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Antigravity Agent
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id: wiki-2026-0508-pedestrian-modeling
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title: Pedestrian Modeling
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category: 10_Wiki/Topics
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status: verified
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canonical_id: self
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aliases: [Crowd Simulation, Pedestrian Dynamics, Foot Traffic Modeling]
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duplicate_of: none
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source_trust_level: A
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confidence_score: 0.9
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verification_status: applied
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tags: [simulation, agent-based, urban-planning, crowd-dynamics]
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raw_sources: []
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last_reinforced: 2026-05-10
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github_commit: pending
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tech_stack:
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language: Python
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framework: Mesa / SUMO / PyTorch
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---
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# Pedestrian Modeling
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## 매 한 줄
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> **"매 보행자는 force field 위의 agent"**. 1995년 Helbing의 Social Force Model이 분야를 정의했고, 2026 현재 ML-augmented agent-based simulation (Mesa 3.x, MATSim, SUMO)이 evacuation planning, station design, retail flow 분석의 표준 도구다.
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## 매 핵심
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### 매 모델 계열
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- **Macroscopic**: fluid-like density/flow PDE — Hughes model, LWR for crowds. 매 large-scale aggregate.
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- **Mesoscopic**: cellular automata (Burstedde 2001) — discrete grid + transition probabilities.
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- **Microscopic**: 개별 agent — Social Force (Helbing-Molnár), ORCA (reciprocal velocity obstacles), RVO2.
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- **Data-driven**: GNN trajectory prediction (Social-LSTM, Trajectron++, EqMotion 2025).
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### 매 Social Force 핵심
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- 매 보행자 i 의 motion equation: `m_i * dv_i/dt = F_desired + ΣF_social + ΣF_obstacle + ξ`.
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- `F_desired = (v_target - v_i) / τ` — 매 goal-directed term.
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- `F_social = A * exp((r_ij - d_ij)/B) * n_ij` — 매 repulsion exponential.
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### 매 응용
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1. Evacuation simulation (역, stadium, 고층빌딩).
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2. Urban design (sidewalk width, crossing geometry, wayfinding).
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3. Retail analytics (store layout heatmap).
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4. Autonomous robot navigation in crowds.
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## 💻 패턴
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### Social Force Model (Mesa 3.x)
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```python
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import numpy as np
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from mesa import Agent, Model
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from mesa.space import ContinuousSpace
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class Pedestrian(Agent):
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def __init__(self, model, pos, target, v_desired=1.34):
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super().__init__(model)
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self.pos = np.array(pos, dtype=float)
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self.vel = np.zeros(2)
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self.target = np.array(target, dtype=float)
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self.v_desired = v_desired
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self.tau = 0.5 # relaxation time
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self.radius = 0.3
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def desired_force(self):
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direction = self.target - self.pos
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norm = np.linalg.norm(direction) + 1e-9
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e = direction / norm
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return (self.v_desired * e - self.vel) / self.tau
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def social_force(self, A=2000, B=0.08):
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f = np.zeros(2)
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for other in self.model.agents:
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if other is self: continue
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r_ij = self.radius + other.radius
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d_vec = self.pos - other.pos
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d_ij = np.linalg.norm(d_vec) + 1e-9
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n_ij = d_vec / d_ij
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f += A * np.exp((r_ij - d_ij) / B) * n_ij
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return f
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def step(self):
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f = self.desired_force() + self.social_force()
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self.vel += f * self.model.dt
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speed = np.linalg.norm(self.vel)
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if speed > 1.5 * self.v_desired:
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self.vel *= 1.5 * self.v_desired / speed
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self.pos += self.vel * self.model.dt
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```
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### ORCA (RVO2) collision avoidance
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```python
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import rvo2
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sim = rvo2.PyRVOSimulator(
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timeStep=1/30, neighborDist=2.0, maxNeighbors=10,
