The influence of street spatial form on thermal comfort from urban morphology and human-centered perspectives has been underexplored. This study, utilizing multi-source data and focusing on urban central districts, establishes a refined index system for street spatial form and a thermal comfort prediction model based on extreme gradient boosting (XGBoost) and Shapley additive explanations (SHAP). The results reveal the following: (1) Thermal comfort levels display spatial heterogeneity, with areas of thermal discomfort concentrated in commercial zones and plaza spaces. (2) Compared to the human-centered perspective, urban morphology indicators correlate strongly with thermal comfort. (3) The key factors influencing thermal comfort, in descending order of importance, are distance from green and blue infrastructure (GBI), tree visibility factor (TVF), street aspect ratio (H/W), orientation, functional diversity indices, and sky view factor. All but the TVF negatively correlates with thermal comfort. (4) In local analyses, the primary factors affecting thermal comfort vary across streets with different heat-risk levels. In high heat-risk streets, thermal comfort is mainly influenced by distance from GBI, H/W, and orientation, whereas in low heat-risk streets, vegetation-related factors dominate. These findings provide a new methodological approach for optimizing urban thermal environments from both urban and human perspectives, offering theoretical insights for creating more comfortable cities.

Systematically and comprehensively enhancing road traffic safety using artificial intelligence (AI) is of paramount importance, and it is gradually becoming a crucial framework in smart cities. Within this context of heightened attention, we propose to utilize machine learning (ML) to optimize and ameliorate pedestrian crossing predictions in intelligent transportation systems, where the crossing process is vital to pedestrian crossing behavior. Compared with traditional analytical models, the application of OpenCV image recognition and machine learning methods can analyze the mechanisms of pedestrian crossing behaviors with greater accuracy, thereby more precisely judging and simulating pedestrian violations in crossing. Authentic pedestrian crossing behavior data were extracted from signalized intersection scenarios in Chinese cities, and several machine learning models, including decision trees, multilayer perceptrons, Bayesian algorithms, and support vector machines, were trained and tested. In comparing the various models, the results indicate that the support vector machine (SVM) model exhibited optimal accuracy in predicting pedestrian crossing probabilities and speeds, and it can be applied in pedestrian crossing prediction and traffic simulation systems in intelligent transportation.

The growing worldwide awareness of the significant benefits of bicycling as an urban transport mode has aroused great interest in exploring the role that bikeways play in promoting utilitarian bicycling. However, few studies assess the contribution of citywide bikeway provision with the inclusion of all facility types and differentiation of facility utilities. This study provides new evidence by evaluating the collective effects of bikeway kilometers per square kilometer, bikeway kilometers per 10,000 population, and low-stress bikeway proportion on the bicycle-commuting share in 28 US cities between 2005 and 2017. Using linear panel regression models, we found that the expansion of citywide bikeway infrastructure positively influences the share of commute trips by bicycle. The results also indicated that the proportion of low-stress bikeways has a stronger impact on the bicycling-to-work share than bikeway kilometers per 10,000 population, while the impact of bikeway kilometers per square kilometer ranks last. These findings may aid policy makers and planners in formulating sound city-level bikeway policies favoring sustainable urban transportation scenarios.

The development of cities and regions emerges from the complex associations at various spatial levels, highlighting the importance of a multi-scale approach to analyzing regional urban networks. This study attempts to establish a new analysis framework encompassing national, regional, and local dimensions by employing a population flow network in the Yangtze River Delta in China. It explores the inter-city connections and spatial structures of regional urban networks as well as the correlations and differentiations of urban functions under multi-scale interaction. The results indicate that: (1) Regional network demonstrates notable multi-scale interactions with an explicit hierarchical structure; (2) The roles and positions of different cities vary significantly across scales due to economic, administrative, locational, and transportation differences; (3) Different city types can drive their evolution by navigating through rescaling in a diverse multi-scale environment; (4) A positive correlation is observed when comparing the functional behaviors of cities across various scales. This study provides insights for cities to identify their strategic roles and adapt development strategies within the wider network framework, offering theoretical and practical contributions to multi-scale urban networks analysis.