Crowd replication: Sensing-Assisted quantification of human behavior in public spaces

Samuli Hemminki, Keisuke Kuribayashi, Shin'ichi Konomi, Petteri Nurmi, Sasu Tarkoma

Research output: Contribution to journalArticle

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.

Original languageEnglish
Article numbera15
JournalACM Transactions on Spatial Algorithms and Systems
Volume5
Issue number3
DOIs
Publication statusPublished - Aug 1 2019

Fingerprint

Human Behavior
Quantification
Replication
Sensing
Trajectories
Sensors
Mobile devices
Trajectory
Sensor
Trace
Social Interaction
Mobile Devices
Mathematical Modeling
Mirror
High Accuracy
Benchmark
Robustness
Target
Evaluate
Modeling

All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Information Systems
  • Modelling and Simulation
  • Computer Science Applications
  • Geometry and Topology
  • Discrete Mathematics and Combinatorics

Cite this

Crowd replication : Sensing-Assisted quantification of human behavior in public spaces. / Hemminki, Samuli; Kuribayashi, Keisuke; Konomi, Shin'ichi; Nurmi, Petteri; Tarkoma, Sasu.

In: ACM Transactions on Spatial Algorithms and Systems, Vol. 5, No. 3, a15, 01.08.2019.

Research output: Contribution to journalArticle

Hemminki, Samuli ; Kuribayashi, Keisuke ; Konomi, Shin'ichi ; Nurmi, Petteri ; Tarkoma, Sasu. / Crowd replication : Sensing-Assisted quantification of human behavior in public spaces. In: ACM Transactions on Spatial Algorithms and Systems. 2019 ; Vol. 5, No. 3.
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