Description

Voronoi tessellation partitions space into cells, one per pedestrian, where each cell is the set of points closest to its agent. The reciprocal of cell area gives a per-agent density estimate that is far less biased than fixed-window kernel estimators, especially in heterogeneous crowds. Voronoi-based density is the standard way to extract a fundamental-diagram from microscopic trajectories and crops up in 3D variants for stairs and multi-level venues.

When it's used

  • Local density estimation around each pedestrian
  • Empirical fundamental-diagram extraction from BLE trajectories
  • Spatial partitioning for cell-level CSI feature aggregation
  • Anisotropy analysis in crowd dynamics

Limitations

  • Boundary cells become unbounded — need clipping
  • Sensitive to localisation noise; small position errors alter cell topology
  • 3D extensions are computationally heavier and rarely needed indoors

Source Papers

  • bendalibraham2021_476e — Voronoi-density crowd analysis
  • duives2013_3924 — Voronoi diagrams in crowd analytics
  • di2023_285b — Voronoi cells alongside continuum modelling

7 vault papers use this method

Titles and DOIs only — no abstracts, no analyses.

  • Microscopic insights into pedestrian motion through a bottleneck, resolving spatial and temporal variations 2022 DOI ↗
  • Microscopic insights into pedestrian motion through a bottleneck, resolving spatial and temporal variations 2022 DOI ↗
  • 3D Indoor Environment Abstraction for Crowd Simulations in Complex Buildings 2021 DOI ↗
  • A Local 3D Voronoi-Based Optimization Method for Sensor Network Deployment in Complex Indoor Environments 2021 DOI ↗
  • Data Science and Machine Learning: Mathematical and Statistical Methods 2019
  • Data Science and Machine Learning: Mathematical and Statistical Methods 2019
  • Handbook of Data Visualization 2008 DOI ↗