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Agent-based models (ABMs) in urban modelling simulate how individual entities—such as residents, businesses, and vehicles—interact within cities to collectively produce complex urban patterns. They are particularly useful for exploring how micro-level decisions create macro-level phenomena in urban systems such as land use, traffic flow, and neighborhood change.​

An agent-based model consists of autonomous agents that follow behavioral rules within a virtual urban environment. These agents—representing people, households, or organizations—make independent decisions influenced by their surroundings and interactions with other agents. Over time, these interactions generate emergent large-scale patterns like traffic congestion, urban sprawl, or gentrification trends without having those outcomes predefined.​

Applications in Urban Geography

1. Urban Growth Simulation: ABMs are used to model urban growth dynamics by combining population behavior and spatial constraints. They can simulate how land use changes unfold based on local decision-making and planning policies.​
2. Traffic and Transportation Modeling: By simulating thousands of individual commuter decisions, ABMs help evaluate the impact of new transit routes, congestion pricing, or road closures before real-world implementation. For example, Barcelona used ABMs to test lane reductions on Gran Via.​
3. Land Use and Zoning Optimization: Urban planners use ABMs to predict how zoning policies affect residential and commercial development patterns, offering a dynamic alternative to static urban growth models.​
4. Service Accessibility Simulations: The combination of cellular and vector agents in hybrid ABMs helps distribute public services such as schools or hospitals equitably across a city, as demonstrated in simulation studies on Casablanca.​

Advantages

• Captures human behavioral complexity and heterogeneity.
• Identifies emergent patterns that top-down planning might overlook.
• Allows testing of development scenarios in virtual “urban laboratories.”
• Supports adaptive planning via integration with real-time data and AI.​

Emerging innovations link ABMs with AI, digital twins, and IoT sensor data, enabling real-time simulations of urban dynamics. Enhanced visualization through VR and 3D rendering further helps planners interact with complex model outputs more intuitively. These advances are driving ABMs toward becoming central decision-support systems in smart city planning.​

Agent-based models revolutionise urban geography and planning by transforming static city representations into dynamic, behaviour-rich systems, enabling predictive, adaptive, and resilient approaches to shaping the cities of the future.