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.

Cycling significantly contributes to improving metro accessibility; however, the quality of bicycle environments surrounding metro stations remains insufficiently studied. This study develops a criteria–indicators assessment framework that incorporates both objective characteristics of bicycle infrastructure and subjective perceptions of bicycle access to metro stations. The framework consists of four primary criteria—accessibility, convenience, safety, and comfort—along with eighteen sub-level indicators. Taking central Tianjin as the study area, the study evaluated the cycling environment quality around eight representative metro stations by employing information entropy and the analytic hierarchy process, with cosine similarity used to compare the outcomes against human–machine adversarial scoring result to ensure analytical robustness. The findings reveal substantial disparities in cycling infrastructure, with safety and accessibility exhibiting higher scores than convenience and comfort. Additionally, cycling environment quality is higher around comprehensive and public-service stations compared to residential stations, while commercial stations exhibit the lowest quality. The study underscores the necessity of expanding protected bike lanes, enhancing route directness, and improving parking and wayfinding facilities to promote cycling as an effective first- and last-mile metro access mode.

Conventional tourism planning in ecologically fragile regions often adopts a reductionist perspective, failing to address the synergistic spatial interactions between ecological conservation, resource utilization, and infrastructure. To bridge this gap, this study develops a multi-constraint synergistic assessment framework for the dry-hot valley of Lujiang Dam (LJD) in China. Grounded in the understanding of rural tourism as a complex adaptive system, the framework innovatively integrates the InVEST model, kernel density estimation, and cumulative cost-distance algorithms to identify Natural Spatial Suitability for Tourism Development (NSSTD). Key findings include (1) pronounced spatial heterogeneity in habitat quality, with high-quality zones in the west/southeast requiring strict conservation; (2) a “barbell-shaped” clustering of natural/cultural resources at the valley’s northern and southern extremities, highly congruent with ethnic settlements; and (3) a “concentric layered” accessibility pattern where 88.08% of resources are within a 90 min drive. Crucially, the spatial overlay analysis revealed that NSSTD (54.74 km²) emerges not from single high-value zones but from areas of synergy, such as those with medium habitat quality coupled with high resource endowment and accessibility. These results provide a scientifically robust, spatially explicit layer for China’s “Multi-plan Integration” territorial spatial planning. They enable differentiated strategies—channeling development to southern corridors, implementing niche tourism in northern “structural hole” villages, and enforcing conservation in western habitats—thereby offering a replicable methodology to balance ecological integrity with sustainable rural development.

With increasing concerns over climate change and air pollution, sustainable transportation has become a critical component of modern city planning. Bike-sharing systems have emerged as an eco-friendly alternative to motorized transport, contributing to energy conservation and emission reduction. To elaborate on bike-sharing’s contribution to urban sustainable development, this study conducts a quantitative analysis of its environmental benefits through a case study of the Bluebikes program in the Boston area, using a longitudinal dataset of 20.07 million bike trips from January 2015 to December 2024, with data between January 2020 and December 2021 excluded. A combination of Scheiner’s model and Multinomial Logit model was adopted to evaluate the substitution of Bluebikes trips, an optimized Seasonal Autoregressive Integrated Moving Average (SARIMA) model was employed to predict future usage, while energy savings were calculated by estimating reductions in gasoline and diesel consumption. The findings reveal that during the analyzed period, Bluebikes trips saved 2616.44 tons of oil equivalent and reduced CO₂ and NO_X emissions by 7614.96 and 16.43 tons, respectively. Furthermore, based on the historical trends, it is forecasted that the Bluebikes program will annually save an average of 723.66 tons of oil equivalent and decrease CO₂ and NO_X emissions by 2422.65 and 4.52 tons between 2025 and 2027. The results highlight the substantial environmental impact of Bluebikes and support policies that encourage their usage.

Balancing the development of urban road infrastructure (URI) with the pace of urbanization is crucial to supporting high-quality urban growth. This study constructed a comprehensive evaluation framework of URI and urbanization using data from 101 Chinese cities between 2002 and 2021. The spatio-temporal characteristics of URI and urbanization were assessed using the entropy weighting method and the relative development index (RDI). Key variables were identified through the obstacle degree model and further refined via relative importance analysis. To investigate the nonlinear interactions among the most influential factors, a random forest model was employed in combination with SHapley Additive exPlanations (SHAP). The results revealed three key findings: (1) both URI and urbanization levels exhibited overall upward trends during the study period, although notable disparities were observed across cities; (2) URI development generally outpaced urbanization, indicating a lack of synergy between the two systems; and (3) key determinants of this mismatch included road density, total road area, the number of streetlights per unit road length, resident population size, and educational human capital. By integrating multidimensional URI and urbanization metrics in a comprehensive evaluation framework, this study provides new insights into the spatial synergy mechanisms and supports the formulation of tier-specific urban planning strategies.

Under globalization, architectural forms, spatial logic, and consumption experiences have become increasingly homogeneous, making placelessness particularly evident in large-scale commercial complexes. Establishing meaningful connections between globalized commercial spaces and local culture, urban memory, and everyday life has consequently become an important issue in research on architectural cultural sustainability. Guided by critical regionalism, this study takes placelessness as its point of departure and cultural sustainability as its evaluative perspective. It develops a two-level analytical framework comprising architectural analysis and user perception evaluation and applies this framework to the Chengdu New Century Global Center. Architectural analysis examines the building’s objective characteristics in terms of spatial image, functional organization, environmental setting, and cultural expression. User perception data were collected through a questionnaire survey and semi-structured interviews, followed by descriptive statistics and comparative analysis across four corresponding dimensions: spatial, functional, environmental, and cultural perception. The results indicate that the building’s megascale form, contemporary architectural appearance, and multifunctional organization contribute to higher levels of spatial and functional perception. Environmental perception is also relatively positive, although deficiencies remain in resting facilities, route organization, and detailed spatial experience. By contrast, cultural perception is substantially lower than the other dimensions. The marine theme, exoticized settings, and entertainment-oriented imagery are more closely associated with a globalized consumer landscape than with the spatial translation of Chengdu’s local culture, urban memory, and everyday life. Consequently, the building’s thematic identity has not been effectively translated into users’ recognition of place-specific characteristics, suggesting that the production of commercial complex space continues to prioritize consumption efficiency over cultural expression. By operationalizing the critical regionalist concept of sense of place as an empirically assessable analytical tool, this study extends its applicability to large-scale commercial complexes and provides a diagnostic framework and practical strategies for enhancing their cultural sustainability.