Abstract
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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Sagl, G., Loidl, M., Beinat, E.: A visual analytics approach for extracting spatio-temporal urban mobility information from mobile network traffic. ISPRS Int. J. Geo-Inf. 1(3), 256–271 (2012)
Veloso, M., Phithakkitnukoon, S., Bento, C.: Urban mobility study using taxi traces. In: Proceedings of the 2011 International Workshop on Trajectory Data Mining and Analysis, pp. 23–30. ACM (2011)
Mazimpaka, J.D., Timpf, S.: Trajectory data mining: a review of methods and applications. J. Spat. Inf. Sci. 2016(13), 61–9 (2016)
Zhang, W., Qi, G., Pan, G., Lu, H., Li, S., Wu, Z.: City-scale social event detection and evaluation with taxi traces. ACM Trans. Intell. Syst. Technol. 6(3), 40 (2015)
Pan, B., Zheng, Y., Wilkie, D., Shahabi, C.: Crowd sensing of traffic anomalies based on human mobility and social media. In: Proceedings of the 21st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, pp. 344–353. ACM (2013)
Kuang, W., An, S., Jiang, H.: Detecting traffic anomalies in urban areas using taxi GPS data. Math. Prob. Eng. 2015, Article ID 809582, 1–13 (2015). doi:10.1155/2015/809582
Hu, Y., Miller, H.J., Li, X.: Detecting and analyzing mobility hotspots using surface networks. Trans. GIS 18(6), 911–935 (2014)
Lu, Y.: An intelligent system for taxi service monitoring, analytics and visualization. In: Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence (2016)
Keler, A., Krisp, J.M.: Is there a relationship between complicated crossings and frequently visited locations? A case study with boro taxis and OSM in NYC. In: 13th International Conference on Location-Based Services (2016)
Li, X., Pan, G., Wu, Z., Qi, G., Li, S., Zhang, D., Zhang, W., Wang, Z.: Prediction of urban human mobility using large-scale taxi traces and its applications. Front. Comput. Sci. 6(1), 111–121 (2012)
Castro, P.S., Zhang, D., Chen, C., Li, S., Pan, G.: From taxi GPS traces to social and community dynamics: a survey. ACM Comput. Surv. 46(2), 17 (2013)
Marsh, L., Onof, C.: Stigmergic epistemology, stigmergic cognition. Cogn. Syst. Res. 9(1–2), 136–149 (2008)
Vernon, D., Metta, G., Sandini, G.: A survey of artificial cognitive systems: implications for the autonomous development of mental capabilities in computational agents. IEEE Trans. Evol. Comput. 11(2), 151–180 (2007)
Barsocchi, P., Cimino, M.G.C.A., Ferro, E., Lazzeri, A., Palumbo, F., Vaglini, G.: Monitoring elderly behavior via indoor position-based stigmergy. Pervasive Mob. Comput. 23, 26–42 (2015). Elsevier Science
Niwattanakul, S., Singthongchai, J., Naenudorn, E., Wanapu, S.: Using of Jaccard coefficient for keywords similarity. In: Proceedings of the International Multi Conference of Engineers and Computer Scientists, vol. 1, p. 6 (2013)
NYC.gov: Taxi and Limousine Commission (TLC) Trip Record Data. http://www.nyc.gov/html/tlc/html/about/trip_record_data.shtml
Zola: interactive map for zone and land use of NYC. http://maps.nyc.gov/doitt/nycitymap/template?applicationName=ZOLA
Weather NYC: Thousands of transatlantic travellers face serious disruption caused by New York winter storm ‘Juno’. The Independent, 26 January 2015
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Alfeo, A.L., Cimino, M.G.C.A., Egidi, S., Lepri, B., Pentland, A., Vaglini, G. (2017). Stigmergy-Based Modeling to Discover Urban Activity Patterns from Positioning Data. In: Lee, D., Lin, YR., Osgood, N., Thomson, R. (eds) Social, Cultural, and Behavioral Modeling. SBP-BRiMS 2017. Lecture Notes in Computer Science(), vol 10354. Springer, Cham. https://doi.org/10.1007/978-3-319-60240-0_35
Download citation
DOI: https://doi.org/10.1007/978-3-319-60240-0_35
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-60239-4
Online ISBN: 978-3-319-60240-0
eBook Packages: Computer ScienceComputer Science (R0)