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timeHorizon=2.0, timeHorizonObst=2.0,
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radius=0.3, maxSpeed=1.5
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)
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agents = [sim.addAgent((x, y)) for x, y in spawn_positions]
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for i, goal in enumerate(goals):
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pref = np.array(goal) - np.array(sim.getAgentPosition(i))
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pref = pref / (np.linalg.norm(pref) + 1e-9) * 1.34
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sim.setAgentPrefVelocity(i, tuple(pref))
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sim.doStep()
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```
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### Trajectory prediction (PyTorch GNN, 2025-style)
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```python
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import torch, torch.nn as nn
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from torch_geometric.nn import GATv2Conv
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class TrajectronLite(nn.Module):
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def __init__(self, hidden=64, future=12):
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super().__init__()
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self.encoder = nn.GRU(4, hidden, batch_first=True)
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self.gat = GATv2Conv(hidden, hidden, heads=4, concat=False)
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self.decoder = nn.GRU(hidden, hidden, batch_first=True)
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self.head = nn.Linear(hidden, 2)
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self.future = future
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def forward(self, hist, edge_index):
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# hist: (N, T, 4) — (x, y, vx, vy)
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h, _ = self.encoder(hist)
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h = self.gat(h[:, -1], edge_index)
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h = h.unsqueeze(1).expand(-1, self.future, -1)
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out, _ = self.decoder(h)
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return self.head(out) # (N, future, 2)
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```
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### SUMO foot traffic export
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```python
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import traci
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traci.start(["sumo", "-c", "station.sumocfg"])
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while traci.simulation.getMinExpectedNumber() > 0:
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traci.simulationStep()
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for pid in traci.person.getIDList():
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x, y = traci.person.getPosition(pid)
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v = traci.person.getSpeed(pid)
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log(pid, x, y, v)
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traci.close()
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```
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### Density heatmap (post-hoc)
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```python
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import numpy as np, matplotlib.pyplot as plt
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from scipy.stats import gaussian_kde
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xy = trajectories[:, :, :2].reshape(-1, 2).T # (2, N*T)
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kde = gaussian_kde(xy, bw_method=0.15)
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gx, gy = np.mgrid[0:50:200j, 0:50:200j]
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density = kde(np.vstack([gx.ravel(), gy.ravel()])).reshape(gx.shape)
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plt.imshow(density.T, origin="lower", extent=(0, 50, 0, 50), cmap="hot")
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```
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## 매 결정 기준
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| 상황 | Approach |
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| Evacuation + dense crowd | Social Force (Helbing) |
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| Smooth navigation, robotics | ORCA / RVO2 |
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| Trajectory prediction (ML) | Trajectron++ / EqMotion |
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| Large-scale urban (10⁵+) | Macroscopic LWR / MATSim |
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| Discrete cell-based grid | Cellular Automata (Burstedde) |
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**기본값**: Mesa 3.x + Social Force for simulation, RVO2 for robot integration.
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## 🔗 Graph
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- 부모: [[Complex Systems]]
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- 변형: [[Cellular Automata]]
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## 🤖 LLM 활용
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**언제**: parameter sweep design, scenario script generation, calibration code 작성.
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**언제 X**: real-time inner-loop force computation — 매 numerical kernel은 Cython/Numba/CUDA.
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## ❌ 안티패턴
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- **Pseudo-uniform spawning**: 매 corner clustering 발생 — Poisson disk sampling 사용.
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- **Naive O(N²) social force**: 매 KD-tree neighborhood 로 cutoff (보통 5m).
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- **Validation skip**: 매 fundamental diagram (density vs flow) 와 비교 필수.
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- **dt too large**: 매 0.05–0.1s 권장; 매 그 이상 instability.
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## 🧪 검증 / 중복
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- Verified (Helbing & Molnár 1995, Helbing et al. 2000 Nature, Mesa docs 3.x, RVO2 reference).
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- 신뢰도 A.
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## 🕓 Changelog
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| 날짜 | 변경 |
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| 2026-05-08 | Phase 1 |
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| 2026-05-10 | Manual cleanup — Social Force / ORCA / GNN trajectory FULL content |
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