Over recent decades, planners in the U.S. have increasingly adopted mixed-use projects to reduce automobile dependency and strengthen local community identity, although results remain inconsistent across cities. Urban health and fitness outcomes are shaped by complex interactions between the built environment, socioeconomic factors, and demographic characteristics. This study introduces a Health and Fitness Index (HFI) for 28,758 U.S. ZIP codes, derived from normalized measures of walkability, healthcare facility density, and carbon emissions, to assess spatial disparities in health-supportive environments. Using four modeling approaches—lasso regression, multiple linear regression, decision trees, and k-nearest neighbor classifiers—we evaluated the predictive importance of 15 urban and socioeconomic variables. Multiple linear regression produced the strongest generalization performance (R² = 0.60, RMSE = 0.04). Key positive predictors included occupied housing units, business density, land-use mix, household income, and racial diversity, while income inequality and population density were negatively associated with health outcomes. This study evaluates five statistical formulations (Metropolis Hybrid Models) that incorporate different combinations of walkability, land-use mix, environmental variables, and socioeconomic indicators to test whether relationships between urban form and socioeconomic conditions remain consistent under different variable combinations. In cross-sectional multivariate regression, although mixed-use development in high-density areas is strongly associated with healthcare facilities, these areas tend to serve younger and more racially diverse populations. Decision tree feature importance rankings and clustering profiles highlight structural inequalities across regions, suggesting that enhancing business diversity, land-use integration, and income equity could significantly improve health-supportive urban design. This research provides a data-driven framework for urban planners to identify underserved neighborhoods and develop targeted interventions that promote walkability, accessibility to health infrastructure, and sustainability. It contributes to the growing literature on urban health analytics, integrating machine learning, spatial clustering, and multidimensional urban indicators to advance equitable and resilient city planning.

Cities in the United States (US) with a cool climate, such as Chicago and Boston, are experiencing a decrease in carbon emissions, possibly as a result of an emphasis on public transit and alternative energy sources. A dearth of dependable data on carbon emissions and urban comparison studies between metropolitan regions makes it difficult to support or refute ideal practices and policies. Thus, we evaluated the association between land use, walkability, socioeconomic characteristics, and carbon dioxide emissions at the level of the zip code. The current study analyzes the carbon footprints of four metropolitan locations in climatic zone 5 with a model of every US zip code in order to build a benchmarking prediction model for climate change in every US zip code. Our research indicates that in temperate climates, an increase in the number of enterprises within walking distance decreases CO2 emissions. This suggests that increasing the walkability of urban areas and renovating retail, art, entertainment, and leisure facilities in line with urban sustainability regulations can substantially reduce CO2 emissions.

Examining pedestrian crossing violations at high-risk road midblock crossings has become essential, particularly in high-speed corridors, as a result of accidents at crossings resulting in fatalities. Hence, this article investigates such behavior in Alexandria, Egypt, as a credible case study in a developing country. According to our research methodology, a comprehensive dataset of over 2400 field-observed video recordings was used for real-life data collection. Machine learning (ML) models, such as CatBoost and gradient boosting (GB), were employed to predict crossing decisions. The models showed that risky behavior is strongly influenced by waiting time, crossing time, and the number of crossing attempts. The highest predictive performance was achieved by CatBoost and gradient boosting, indicating strong interpersonal influence within small groups engaging in unsafe road-crossing behavior. In the same context, the Shapley additive explanation (SHAP) values for these variables were 3, 2, and 0.60, respectively. Subsequently, based on SHAP sensitivity analysis, the results show that the total time (s) and age group (40–60 Y) had a significant negative influence on model prediction converging to class 0 (e.g., crossing illegally). The results also showed that shorter exposure times increase the likelihood of crossing illegally. This research work is among the few studies that employ a behavior-based approach to understanding pedestrian behavior at midblock crossings. This study offers actionable insights and valuable information for urban designers and transportation planners when considering the design of midblock crossings.

The willingness to adopt Autonomous Vehicles (AVs) represents a crucial advancement from the sustainable mobility perspective. This is progressively continuing in the developed countries. A comparable shift is expected in developing nations; however, empirical studies remain limited, especially in areas where AVs have not yet been deployed. This study investigates the willingness to adopt AVs in a developing city where AVs have not been deployed yet. A comprehensive travel behavior questionnaire was conducted among local commuters in Alexandria, Egypt, to identify the influential variables affecting AV choice. The well-known machine learning classifier, Extreme Gradient Boosting (XGB), was employed to develop a forecasting model, which indicated a notable accuracy. The results indicated that trip cost was the most influential feature. On the other hand, there is a considerable level of mode captivity, since most travelers prefer to remain with their current mode, regardless of the effects of other variables. A significant share of travelers expressed concerns about shifting to AVs due to safety worries associated with the travel behavior of other transportation modes’ commuters. The analysis provides nuanced perspectives on the variables promoting modal shift toward the AVs, supporting future policies for smart urban mobility.