Classifying pedestrian crossing flows: A data-driven approach using fundamental diagrams and machine learning

TL;DR

This paper presents a data-driven approach combining fundamental diagrams and machine learning to classify pedestrian crossing scenarios, aiding crowd management and safety planning.

Contribution

It introduces a novel machine learning framework utilizing multiple pedestrian behavior features for accurate scenario classification.

Findings

Machine learning models achieved high classification accuracy.

Velocity and avoidance number are key features for scenario differentiation.

Fundamental diagrams help understand pedestrian flow dynamics.

Abstract

This study investigates the dynamics of pedestrian crossing flows with varying crossing angles $\alpha$ to classify different scenarios and derive implications for crowd management. Probability density functions of four key features$-$velocity $v$, density $\rho$, avoidance number $Av$, and intrusion number $In$$-$were analyzed to characterize pedestrian behavior. Velocity-density fundamental diagrams were constructed for each $\alpha$ and fitted with functional forms from existing literature. Classification attempts using $Av$-$In$ and $v$-$\rho$ phase spaces revealed significant overlaps, highlighting the limitations of these metrics alone for scenario differentiation. To address this, machine learning models, including logistic regression and random forest, were employed using all four features. Results showed robust classification performance, with $v$ and $Av$ contributing most significantly. Insights from feature importance metrics and classification accuracy offer practical guidance for managing high-density crowds, optimizing pedestrian flow, and designing safer public spaces. These findings provide a data-driven framework for advancing pedestrian dynamics research.