Stigmergy-Based Modeling to Discover Urban Activity Patterns from Positioning Data
Name
1704.03667.pdf
Description
Submitted version
Size
2.36 MB
Format
Unknown
Checksum (MD5)
7c61281951fc6ee3b975fdebbfcc4a46
Author(s)
Alfeo, Antonio Luca
Cimino, Mario Giovanni C. A.
Egidi, Sara
Lepri, Bruno
Pentland, Alexander Sandy
Vaglini, Gigliola
Date Issued
2017
Publisher
Springer International Publishing
Citation
Alfeo, Antonio Luca, Cimino, Mario Giovanni C. A., Egidi, Sara, Lepri, Bruno, Pentland, Alex et al. 2017. "Stigmergy-Based Modeling to Discover Urban Activity Patterns from Positioning Data."
Version
Original manuscript
Abstract
© Springer International Publishing AG 2017. Positioning data offer a remarkable source of information to analyze crowds urban dynamics. However, discovering urban activity patterns from the emergent behavior of crowds involves complex system modeling. An alternative approach is to adopt computational techniques belonging to the emergent paradigm, which enables self-organization of data and allows adaptive analysis. Specifically, our approach is based on stigmergy. By using stigmergy each sample position is associated with a digital pheromone deposit, which progressively evaporates and aggregates with other deposits according to their spatiotemporal proximity. Based on this principle, we exploit positioning data to identify high density areas (hotspots) and characterize their activity over time. This characterization allows the comparison of dynamics occurring in different days, providing a similarity measure exploitable by clustering techniques. Thus, we cluster days according to their activity behavior, discovering unexpected urban activity patterns. As a case study, we analyze taxi traces in New York City during 2015.
MIT Department
Massachusetts Institute of Technology. Media Laboratory
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1007/978-3-319-60240-0_35
