Agent-Based Modeling at the Micro-Scale of Urban Environments: A Framework Integrating Imitation in Pedestrian Wayfinding

Previous studies on pedestrian behavior have not fully explored how the presence of others can serve as informational cues about the environment, nor how imitating their choices may act as an effective strategy for wayfinding. This paper presents a framework that integrates imitation into agent-based models of pedestrian movement, addressing the role of social influence in the wayfinding process. Grounded in social psychology, the framework assumes that agents initially lack both a map and specific destinations, requiring them to explore their environment. During this exploration, agents decide on their direction based on factors such as accessibility, attractiveness, popularity, and congestion of available paths. Imitation can be an effective strategy, as other pedestrians might have prior knowledge of the environment. However, this behavior can lead to congestion when too many pedestrians follow the same path. The framework provides a practical approach to modeling pedestrian flows in dynamic environments like temporary markets, fairs, and public events, where navigation is largely driven by exploration and social interaction. By integrating social learning with agent-based modeling, the framework offers new insights for urban design and planning. At the urban scale, it can help urban planners optimize pedestrian flow in high-traffic areas such as tourist attractions, temporary markets, and public plazas. At the municipal and regional levels, it can assist in developing transportation systems by managing pedestrian traffic in transit hubs and public transport terminals, ensuring more efficient and less congested movement across cities and regions.