This study aims to guide the community life circle to create a green, travel-supportive built environment. It quantitatively analyzes the variations in car usage behavior based on the level of the built environment of the community and objectively reflects the car usage behavior based on the parking space utilization rate (PSUR). Ordinary least squares (OLS) and gradient boosting decision tree (GBDT) models were developed to describe the impact of the built environment on this utilization rate. An empirical analysis of the model was also conducted using the multisource, heterogeneous parking data of commercial parking facilities in the main urban area of Chongqing, China; the data include records of parking survey, points of interest, and road networks. The results showed that the GBDT model had a better fitting degree than the OLS model considering nonlinear effects. In terms of the contribution of community-built environment variables, distance to business center (14.30%), population density (14.20%), and land use mix (12.60%) considerably affect the PSUR, indicating that these variables have an important influence on the use of private cars. All built environment variables have nonlinear relationships, and the threshold effects reflect a complex relationship between the built environment and car usage behavior. This study provides refined suggestions for the spatial design and transformation of the community life circle.

In order to scientifically evaluate the characteristics and impact outcomes of transportation carbon emissions, this paper uses the panel statistics of 286 cities to measure transportation carbon emissions and analyze their spatial correlation characteristics. Afterwards, primarily based on the current research, a system of indicators for the impact factors of transportation carbon emissions was established. After that, ordinary least squares regression, geographically weighted regression, and multiscale geographically weighted regression models were used to evaluate and analyze the data, and the outcomes of the multiscale geographically weighted regression model were selected to analyze the spatial heterogeneity of the elements influencing transportation carbon emissions. The effects exhibit that: (1) The spatial characteristics of China’s transportation carbon emissions demonstrate that emissions are high in the east, low in the west, high in the north, and low in the south, with high-value areas concentrated in the central cities of Beijing-Tianjin-Hebei, the Yangtze River Delta, the Guangdong-Hong Kong-Macao region, and the Chengdu-Chongqing regions, and the low values concentrated in the Western Sichuan region, Yunnan, Guizhou, Qinghai, and Gansu. (2) The spatial heterogeneity of transportation carbon emissions is on the rise, but the patten of local agglomeration is obvious, showing a clear high-high clustering, and the spatial distribution of high-high agglomeration and low-low agglomeration is positively correlated, with high-high agglomeration concentrated in the eastern region and low-low agglomeration concentrated in the western region. (3) The effects of three variables—namely, GDP per capita, vehicle ownership, and road mileage—have a predominantly positive effect on transportation carbon emissions within the study area, while another three variables—namely, constant term, population density, and number of people employed in transportation industry—have different mechanisms of influence in different regions. Constant term, vehicle ownership, and road mileage have greater impacts on transportation carbon emissions.

The novel coronavirus disease (COVID-19) pandemic has had a significant impact on transportation. Understanding how the epidemic in China has affected people’s travel mode choices can help city managers analyze the travel mode of different populations in the postepidemic phase. Based on a travel behavior questionnaire conducted in Guangzhou, China, during the COVID-19 epidemic and points-of-interest data, this study explored the impact of the built environment, travel characteristics, and socioeconomic factors on changes in commuters’ travel modes during the postepidemic phase. We found that gender, age, occupation, the decline in the rate of travel frequency, and built environment characteristics significantly influenced the change in travel mode. When respondents had to give up public transport, those in different professions had different options for alternative means of transportation. The density of residential facilities, bus stations, and government institutions had a more significant impact on the change in the travel mode of commuters. The research results provide a theoretical basis for policy and practice. After the outbreak of a major public health event, urban transport managers and policymakers should consider individual heterogeneity and environmental factors when formulating strategies to address public travel in unconventional situations.