Everything you need to know about desire paths, agent-based pedestrian simulation methods, and how this interactive simulator works.
A desire path (also called a desire line, social trail, or goat track) is an informal route created by people repeatedly choosing a path that better matches convenience, comfort, or directness than planned infrastructure.
Desire paths emerge when official paths force detours, cross green spaces, or don't align with natural movement patterns. They are visible evidence of the gap between designed and used space — one of the most common observations in landscape architecture and urban planning.
Desire path formation follows a well-documented positive feedback loop:
The first few pedestrians take a shortcut across grass or soil, creating an initial worn trace.
The worn surface becomes easier and more comfortable to walk on than the surrounding terrain — higher affordance for future walkers.
People naturally follow paths others have created. The visible trail signals that this route is safe and efficient.
Over time, repeated use creates a distinct, well-defined path — sometimes permanent even if the surrounding landscape changes.
Agent-based modeling (ABM) is a computational approach that simulates the actions and interactions of autonomous individual agents to assess their effects on the system as a whole. In the context of desire path research:
The key insight of ABM is that complex, realistic desire path networks emerge from simple individual rules — no top-down design required. This mirrors how actual desire paths form in the real world.
The simulator uses Uber's H3 hexagonal hierarchical spatial indexing system at resolution 15, providing approximately 0.88-meter spacing between cell centers. This creates a fine-grained grid suitable for detailed pedestrian flow simulation over real-world terrain data.
| Surface Type | Cost |
|---|---|
| Barrier (buildings, water) | ∞ (impassable) |
| Dense planting | 4.0 |
| Lawn / soft | 2.5 |
| Pavement / easy walking | 1.0 |
This simulator extends the framework described in:
Bossowski, J., Szandała, T., & Mazurkiewicz, J. (2025). Predicting Desire Paths: Agent-Based Simulation for Neighbourhood Route Planning. Computer Environment and Urban Systems (CEUS).
The work builds on Ma et al.'s agent-based model, adding weighted preferences for route length versus affordance trade-offs, and an obstacle-aware Dijkstra gradient pathfinding system.
Desire paths (also called desire lines, social trails, or goat tracks) are informal routes created by people repeatedly choosing a path that better matches convenience, comfort, or directness than planned infrastructure. They emerge when official paths force detours, cross green spaces, or don't align with natural movement patterns between destinations.
Desire paths form through a positive feedback loop. When the first few pedestrians take a shortcut across grass or soil, they wear down the surface. This worn surface becomes easier and more comfortable to walk on than the surrounding terrain, encouraging more people to follow the same route. Over time, repeated use solidifies the informal path into a visible trail.
Agent-based modeling (ABM) simulates individual autonomous agents to assess their collective effects. In pedestrian simulation, each agent represents a virtual person making route decisions based on terrain resistance, distance to destination, and accumulated wear from previous agents — producing emergent desire path networks.
H3 is a hexagonal hierarchical spatial indexing system developed by Uber. This simulator uses resolution 15, providing approximately 0.88-meter spacing — fine enough to capture detailed pedestrian flow patterns while remaining computationally efficient for real-time simulation.
Affordance theory (Gibson, 1977) describes the action possibilities an environment offers. Paved surfaces offer high walking affordance; grass offers lower initial affordance that increases as agents leave wear traces — creating the positive feedback loop essential to desire path formation.
The simulator uses Dijkstra's shortest-path algorithm to compute distance gradients from each destination across all accessible cells. Agents navigate toward the neighboring cell with the lowest gradient value, enabling strategic obstacle-aware routing around buildings and barriers.
The simulator offers: Affordance Weight (1–8), Distance Penalty (1–8), Vision Depth (5–30), Field of View (30°–360°), Crowd size / Agents per Weight Unit (25–500), Temperature (0–2), Grid resolution (0–15), and Emergent Wear toggle. In the app these appear as Comfort preference, Shortcut preference, Look-ahead, Viewing arc, Crowd size, Spontaneity, and Grid resolution. Each parameter controls a different aspect of agent behavior.
Urban planners can predict where pedestrians will create new routes, design park layouts aligned with natural foot traffic, identify walkable zones around facilities, plan paths to protect sensitive areas, and export results as GeoJSON for GIS analysis.
This simulator extends Bossowski et al. (2025) published in CEUS, building on Ma et al.'s agent-based model with weighted preferences and Dijkstra-based obstacle avoidance. The web implementation uses H3 hex grids for real-world map integration.
Yes. The simulator exports flow networks as GeoJSON files with desireScore values per hex cell, importable into QGIS, ArcGIS Pro, or any platform supporting GeoJSON.
The model operates at neighborhood scale. Weather, time of day, group behavior, and cultural norms are not modeled. Obstacle avoidance uses strategic pre-planned navigation rather than dynamic reaction. Future improvements include real-time GPS data integration and multi-agent social dynamics.
The study traces to early 20th-century urban planning. Ohio State University's "The Oval" campus (1914) famously incorporated student-created shortcuts into its final design. Academic research accelerated with Helbing et al.'s active walkers model (2001) and later agent-based approaches.
At H3 resolution 15 a single cell is only about 1 meter across — thinner than a real footway and narrow enough that adjacent path cells can become disconnected. To keep linear features (footways, paths, tracks) connected and realistically wide, they are rasterized as a corridor widened by a landcover-aware Gaussian blur (radius 1, σ=1.0) around each cell along the line. The blur accumulates weight from every corridor source within radius, producing a smooth falloff that yields a roughly 3-meter-wide path — equivalent to a single resolution-14 cell — and improves path connectivity. The BFS does not propagate through cells with friction ≥ dense vegetation (4.0), so paths respect landcover boundaries and do not force walkers through bush or keep-off terrain. The widening radius is derived from a target path width and the resolution's cell spacing, so it stays approximately constant across resolutions: radius 1 at res 15, and 0 at coarser resolutions where one cell is already wide enough.