Inferring travel purpose from crowd-augmented human mobility data

Urb-IoT 2014 - 1st International Conference on IoT in Urban Space, Conference Proceedings

Volumn , Issue , 2014, Pages 44-49

  Zhu, Zack ^a   Blanke, Ulf ^a   Tröster, Gerhard ^a  

^a ETH ZURICH   (Switzerland)

Author keywords

Crowd generated urban data; Data fusion; Human mobility mining

Indexed keywords

BEHAVIORAL RESEARCH; INTERNET OF THINGS; SEMANTICS; SURVEYS; URBAN PLANNING;

AFFORDANCES; CROWD-GENERATED URBAN DATA; HUMAN MOBILITY; HUMAN MOBILITY MINING; LOCATION-BASED SOCIAL NETWORKS; MOBILITY DATUM; MOBILITY MININGS; NETWORK DATA; TRAVEL SURVEYS; URBAN SPACES;

DATA FUSION;

EID: 85075936878     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.4108/icst.urb-iot.2014.257173     Document Type: Conference Paper

References (14)

1. Blanke, U., Franke, T., Tröster, G., and Lukowicz, P. Capturing crowd dynamics at large scale events using participatory gps-localization. ISSNIP '14 (2014).
2. Chang, Y.-W., and Lin, C.-J. Feature ranking using linear svm. In WCCI Causation and Prediction Challenge (2008), 53-64.
3. Coffey, C., and Pozdnoukhov, A. Temporal decomposition and semantic enrichment of mobility flows. LBSN '13 (2013), 34-43.
4. Eagle, N., and (Sandy) Pentland, A. Reality mining: Sensing complex social systems. Personal Ubiquitous Comput. 10, 4 (Mar. 2006), 255-268.
5. Fan, R.-E., Chang, K.-W., Hsieh, C.-J., Wang, X.-R., and Lin, C.-J. LIBLINEAR: A library for large linear classification. Journal of Machine Learning Research 9 (2008), 1871-1874.
6. Gibson, J. J. The theory of affordances. Hilldale, USA (1977).
7. Gonzalez, M. C., Hidalgo, C. A., and Barabasi, A.-L. Understanding individual human mobility patterns. Nature 453, 7196 (2008), 779-782.
8. Jiang, S., Ferreira, J., and Gonzalez, M. Clustering daily patterns of human activities in the city. Data Mining and Knowledge Discovery 25, 3 (2012), 478-510.
9. Krumm, J., and Rouhana, D. Placer: Semantic place labels from diary data. UbiComp '13 (2013), 163-172.
10. Noulas, A., Mascolo, C., and Frias-Martinez, E. Exploiting foursquare and cellular data to infer user activity in urban environments. MDM '13 (2013), 167-176.
11. Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., and Duchesnay, E. Scikit-learn: Machine learning in Python. Journal of Machine Learning Research 12 (2011), 2825-2830.
12. Puget Sound Regional Council. 2006 Household Activity Survey: General User's Guide. http://www.psrc.org/assets/ 4358/2006HHSurveyUserGuide.pdf, 2006.
13. Wu, L., Zhi, Y., Sui, Z., and Liu, Y. Intra-urban human mobility and activity transition: Evidence from social media check-in data. PloS one 9, 5 (2014), e97010.
14. Zheng, Y., Liu, Y., Yuan, J., and Xie, X. Urban computing with taxicabs. UbiComp '11 (2011), 89-98.