Visitor capacity considering social distancing in urban parks with agent-based modeling

Abstract

The COVID-19 pandemic has greatly influenced society in the past few years. Park accessibility and social distancing are considered important under the threat of a long-term epidemic. However, measures that can maintain park accessibility and diminish virus spreading synchronously have been seldom studied before, which may threaten public health in all major urban parks globally. This paper proposed a methodology based on an agent-based model to analyze capacities for parks by simulating park visitor behaviors when they all are social distancing. The model was derived from historical visitor data and realistic visitor behaviors in three park settings. Then, park capacities of varied contact conditions, different park policies, and layout adjustments were analyzed. First, congestions caused by social distancing without proper visitor control are found inside all parks. Second, 85 to 3972 square meters per person is predicted as a safe space in different parks. Third, the current results can be easily adjusted according to various concerns regarding infection distance and rate. Finally, it can be inferred that information provisions are more effective than space design adjustments and mandatory measures. The results can guide park managers and those who plan and design park settings. They are also helpful in improving knowledge of the mechanisms behind visitor behaviors. Moreover, these findings can be tested and verified in a variety of public spaces with many other contact-based illnesses.