Modelling the effect of landmarks on pedestrian dynamics in urban environments

Abstract

Landmarks have been identified as relevant and prominent urban elements, explicitly involved in human navigation processes. Despite the understanding accumulated around their functions, landmarks have not been included in simulation models of pedestrian movement in urban environments. In this paper, we describe an Agent-Based Model (ABM) for pedestrian movement simulation that incorporates the role of on-route and distant landmarks in agents' route choice behaviour. Route choice models with and without landmarks were compared by using four scenarios: road distance minimisation, least cumulative angular change, road distance minimisation and landmarks, least cumulative angular change and landmarks. The city centre of London was used as a case study and a set of GPS trajectories was employed to evaluate the model. The introduction of landmarks led to more heterogeneous patterns that diverge from the minimisation models. Landmark-based navigation brought about high pedestrian volumes along the river (up to 13% of agents) and the boundaries of the parks (around 8% of the agents). Moreover, the model evaluation showed that the results of the landmark-based scenarios were not significantly different from the GPS trajectories in terms of cumulative landmarkness, whereas the other scenarios were. This implies that our proposed landmark-based route choice approach was better able to reproduce human navigation. At the street-segment level, the pedestrian volumes emerging from the scenarios were comparable to the trajectories' volumes in most of the case study area; yet, under- and over-estimation were observed along the banks of the rivers and across green areas (up to +7%, −11% of volumes) in the landmark-based scenarios, and along major roads (up to +11% of volumes) in the least cumulative angular change scenario. While our model could be expanded in relation to the agents' cognitive representation of the environment, e.g. by considering other relevant urban elements and accounting for individual spatial knowledge differences, the inclusion of landmarks in route choice models results in more plausible agents that make use of relevant urban information.