Prediction approach on pedestrian outdoor activity preference under factors of public open space integrated microclimate

Abstract

This study conducted measurements of microclimates and investigations of spatial environments and human activities in six typical waterfront public spaces in Shanghai. The aim was to explore the synergistic influence mechanism of public open space (POS) integrated microclimate (POSIM) on pedestrians’ activities to predict the utilization of public space. The measurement period was during the spring and early summer when outdoor activities were more diversified. 121 observation points were set up in urban waterfront public spaces, and an outdoor activity characteristic database of 25,583 people was established. In addition, multiple linear regression and nonlinear neural network models were introduced to analyze data from different types of activities to calculate the fitness of the models and the influence weights of the variables. The results indicated that the neural network had stronger predictive ability for the spatial integration of microclimate demands of different activities. The prediction degree for strolling and sitting activities was the highest, with R2 (goodness of fit) of 0.704 and 0.844, respectively, while the prediction degree for viewing and sports activities was lower (R2 < 0.5). This study integrated the synergistic influence of urban waterfront public open spaces and microclimates factors on pedestrian outdoor activities to predict the preference patterns of different activities for spatial types. Focus on the requirements of space occupants, this study analyzed the public space environment from the bottom up and provided reference and inspiration for subsequent design optimization of urban waterfront areas.