# Understanding People Flow in Transportation Hubs

## TL;DR

This paper introduces an unsupervised method using 3D depth images to analyze and cluster people flow patterns in transportation hubs, effectively identifying typical and rare activity configurations for operational insights.

## Contribution

The paper presents a novel unsupervised clustering approach with a data-irregularity measure to identify meaningful people flow patterns from 3D images, outperforming standard methods.

## Key findings

- Successfully summarized 14 days of airport flow data
- Identified both common and rare activity patterns
- Provided actionable insights for airport management

## Abstract

In this paper, we aim to monitor the flow of people in large public infrastructures. We propose an unsupervised methodology to cluster people flow patterns into the most typical and meaningful configurations. By processing 3D images from a network of depth cameras, we build a descriptor for the flow pattern. We define a data-irregularity measure that assesses how well each descriptor fits a data model. This allows us to rank flow patterns from highly distinctive (outliers) to very common ones. By discarding outliers, we obtain more reliable key configurations (classes). Synthetic experiments show that the proposed method is superior to standard clustering methods. We applied it in an operational scenario during 14 days in the X-ray screening area of an international airport. Results show that our methodology is able to successfully summarize the representative patterns for such a long observation period, providing relevant information for airport management. Beyond regular flows, our method identifies a set of rare events corresponding to uncommon activities (cleaning, special security and circulating staff).