Crowd Replication: Sensing-Assisted Quantification of Human Behaviour in Public Spaces

Research output: Contribution to journalArticleScientificpeer-review

Abstract

A central challenge for public space design is to evaluate whether a given space promotes different types of activities. In this paper, as our first contribution, we develop crowd replication as a novel sensor-assisted method for quantifying human behaviour within public spaces. In crowd replication, a researcher is tasked with recording the behaviour of people using a space while being instrumented with a mobile device that captures a sensor trace of the replicated movements and activities. Through mathematical modelling, behavioural indicators extracted from the replicated trajectories can be extrapolated to represent a larger target population. As our second contribution, we develop a novel highly accurate pedestrian sensing solution for reconstructing movement trajectories from sensor traces captured during the replication process. Our key insight is to tailor sensing to characteristics of the researcher performing replication, which allows reconstruction to operate robustly against variations in pace and other walking characteristics. We validate crowd replication through a case study carried out within a representative example of a metropolitan-scale public space. Our results show that crowd-replicated data closely mirrors human dynamics in public spaces, and reduces overall data collection effort while producing high quality indicators about behaviours and activities of people within the space. We also validate our pedestrian modelling approach through extensive benchmarks, demonstrating our approach can reconstruct movement trajectories with high accuracy and robustness (median error below 1%). Finally, we demonstrate that our contributions enable capturing detailed indicators of liveliness, extent of social interaction, and other factors indicative of public space quality.
Original languageFinnish
JournalACM transactions on spatial algorithms and systems
Publication statusAccepted/In press - 2019
MoE publication typeA1 Journal article-refereed

Cite this

@article{54cf33b6f084411eac95b25a7702eeed,
title = "Crowd Replication: Sensing-Assisted Quantification of Human Behaviour in Public Spaces",
abstract = "A central challenge for public space design is to evaluate whether a given space promotes different types of activities. In this paper, as our first contribution, we develop crowd replication as a novel sensor-assisted method for quantifying human behaviour within public spaces. In crowd replication, a researcher is tasked with recording the behaviour of people using a space while being instrumented with a mobile device that captures a sensor trace of the replicated movements and activities. Through mathematical modelling, behavioural indicators extracted from the replicated trajectories can be extrapolated to represent a larger target population. As our second contribution, we develop a novel highly accurate pedestrian sensing solution for reconstructing movement trajectories from sensor traces captured during the replication process. Our key insight is to tailor sensing to characteristics of the researcher performing replication, which allows reconstruction to operate robustly against variations in pace and other walking characteristics. We validate crowd replication through a case study carried out within a representative example of a metropolitan-scale public space. Our results show that crowd-replicated data closely mirrors human dynamics in public spaces, and reduces overall data collection effort while producing high quality indicators about behaviours and activities of people within the space. We also validate our pedestrian modelling approach through extensive benchmarks, demonstrating our approach can reconstruct movement trajectories with high accuracy and robustness (median error below 1{\%}). Finally, we demonstrate that our contributions enable capturing detailed indicators of liveliness, extent of social interaction, and other factors indicative of public space quality.",
author = "Hemminki, {Jaakko Jukka Samuli} and Nurmi, {Petteri Tapio} and Tarkoma, {Sasu Arimo Olavi} and Shin'Ichi Konomi and Keisuke KURIBAYASHI",
year = "2019",
language = "suomi",
journal = "ACM transactions on spatial algorithms and systems",
issn = "2374-0353",
publisher = "ACM",

}

TY - JOUR

T1 - Crowd Replication: Sensing-Assisted Quantification of Human Behaviour in Public Spaces

AU - Hemminki, Jaakko Jukka Samuli

AU - Nurmi, Petteri Tapio

AU - Tarkoma, Sasu Arimo Olavi

AU - Konomi, Shin'Ichi

AU - KURIBAYASHI, Keisuke

PY - 2019

Y1 - 2019

N2 - A central challenge for public space design is to evaluate whether a given space promotes different types of activities. In this paper, as our first contribution, we develop crowd replication as a novel sensor-assisted method for quantifying human behaviour within public spaces. In crowd replication, a researcher is tasked with recording the behaviour of people using a space while being instrumented with a mobile device that captures a sensor trace of the replicated movements and activities. Through mathematical modelling, behavioural indicators extracted from the replicated trajectories can be extrapolated to represent a larger target population. As our second contribution, we develop a novel highly accurate pedestrian sensing solution for reconstructing movement trajectories from sensor traces captured during the replication process. Our key insight is to tailor sensing to characteristics of the researcher performing replication, which allows reconstruction to operate robustly against variations in pace and other walking characteristics. We validate crowd replication through a case study carried out within a representative example of a metropolitan-scale public space. Our results show that crowd-replicated data closely mirrors human dynamics in public spaces, and reduces overall data collection effort while producing high quality indicators about behaviours and activities of people within the space. We also validate our pedestrian modelling approach through extensive benchmarks, demonstrating our approach can reconstruct movement trajectories with high accuracy and robustness (median error below 1%). Finally, we demonstrate that our contributions enable capturing detailed indicators of liveliness, extent of social interaction, and other factors indicative of public space quality.

AB - A central challenge for public space design is to evaluate whether a given space promotes different types of activities. In this paper, as our first contribution, we develop crowd replication as a novel sensor-assisted method for quantifying human behaviour within public spaces. In crowd replication, a researcher is tasked with recording the behaviour of people using a space while being instrumented with a mobile device that captures a sensor trace of the replicated movements and activities. Through mathematical modelling, behavioural indicators extracted from the replicated trajectories can be extrapolated to represent a larger target population. As our second contribution, we develop a novel highly accurate pedestrian sensing solution for reconstructing movement trajectories from sensor traces captured during the replication process. Our key insight is to tailor sensing to characteristics of the researcher performing replication, which allows reconstruction to operate robustly against variations in pace and other walking characteristics. We validate crowd replication through a case study carried out within a representative example of a metropolitan-scale public space. Our results show that crowd-replicated data closely mirrors human dynamics in public spaces, and reduces overall data collection effort while producing high quality indicators about behaviours and activities of people within the space. We also validate our pedestrian modelling approach through extensive benchmarks, demonstrating our approach can reconstruct movement trajectories with high accuracy and robustness (median error below 1%). Finally, we demonstrate that our contributions enable capturing detailed indicators of liveliness, extent of social interaction, and other factors indicative of public space quality.

M3 - Artikkeli

JO - ACM transactions on spatial algorithms and systems

JF - ACM transactions on spatial algorithms and systems

SN - 2374-0353

ER -