Document Open Access Logo

Dynamic Purpose Decomposition of Mobility Flows Based on Geographical Data

Authors Etienne Thuillier, Laurent Moalic, Alexandre Caminada

Thumbnail PDF


  • Filesize: 1.07 MB
  • 14 pages

Document Identifiers

Author Details

Etienne Thuillier
Laurent Moalic
Alexandre Caminada

Cite AsGet BibTex

Etienne Thuillier, Laurent Moalic, and Alexandre Caminada. Dynamic Purpose Decomposition of Mobility Flows Based on Geographical Data. In 24th International Symposium on Temporal Representation and Reasoning (TIME 2017). Leibniz International Proceedings in Informatics (LIPIcs), Volume 90, pp. 20:1-20:14, Schloss Dagstuhl - Leibniz-Zentrum für Informatik (2017)


Spatial and temporal decomposition of aggregated mobility flows is nowadays a commonly addressed issue, but a trip-purpose decomposition of mobility flows is a more challenging topic, which requires more sensitive analysis such as heterogeneous data fusion. In this paper, we study the relation between land use and mobility purposes. We propose a model that dynamically decomposes mobility flows into six mobility purposes. To this end, we use a national transportation database that surveyed more than 35,000 individuals and a national ground description database that identifies six distinct ground types. Based on these two types of data, we dynamically solve several overdetermined systems of linear equations from a training set and we infer the travel purposes. Our experimental results demonstrate that our model effectively predicts the purposes of mobility from the land use. Furthermore, our model shows great results compared with a reference supervised learning decomposition.
  • Human mobility
  • Purpose decomposition
  • Information extraction
  • Linear model


  • Access Statistics
  • Total Accesses (updated on a weekly basis)
    PDF Downloads


  1. Mariano G. Beiró, André Panisson, Michele Tizzoni, and Ciro Cattuto. Predicting human mobility through the assimilation of social media traces into mobility models. EPJ Data Science, 5(1):30, October 2016. URL:
  2. Michele Berlingerio, Francesco Calabrese, Giusy Di Lorenzo, Rahul Nair, Fabio Pinelli, and Marco Luca Sbodio. AllAboard: A System for Exploring Urban Mobility and Optimizing Public Transport Using Cellphone Data. In Hendrik Blockeel, Kristian Kersting, Siegfried Nijssen, and Filip Železný, editors, Machine Learning and Knowledge Discovery in Databases, number 8190 in Lecture Notes in Computer Science, pages 663-666. Springer Berlin Heidelberg, January 2013. Google Scholar
  3. F. Calabrese, G. Di Lorenzo, and C. Ratti. Human mobility prediction based on individual and collective geographical preferences. In 2010 13th International IEEE Conference on Intelligent Transportation Systems (ITSC), pages 312-317, September 2010. URL:
  4. Cynthia Chen, Jingtao Ma, Yusak Susilo, Yu Liu, and Menglin Wang. The promises of big data and small data for travel behavior (aka human mobility) analysis. Transportation Research Part C: Emerging Technologies, 68:285-299, 2016. URL:
  5. Cathal Coffey and Alexei Pozdnoukhov. Temporal Decomposition and Semantic Enrichment of Mobility Flows. In Proceedings of the 6th ACM SIGSPATIAL International Workshop on Location-Based Social Networks, LBSN'13, pages 34-43, New York, NY, USA, 2013. ACM. URL:
  6. Mi Diao, Yi Zhu, Joseph Ferreira, and Carlo Ratti. Inferring individual daily activities from mobile phone traces: A Boston example. Environment and Planning B: Planning and Design, 43(5):920-940, September 2016. URL:
  7. Zhanwei Du, Bo Yang, and Jiming Liu. Understanding the Spatial and Temporal Activity Patterns of Subway Mobility Flows. arXiv:1702.02456 [cs], February 2017. arXiv: 1702.02456. Google Scholar
  8. Li Gong, Xi Liu, Lun Wu, and Yu Liu. Inferring trip purposes and uncovering travel patterns from taxi trajectory data. Cartography and Geographic Information Science, 43(2):103-114, March 2016. URL:
  9. Sibren Isaacman, Richard Becker, Ramón Cáceres, Stephen Kobourov, Margaret Martonosi, James Rowland, and Alexander Varshavsky. Identifying Important Places in People’s Lives from Cellular Network Data. In Kent Lyons, Jeffrey Hightower, and Elaine M. Huang, editors, Pervasive Computing, number 6696 in Lecture Notes in Computer Science, pages 133-151. Springer Berlin Heidelberg, January 2011. Google Scholar
  10. S. Jiang, J. Ferreira, and M. C. Gonzalez. Activity-Based Human Mobility Patterns Inferred from Mobile Phone Data: A Case Study of Singapore. IEEE Transactions on Big Data, PP(99), 2017. URL:
  11. Shan Jiang, Marta C. Gonzalez, and Joseph Ferreira. Understanding the Link between Urban Activity Destinations and Human Travel Pattern. MIT web domain, July 2011. Google Scholar
  12. M. Katranji, E. Thuillier, S. Kraiem, L. Moalic, and F. H. Selem. Mobility data disaggregation: A transfer learning approach. In 2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC), pages 1672-1677, Nov 2016. URL:
  13. Anastasios Noulas. Human urban mobility in location-based social networks : analysis, models and applications. PhD thesis, University of Cambridge, UK, 2013. Google Scholar
  14. Lucas M. Silveira, Jussara M. de Almeida, Humberto T. Marques-Neto, Carlos Sarraute, and Artur Ziviani. MobHet: Predicting human mobility using heterogeneous data sources. Computer Communications, 95:54-68, 2016. URL:
  15. Zbigniew Smoreda, Ana-Maria Olteanu, and Thomas Couronné. Spatiotemporal data from mobile phones for personal mobility assessment. Transport Survey Methods: Best Practice for Decision Making, 2013. Google Scholar
  16. C. Spearman. The Proof and Measurement of Association between Two Things. The American Journal of Psychology, 15(1):72-101, 1904. URL:
  17. Jameson L. Toole, Michael Ulm, Marta C. González, and Dietmar Bauer. Inferring land use from mobile phone activity. In Proceedings of the ACM SIGKDD International Workshop on Urban Computing, UrbComp@KDD 2012, Beijing, China, August 12, 2012, pages 1-8. ACM Press, 2012. URL:
  18. Peter Widhalm, Yingxiang Yang, Michael Ulm, Shounak Athavale, and Marta C. González. Discovering urban activity patterns in cell phone data. Transportation, 42(4):597-623, July 2015. URL:
Questions / Remarks / Feedback

Feedback for Dagstuhl Publishing

Thanks for your feedback!

Feedback submitted

Could not send message

Please try again later or send an E-mail