affect combined travel mode choice. This study aims to reveal the effect of residents' attitude towards bike-sharing on combined travel mode choice using stated-preference data collected in Beijing, China. An integrated model of choice model and latent variables based on nested logit model (ICLV-NL) is used to incorporate two latent variables of social benefit and satisfaction with bike-sharing into the discrete choice model and thereby captures relationships between attitudinal factors and observed variables. The results indicate that compared with multinomial logit and nested logit models, the ICLV-NL model yields a better fit and explanatory power. The ICLV-NL model results show that positive impressions on bike-sharing make residents more likely to choose the integration of public transit and DBS, and the pick-up distance of DBS significantly affects the use of DBS as a feeder mode to public transit. Surprisingly, the status of DBS does not have a significant impact on travel mode choice. This paper provides more insights into the choice of the integration of DBS and public transit, which is helpful to the development of green and sustainable transportation. • An ICLV-NL model is proposed to account for the unobservable attitude factors towards DBS. • Users' attitudes, including social benefits and satisfaction with DBS, positively affect the choice of integration mode. • Female, high-education, low-income, and young passengers are more likely to utilize the integration of DBS and public transit. [ABSTRACT FROM AUTHOR]