Quality Measurement and Optimization Prediction of Urban Waterfront National Fitness Public Space Based on Behavior Simulation——Taking Shanghai Huangpu River as an Example

Objective: “National Fitness” is the national strategy, promoting the development of green and convenient "new fitness carrier". Waterfront public spaces can offer comfortable environments for exercise, providing the free, anytime, anywhere fitness opportunities. Therefore, they have the potential to serve as the excellent "new fitness carrier". However, the fitness resources available in many waterfronts have not been fully exploited. Based on the multi-agent behavior simulation technology and waterfront fitness quality assessment system, this study conducts a quality measurement of nine typical waterfront public spaces along the Huangpu River and further optimizing of the Minsheng Wharf area, aiming to provide a systematic method for quality assessment and optimization forecasting. Two key aspects are highlighted, one is the simulation of waterfront fitness behavior, including model selection, operating mechanisms, and fitting methods, the other is the measurement indicators related to blue-green fitness, which are derived from measurement of blue-green exercise space, blue-green exercise behavior, and public services for fitness. Methods: The first is the development of an indicator system. Based on existing literatures, waterfront fitness indicators are categorized into three areas: fitness venues, facilities, and environment. Reorganized from the perspective of fitness needs, these became three measurement dimensions: venue characteristics, facility service levels, and environmental support for physical well-being. To address fitness demands and waterfront space issues, eight primary indicators were identified, including accessibility, openness, diversity, completeness, adaptability, efficiency, water proximity, and comfort. Additionally, 21 secondary indicators were selected based on waterfront space characteristics, fitness behavior, and simulation outputs. Their calculation formulas and weights were determined using expert questionnaires and the analytic hierarchy process. The second is the simulation. On-site surveys collected spatial and pedestrian activity data, while eye-tracking and perceived restoration data were obtained through recovery experiments. The initial and corrected recovery weights for different age groups and exercise types were calculated, forming the operational mechanism of the simulation model. The agent-based and social force models were then developed. After qualitative and quantitative adjustments, the model was validated, and the final results were produced through data analysis and indicator output. Results: The comprehensive quality assessment indicated that Y1 and Y2 ranked in the top tier, with X1, X2, and C2 following in the second tier. The alignment between the dimension-specific and overall measurement results showed that the environmental support for physical well-being was greater than characteristics of fitness venues, which were approximately equal to the service levels of facilities. The highest-performing individual indicators included the average open hours of fitness spaces, proximity to sanitary facilities, shoreline openness, and perceived restoration. Among three measurement dimensions, the most significant contributing indicators were the per capita open fitness space area, fitness facility utilization rate and density of people near the water. Based on the quality measurements of the nine sections, reference values for the 21 secondary indicators were derived using the normal distribution method, serving as a benchmark for the quality assessment of other waterfront fitness spaces. Application: These reference values were then applied to the quality assessment and optimization forecast for Minsheng Wharf. Initially, the current quality was measured, showing that its overall quality was only superior to Y2 among nine sections. The primary issues were found in the service levels of facilities and environmental support for physical well-being. It identified specific problematic indicators within each dimension and analyzed the related issues. Following this, eight optimization elements and 19 detailed improving points were proposed. Simulations were run for different detailed levels of individual elements, and the optimization plans were streamlined. Finally, taking into account crowd flow and pre-established conditions, various optimization scenarios were rehearsed and compared, resulting in the identification of optimal and suboptimal plans under all conditions. The results demonstrated that the comprehensive quality scores of all eight optimization plans were significantly higher than the current condition. Conclusion: The quality assessment system for public fitness spaces in urban waterfronts should be based on fitness demands, taking into account the characteristics of waterfront environments and outputs from behavior simulations. This research integrates spatial, behavioral, and psychological dimensions, combining static and dynamic indicators. Three key measurement dimensions were identified: characteristics of fitness venues, service levels of facilities, and environmental support for physical well-being, along with eight primary and 21 secondary indicators. Formulas, weights, and reference values were established to provide a quantitative basis for evaluating both the current status and potential optimization of waterfront fitness spaces. Improvement requires identifying problematic dimensions, indicators, and elements, as well as making accurate forecasts for future usage of optimized plans. Leveraging full-time dynamic simulation, multi-plan comparison, and virtual scenario rehearsals, this study developed methods for fitness preference analysis, model construction, and simulation fitting. The optimization forecast process involved assessing current quality, simulating single-element optimizations, simplifying plans, and generating multidimensional recommendations. These methods were applied to assess the quality of nine typical waterfront sections along the Huangpu River, producing results that serve as benchmarks for other areas.