Asking the fleet what it is doing…
monad-knowledge Wi-Fi sensing lab · FIIT STU
CRD503 active 2 / 12 weeks

Crowd Dynamics & Pedestrian Simulation

A twelve-week track on how crowds move and how we measure that movement — microscopic models, the fundamental diagram, and the JuPedSim/PedPy toolchain on the lab's own simulation platform. The throughline: a simulation is only as good as its validation against reality.

The 12-week arc

2 of 12 lectures published — each a self-contained read with a slide deck and runnable workbook. Start at the first published week and work forward.

Wk Theme & focus Read
01 Why model crowds start here Safety, design, and sensing ground truth — what models are *for* read → slides · workbook
02 The fundamental diagram Density–flow–speed: the field's core empirical law, read from data read → slides · workbook
03 Microscopic models I: social force Pedestrians as particles under social and physical forces planned
04 Microscopic models II Velocity-based models & cellular automata — trade-offs vs social force. Teach against the lab engine's reality: the agenda/from_floor path runs base CFSM (AVM only on directed_flow); parameters are wired and literature-grounded (radius 0.20 m Maury–Venel contact / Seyfried FD intercept, v0 1.34 m/s Weidmann, T 1.0 s = JuPedSim default ≈ 2× Seyfried b≈0.56 s = low-motivation regime, defensible but not "Seyfried-grounded"); single-v0 crowds symmetry-lock — per-agent draws are a modelling necessity planned
05 JuPedSim scenarios Building and running a scenario on the simulation platform planned
06 PedPy measurement Extracting density, speed, and flow from trajectories planned
07 Voronoi density & the method trap How the measurement method changes the density you report planned
08 Bottlenecks & the multi-room collapse Doorways, capacity, and the onset of congestion planned
09 Evacuation dynamics Egress time, exit choice, and where simple models fail planned
10 Simulation as synthetic ground truth Coupling sims to sensing experiments for labelled data planned
11 Validation against real trajectories Does the simulation match reality, and how would you know? planned
12 Capstone A validated evacuation study with PedPy metrics planned

Capstone

Week 12: design an evacuation scenario, simulate it, measure it with PedPy, and validate the result against a real or reference trajectory dataset — reporting where simulation and reality diverge.