Modelling and simulating the movement of humans during outdoor nearby recreational activities can deliver important insight into the landscape services available to people in urbanized areas. Recreational activities such as walking, jogging and cycling are known to have a positive effect on people's mental and physical health, however in urban areas access to nearby recreation areas is sometimes lacking, under-developed or impeded by man-made infrastructures. In this context, understanding the spatial behaviour of humans during outdoor recreation is a crucial step towards implementing improvement measures. In this paper, we investigated the recreation strategies of multiple individuals and the flow in the movements of these individuals using a spatially-explicit agent-based model. The model combined information on the available infrastructures (roads, housing areas), the recreational potential of the landscape and rule-based movement strategies mimicking recreationists decision-making. At the individual level, we demonstrated that the shortest-path and weighted-path strategies were used the most during nearby recreation activities. At the population level, we successfully predicted recreationist flow patterns, highlighting gaps in path infrastructure and the most preferred path sections in a small town environment. We further illustrated the versatility of the model and its potential to support planning decisions to increase the accessibility of nearby recreation areas.