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

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Crowd Replication: Sensing-Assisted Quantification of Human Behaviour in Public Spaces. / Hemminki, Samuli; Kuribayashi, Keisuke; Konomi, Shin'ichi et al.
In: ACM Transactions on Spatial Algorithms and Systems (TSAS), Vol. 5, No. 3, 15, 01.08.2019.

Research output: Contribution to Journal/MagazineJournal articlepeer-review

Harvard

Hemminki, S, Kuribayashi, K, Konomi, S, Nurmi, PT & Tarkoma, S 2019, 'Crowd Replication: Sensing-Assisted Quantification of Human Behaviour in Public Spaces', ACM Transactions on Spatial Algorithms and Systems (TSAS), vol. 5, no. 3, 15. https://doi.org/10.1145/3317666

APA

Hemminki, S., Kuribayashi, K., Konomi, S., Nurmi, P. T., & Tarkoma, S. (2019). Crowd Replication: Sensing-Assisted Quantification of Human Behaviour in Public Spaces. ACM Transactions on Spatial Algorithms and Systems (TSAS), 5(3), Article 15. https://doi.org/10.1145/3317666

Vancouver

Hemminki S, Kuribayashi K, Konomi S, Nurmi PT, Tarkoma S. Crowd Replication: Sensing-Assisted Quantification of Human Behaviour in Public Spaces. ACM Transactions on Spatial Algorithms and Systems (TSAS). 2019 Aug 1;5(3):15. doi: 10.1145/3317666

Author

Hemminki, Samuli ; Kuribayashi, Keisuke ; Konomi, Shin'ichi et al. / Crowd Replication : Sensing-Assisted Quantification of Human Behaviour in Public Spaces. In: ACM Transactions on Spatial Algorithms and Systems (TSAS). 2019 ; Vol. 5, No. 3.

Bibtex

@article{9ff29ecd820e4530b8263168f1a764d8,
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 article, as our first contribution, we develop crowd replication as a novel sensor-assisted method for quantifying human behavior within public spaces. In crowd replication, a researcher is tasked with recording the behavior 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 modeling, behavioral 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 behaviors and activities of people within the space. We also validate our pedestrian modeling approach through extensive benchmarks, demonstrating that 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 = "Samuli Hemminki and Keisuke Kuribayashi and Shin'ichi Konomi and Nurmi, {Petteri Tapio} and Sasu Tarkoma",
note = "{\textcopyright} ACM, YYYY. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in PUBLICATION, {VOL#, ISS#, (DATE)} http://doi.acm.org/10.1145/nnnnnn.nnnnnn ",
year = "2019",
month = aug,
day = "1",
doi = "10.1145/3317666",
language = "English",
volume = "5",
journal = "ACM Transactions on Spatial Algorithms and Systems (TSAS)",
publisher = "ACM",
number = "3",

}

RIS

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 Tapio

AU - Tarkoma, Sasu

N1 - © ACM, YYYY. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in PUBLICATION, {VOL#, ISS#, (DATE)} http://doi.acm.org/10.1145/nnnnnn.nnnnnn

PY - 2019/8/1

Y1 - 2019/8/1

N2 - A central challenge for public space design is to evaluate whether a given space promotes different types of activities. In this article, as our first contribution, we develop crowd replication as a novel sensor-assisted method for quantifying human behavior within public spaces. In crowd replication, a researcher is tasked with recording the behavior 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 modeling, behavioral 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 behaviors and activities of people within the space. We also validate our pedestrian modeling approach through extensive benchmarks, demonstrating that 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 article, as our first contribution, we develop crowd replication as a novel sensor-assisted method for quantifying human behavior within public spaces. In crowd replication, a researcher is tasked with recording the behavior 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 modeling, behavioral 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 behaviors and activities of people within the space. We also validate our pedestrian modeling approach through extensive benchmarks, demonstrating that 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.

U2 - 10.1145/3317666

DO - 10.1145/3317666

M3 - Journal article

VL - 5

JO - ACM Transactions on Spatial Algorithms and Systems (TSAS)

JF - ACM Transactions on Spatial Algorithms and Systems (TSAS)

IS - 3

M1 - 15

ER -