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

Samuli Hemminki, Keisuke KURIBAYASHI, Shin'Ichi Konomi, Petteri Nurmi, Sasu Tarkoma

Tutkimustuotos: ArtikkelijulkaisuArtikkeliTieteellinenvertaisarvioitu

Kuvaus

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.
Alkuperäiskielienglanti
Artikkeli15
LehtiACM transactions on spatial algorithms and systems
Vuosikerta5
Numero3
Sivumäärä34
ISSN2374-0353
DOI - pysyväislinkit
TilaJulkaistu - elokuuta 2019
OKM-julkaisutyyppiA1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä, vertaisarvioitu

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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.",
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author = "Samuli Hemminki and Keisuke KURIBAYASHI and Shin'Ichi Konomi and Petteri Nurmi and Sasu Tarkoma",
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Crowd Replication : Sensing-Assisted Quantification of Human Behaviour in Public Spaces. / Hemminki, Samuli; KURIBAYASHI, Keisuke; Konomi, Shin'Ichi; Nurmi, Petteri ; Tarkoma, Sasu .

julkaisussa: ACM transactions on spatial algorithms and systems, Vuosikerta 5, Nro 3, 15, 08.2019.

Tutkimustuotos: ArtikkelijulkaisuArtikkeliTieteellinenvertaisarvioitu

TY - JOUR

T1 - Crowd Replication

T2 - Sensing-Assisted Quantification of Human Behaviour in Public Spaces

AU - Hemminki, Samuli

AU - KURIBAYASHI, Keisuke

AU - Konomi, Shin'Ichi

AU - Nurmi, Petteri

AU - Tarkoma, Sasu

PY - 2019/8

Y1 - 2019/8

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.

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DO - 10.1145/3317666

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VL - 5

JO - ACM transactions on spatial algorithms and systems

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