Following rapid urban development, higher demands are now being placed on urban open spaces in China, and the relationship between environmental elements and respondents’ behaviors in open spaces has become a common concern for researchers. Current research using geographic information systems has yielded macroscopic portraits of the behavioral trends and outcomes of research subjects, but evaluating their actual needs is complex. This paper proposes a new method to analyze the relationship between open spaces and respondents’ behaviors from a detailed perspective. Direct gradient analysis was employed with stratified sampling to select sample points in open spaces. Environment quality, ancillary facilities, and canal culture were selected as subjective evaluation factors. The greatest advantage of the proposed procedure is that it produces a ranking diagram, which compensates for the shortcomings of research methods that cannot directly express the actual needs related to respondents’ behaviors. From a case study in Jining, China, a location’s environmental quality and ancillary facilities were found to have the greatest influence on the behaviors of those using open spaces. Finally, strategies for improving environmental quality in open spaces are proposed.

Introduction: In the process of urbanization, public space plays an increasingly important role in improving the livability and sustainability of cities. However, effectively understanding the preferences of different groups for public space and conducting reasonable planning integrated with environmental and infrastructure elements remains a challenge in urban planning. This is because traditional planning methods often fail to fully capture the detailed behavior of residents. Therefore, the purpose of this study was to explore the empirical application of machine learning technology to public space planning along the Grand Canal in Shandong Province (China), analyze the behavior patterns and preferences of residents regarding different public spaces, and thereby provide support for data - driven public space planning.Methods: Based on survey data from 1008 respondents across 4 cities, this study employed machine learning methods such as K - means clustering, association rule mining, and correlation analysis to investigate the relationships between visitor behavior and the environmental characteristics of public spaces.Results: The application of these methods yielded several important results. Cluster analysis identified three distinct groups: young and middle - aged local residents with a preference for accessibility, middle - aged and elderly groups enthusiastic about cultural engagement, and diverse transportation users with mixed spatial preferences. Additionally, association rule mining uncovered strong correlations between location types and perceived attributes such as cleanliness and aesthetics. Moreover, correlation analysis indicated statistically significant positive correlations between aesthetics and cleanliness, as well as between safety and cleanliness.Discussion: This research offers valuable data - driven insights for public space planning and management. It demonstrates that machine learning can effectively identify and quantify key factors influencing public space use. As a result, it provides more accurate policy recommendations for urban planners and ensures that public space planning better meets the needs of different groups. For urban planners, the findings can guide the optimization of facility layouts for specific groups. For instance, adding canal cultural display nodes for cultural engagement groups and improving barrier - free facilities for groups with high accessibility needs, thereby enhancing the inclusiveness and utilization efficiency of public spaces.

The safe deployment of multiple Unmanned Aerial Vehicles (UAVs) in complex urban environments relies heavily on accurate environmental perception and efficient cooperative path planning. However, executing multi-UAV operations in low-altitude airspaces faces severe challenges due to the dual constraints of complex building clusters and steady-state wind field disturbances. These dynamic environmental factors frequently distort sensory expectations, inducing trajectory drift and degrading policy robustness. To address these limitations, this paper proposes an enhanced Dueling Double Deep Q-Network (D3QN) algorithm, termed NPD3QN, tailored for perception-aware multi-UAV cooperative path planning. By formulating the perceived environmental data (e.g., wind speed, obstacle distances, and inter-UAV states) into a Markov Decision Process, an N-step update strategy is integrated to enhance the characterization of long-term returns. Simultaneously, an improved Prioritized Experience Replay (PER) mechanism is developed to actively filter negative experiences and assign dynamic weights to critical state-action samples, thereby significantly elevating training stability. A 3D urban kinematic environment incorporating a steady-state simulated wind field is constructed. Extensive ablation and comparative results demonstrate that NPD3QN effectively maps high-dimensional state perceptions to robust control commands. In wind-disturbed scenarios, it generates highly streamlined cooperative trajectories, reducing the total path length by approximately 11.7% compared to the standard D3QN baseline. While currently evaluated within steady-state simulated constraints, this study establishes a robust, sensor-driven methodological foundation for autonomous multi-UAV cooperative path planning in wind-disturbed airspaces.

The purpose of this paper is to study consumer service-oriented APP and try to build a theoretical framework of its impact on urban space. The Physical space space when consumers use APP is defined as “window space”. Based on constructing the dynamic mechanism for the reorganization and diffusion of consumer service-oriented APP, this paper puts forward the hypothesis that “window space” gathers the physical space of service industry. Focusing on the spatial development in the Internet era, this paper predicts the transformation of urban space from “business center” to “crowd center”.

Traditional sanitation systems are increasingly challenged by contemporary waste collection demands, owing to limited service capacity, high operational costs, and uneven resource distribution. Sanitation robots, capable of high-precision waste identification and multi-robot collaboration, offer a promising alternative to improve collection efficiency. Their performance, however, critically depends on the scientific layout of base stations. This study proposes a multi-objective location and capacity optimization model based on an improved NSGA-II, incorporating multi-source data such as population density and spatiotemporal waste distribution. To strengthen the algorithm’s adaptability to complex urban environments, a self-adaptive crossover operator and a dynamic objective weighting mechanism are introduced. The TOPSIS method is then applied to evaluate and rank Pareto-optimal solutions, yielding an optimal layout and capacity plan for base stations. An empirical analysis in Lixia District, Jinan City, demonstrates that the optimized scheme reduces the number of facilities by one, improves service utility, and decreases the number of uncovered demand points to eight. Compared with existing planning approaches, the proposed model proves effective in dynamic scenarios and provides a collaborative multi-objective optimization framework for smart urban sanitation planning.

With the increasing deployment of service robots in urban public spaces, this study examines how robot proportion (RP) and composite interaction scenarios affect pedestrian circulation performance. An AnyLogic agent-based model of Quancheng Square, Jinan, was developed using a fixed total population of 10,000 agents. Three composite interaction scenarios (CIS-1–CIS-3) jointly vary interaction distance and duration. Human–robot mixed density (HRMD) and average pedestrian speed (APS) were evaluated over a common post-transient interval. To address population composition confounding and stochastic variability, an additional fixed-pedestrian-demand validation under CIS-3 used 10 independent random-seed runs per condition. Across the original runs, HRMD generally increased with RP, whereas APS showed a non-monotonic pattern. In the validation experiment, APS decreased monotonically as robot number increased when pedestrian demand was held constant, while the fixed-total design showed a high robot-level recovery. This contrast indicates that the apparent APS recovery was partly associated with reduced pedestrian demand. Overall, pedestrian circulation patterns were sensitive to both robot deployment and population composition.