Active and reactive behaviour in human mobility: the influence of attraction points on pedestrians

TL;DR

This study models human pedestrian movement at a public fair using Langevin Dynamics, distinguishing reactive attraction-driven behavior from active, intrinsic movement patterns to better understand and predict crowd distribution.

Contribution

It introduces a scalable modeling framework that separates reactive and active components of human mobility using experimental data and Langevin Dynamics.

Findings

Reactive attraction points significantly influence pedestrian paths.

Active movement patterns like persistence and memory also shape mobility.

The model can predict spatial distribution of pedestrians in public spaces.

Abstract

Human mobility is becoming an accessible field of study thanks to the progress and availability of tracking technologies as a common feature of smart phones. We describe an example of a scalable experiment exploiting these circumstances at a public, outdoor fair in Barcelona (Spain). Participants were tracked while wandering through an open space with activity stands attracting their attention. We develop a general modeling framework based on Langevin Dynamics, which allows us to test the influence of two distinct types of ingredients on mobility: reactive or context-dependent factors, modelled by means of a force field generated by attraction points in a given spatial configuration, and active or inherent factors, modelled from intrinsic movement patterns of the subjects. The additive and constructive framework model accounts for the observed features. Starting with the simplest model (purely random walkers) as a reference, we progressively introduce different ingredients such as persistence, memory, and perceptual landscape, aiming to untangle active and reactive contributions and quantify their respective relevance. The proposed approach may help in anticipating the spatial distribution of citizens in alternative scenarios and in improving the design of public events based on a facts-based approach.