Predicting Desire Paths: Agent-Based Simulation for Neighbourhood Route Planning

This study addresses how informal “desire paths” emerge when urban infrastructure does not align with pedestrian preferences, focusing on small, local neighbourhoods to bridge a recognised research gap in urban planning. It introduces an agent-based modelling (ABM) framework that builds on path optimisation, affordance theory, and two novel models: a weighted preferences model that accounts for varied pedestrian priorities beyond route length and an obstacle avoidance model that achieves 60 % accuracy, indicating it can successfully replicate aspects of natural navigation, but still showing potential for further refinements. Conducted in three-dimensional simulations using Unity3D and the Agent-Based Modelling for Unity (ABMU) toolkit, this approach yields complex path networks that illuminate pedestrian decision-making processes and highlight the impact of diverse priorities, including local optimisation of route length, on network efficiency. By clarifying these ABM-driven insights, we underscore the significance of integrating multiple pedestrian preferences into urban planning to foster more sustainable development in local neighbourhoods. • The study utilises agent-based simulation to predict pedestrian-created paths aligned with user preferences. • The simulation incorporates path optimisation, perception of space, affordance theory, and collective behaviour. • Agents leave “footprints” on surfaces, increasing the affordance and mimicking the formation of desired paths over time. • A new obstacle avoidance model was proposed, offering improved analysis for more complex environments. • The avoidance model showed 60 % accuracy, showing progress in modelling realistic behaviour.