---
id: wiki-2026-0508-pedestrian-modeling
title: Pedestrian Modeling
category: 10_Wiki/Topics
status: verified
canonical_id: self
aliases: [Crowd Simulation, Pedestrian Dynamics, Foot Traffic Modeling]
duplicate_of: none
source_trust_level: A
confidence_score: 0.9
verification_status: applied
tags: [simulation, agent-based, urban-planning, crowd-dynamics]
raw_sources: []
last_reinforced: 2026-05-10
github_commit: pending
tech_stack:
  language: Python
  framework: Mesa / SUMO / PyTorch
---

# Pedestrian Modeling

## 매 한 줄
> **"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.

## 매 핵심

### 매 모델 계열
- **Macroscopic**: fluid-like density/flow PDE — Hughes model, LWR for crowds. 매 large-scale aggregate.
- **Mesoscopic**: cellular automata (Burstedde 2001) — discrete grid + transition probabilities.
- **Microscopic**: 개별 agent — Social Force (Helbing-Molnár), ORCA (reciprocal velocity obstacles), RVO2.
- **Data-driven**: GNN trajectory prediction (Social-LSTM, Trajectron++, EqMotion 2025).

### 매 Social Force 핵심
- 매 보행자 i 의 motion equation: `m_i * dv_i/dt = F_desired + ΣF_social + ΣF_obstacle + ξ`.
- `F_desired = (v_target - v_i) / τ` — 매 goal-directed term.
- `F_social = A * exp((r_ij - d_ij)/B) * n_ij` — 매 repulsion exponential.

### 매 응용
1. Evacuation simulation (역, stadium, 고층빌딩).
2. Urban design (sidewalk width, crossing geometry, wayfinding).
3. Retail analytics (store layout heatmap).
4. Autonomous robot navigation in crowds.

## 💻 패턴

### Social Force Model (Mesa 3.x)
```python
import numpy as np
from mesa import Agent, Model
from mesa.space import ContinuousSpace

class Pedestrian(Agent):
    def __init__(self, model, pos, target, v_desired=1.34):
        super().__init__(model)
        self.pos = np.array(pos, dtype=float)
        self.vel = np.zeros(2)
        self.target = np.array(target, dtype=float)
        self.v_desired = v_desired
        self.tau = 0.5  # relaxation time
        self.radius = 0.3

    def desired_force(self):
        direction = self.target - self.pos
        norm = np.linalg.norm(direction) + 1e-9
        e = direction / norm
        return (self.v_desired * e - self.vel) / self.tau

    def social_force(self, A=2000, B=0.08):
        f = np.zeros(2)
        for other in self.model.agents:
            if other is self: continue
            r_ij = self.radius + other.radius
            d_vec = self.pos - other.pos
            d_ij = np.linalg.norm(d_vec) + 1e-9
            n_ij = d_vec / d_ij
            f += A * np.exp((r_ij - d_ij) / B) * n_ij
        return f

    def step(self):
        f = self.desired_force() + self.social_force()
        self.vel += f * self.model.dt
        speed = np.linalg.norm(self.vel)
        if speed > 1.5 * self.v_desired:
            self.vel *= 1.5 * self.v_desired / speed
        self.pos += self.vel * self.model.dt
```

### ORCA (RVO2) collision avoidance
```python
import rvo2

sim = rvo2.PyRVOSimulator(
    timeStep=1/30, neighborDist=2.0, maxNeighbors=10,
    timeHorizon=2.0, timeHorizonObst=2.0,
    radius=0.3, maxSpeed=1.5
)
agents = [sim.addAgent((x, y)) for x, y in spawn_positions]
for i, goal in enumerate(goals):
    pref = np.array(goal) - np.array(sim.getAgentPosition(i))
    pref = pref / (np.linalg.norm(pref) + 1e-9) * 1.34
    sim.setAgentPrefVelocity(i, tuple(pref))
sim.doStep()
```

### Trajectory prediction (PyTorch GNN, 2025-style)
```python
import torch, torch.nn as nn
from torch_geometric.nn import GATv2Conv

class TrajectronLite(nn.Module):
    def __init__(self, hidden=64, future=12):
        super().__init__()
        self.encoder = nn.GRU(4, hidden, batch_first=True)
        self.gat = GATv2Conv(hidden, hidden, heads=4, concat=False)
        self.decoder = nn.GRU(hidden, hidden, batch_first=True)
        self.head = nn.Linear(hidden, 2)
        self.future = future

    def forward(self, hist, edge_index):
        # hist: (N, T, 4) — (x, y, vx, vy)
        h, _ = self.encoder(hist)
        h = self.gat(h[:, -1], edge_index)
        h = h.unsqueeze(1).expand(-1, self.future, -1)
        out, _ = self.decoder(h)
        return self.head(out)  # (N, future, 2)
```

### SUMO foot traffic export
```python
import traci
traci.start(["sumo", "-c", "station.sumocfg"])
while traci.simulation.getMinExpectedNumber() > 0:
    traci.simulationStep()
    for pid in traci.person.getIDList():
        x, y = traci.person.getPosition(pid)
        v = traci.person.getSpeed(pid)
        log(pid, x, y, v)
traci.close()
```

### Density heatmap (post-hoc)
```python
import numpy as np, matplotlib.pyplot as plt
from scipy.stats import gaussian_kde

xy = trajectories[:, :, :2].reshape(-1, 2).T  # (2, N*T)
kde = gaussian_kde(xy, bw_method=0.15)
gx, gy = np.mgrid[0:50:200j, 0:50:200j]
density = kde(np.vstack([gx.ravel(), gy.ravel()])).reshape(gx.shape)
plt.imshow(density.T, origin="lower", extent=(0, 50, 0, 50), cmap="hot")
```

## 매 결정 기준
| 상황 | Approach |
|---|---|
| Evacuation + dense crowd | Social Force (Helbing) |
| Smooth navigation, robotics | ORCA / RVO2 |
| Trajectory prediction (ML) | Trajectron++ / EqMotion |
| Large-scale urban (10⁵+) | Macroscopic LWR / MATSim |
| Discrete cell-based grid | Cellular Automata (Burstedde) |

**기본값**: Mesa 3.x + Social Force for simulation, RVO2 for robot integration.

## 🔗 Graph
- 부모: [[Complex Systems]]
- 변형: [[Cellular Automata]]

## 🤖 LLM 활용
**언제**: parameter sweep design, scenario script generation, calibration code 작성.
**언제 X**: real-time inner-loop force computation — 매 numerical kernel은 Cython/Numba/CUDA.

## ❌ 안티패턴
- **Pseudo-uniform spawning**: 매 corner clustering 발생 — Poisson disk sampling 사용.
- **Naive O(N²) social force**: 매 KD-tree neighborhood 로 cutoff (보통 5m).
- **Validation skip**: 매 fundamental diagram (density vs flow) 와 비교 필수.
- **dt too large**: 매 0.05–0.1s 권장; 매 그 이상 instability.

## 🧪 검증 / 중복
- Verified (Helbing & Molnár 1995, Helbing et al. 2000 Nature, Mesa docs 3.x, RVO2 reference).
- 신뢰도 A.

## 🕓 Changelog
| 날짜 | 변경 |
|---|---|
| 2026-05-08 | Phase 1 |
| 2026-05-10 | Manual cleanup — Social Force / ORCA / GNN trajectory FULL content |
