Urban activity pattern classification using topic models from online geo-location data

Transportation Research Part C: Emerging Technologies

Volumn 44, Issue , 2014, Pages 363-381

  Hasan, Samiul ^a   Ukkusuri, Satish V ^a  

^a PURDUE UNIVERSITY   (United States)

Author keywords

Activity pattern classification; Activity based modeling; Big data; Location based data; Machine learning; Social computing; Social media; Topic modeling

Indexed keywords

BIG DATA; LEARNING SYSTEMS; LOCATION; SOCIAL COMPUTING; SOCIAL NETWORKING (ONLINE);

ACTIVITY BASED MODELING; ACTIVITY PATTERNS; LOCATION BASED; SOCIAL MEDIA; TOPIC MODELING;

LOCATION BASED SERVICES;

ACTIVITY PATTERN; ARTIFICIAL INTELLIGENCE; MODELING; SOCIAL NETWORK; SPATIAL DATA; CLASSIFICATION; MEDIA ROLE; TRAVEL BEHAVIOR; URBAN AREA; WORLD WIDE WEB;

EID: 84901405586     PISSN: 0968090X     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.trc.2014.04.003     Document Type: Article

References (35)

1. Alesiani, Francesco, Gkiotsalitis, Konstantinos, Baldessari, Roberto, 2014. A probabilistic activity model for predicting the mobility patterns of homogeneous social groups based on social network data. In: The 93rd Annual Meeting of Transportation Research Board.
2. Allahviranloo Mahdieh, Recker Will Daily activity pattern recognition by using support vector machines with multiple classes. Transport. Res. Part B: Methodol. 2013, 58:16-43.
3. Blei David M., Ng Andrew Y., Jordan Michael I. Latent dirichlet allocation. J. Mach. Learn. Res. 2003, 3:993-1022.
4. Cebelak, Meredith Kimberly, 2013. Location-based social networking data: doubly-constrained gravity model origin-destination estimation of the urban travel demand for Austin, TX. Master's Thesis, The University of Texas at Austin.
5. Cheng, Zhiyuan, Caverlee, James, Lee, Kyumin, Sui, Daniel Z., 2011. Exploring millions of footprints in location sharing services. In: Proceedings of the 5th International AAAI Conference on Weblogs and Social Media (ICWSM).
6. Collins Craig, Hasan Samiul, Ukkusuri Satish V. A novel transit rider satisfaction metric: rider sentiments measured from online social media data. J. Public Transport. 2013, 16(2).
7. Comscore, 2011. Comscore Press Release May 12, 2011. http://www.comscore. com/Press_Events/Press_Releases/2011/5/Nearly_1_in_5_Smartphone_Owners_A ccess_Check-In_Services_Via_their_Mobile_Device(accessed 12.01.12).
8. Cranshaw, Justin, Schwartz, Raz, Hong, JasonI, Sadeh, Norman M., 2012. The livehoods project: utilizing social media to understand the dynamics of a city. In: Proceedings of the 6th International AAAI Conference on Weblogs and Social Media (ICWSM).
9. De Choudhury, Munmun, Gamon, Michael, Counts, Scott, Horvitz, Eric. 2013. Predicting depression via social media. In: Proceedings of the 7th International AAAI Conference on Weblogs and Social Media (ICWSM).
10. eMarketer, 2014. Smartphone Users Worldwide will Total 1.75Billion in 2014. http://www.emarketer.com/Article/Smartphone-Users-Worldwide-Will-Total-175-Billion-2014/1010536 (accessed 3.04.14).
11. Eunjoon, SethA, Jure Friendship and mobility: user movement in location-based social networks. Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining 2011, 1082-1090. ACM.
12. Farrahi Katayoun., Gatica-Perez Daniel. Discovering routines from large-scale human locations using probabilistic topic models. ACM Trans. Intell. Syst. Technol. 2011, 2(1).
13. Gao Huiji, Tang Jiliang, Liu Huan gSCorr: modeling geo-social correlations for new check-ins on location-based social networks. Proceedings of the 21st ACM International Conference on Information and Knowledge Management 2012, 1582-1586. ACM.
14. Griffiths Thomas L., Steyvers Mark. Finding scientific topics. Proc. Natl. Acad. Sci. 2004, 101(suppl. 1):5228-5235.
15. Halevy Alon, Norvig Peter, Pereira Fernando The unreasonable effectiveness of data. IEEE Intell. Syst. 2009, 24(2):8-12.
16. Hanson Susan, Hanson Perry The travel-activity patterns of urban residents: dimensions and relationships to sociodemographic characteristics. Econ. Geograph. 1981, 57(4):332-347.
17. Hasan Samiul, 2013. Modeling Urban Mobility Dynamics Using Geo-location Data. Ph.D. Dissertation, Purdue University.
18. Hasan Samiul, Zhan Xianyuan, Ukkusuri Satish V. Understanding urban human activity and mobility patterns using large-scale location-based data from online social media. Proceedings of the 2nd ACM SIGKDD International Workshop on Urban Computing 2013, ACM.
19. Huynh Tâm, Fritz Mario, Schiele Bernt Discovery of activity patterns using topic models. Proceedings of the 10th International Conference on Ubiquitous Computing 2008, 10-19. ACM.
20. Jin, Peter J., Cebelak, Meredith, Yang, Fan, Ran, Bin, Walton, C. Michael, Zhang, Jian, 2014. Location-based social networking data: exploration of use of doubly constrained gravity model for origin-destination estimation. In: The 93rd Annual Meeting of Transportation Research Board.
21. Joh Chang-Hyeon, Arentze Theo, Timmermans Harry Pattern recognition in complex activity travel patterns: comparison of Euclidean distance, signal-processing theoretical, and multidimensional sequence alignment methods. Transport. Res. Record J. Transport. Res. Board 2001, 1752(1):16-22.
22. Joh Chang-Hyeon, Arentze Theo, Hofman Frank, Timmermans Harry Activity pattern similarity: a multidimensional sequence alignment method. Transport. Res. Part B: Methodol. 2002, 36(5):385-403.
23. Koppelman, Frank S., Pas, Eric I., 1983. Travel-activity behavior in time and space: methods for representation and analysis. School of Engineering, Duke University.
24. Ni, Ming, He, Qing, Gao, Jing, 2014. Using social media to predict traffic flow under special event conditions. In: The 93rd Annual Meeting of Transportation Research Board.
25. Noulas, Anastasios, Scellato, Salvatore, Mascolo, Cecilia, Pontil, Massimiliano, 2011. An empirical study of geographic user activity patterns in foursquare. In: Proceedings of the 5th International AAAI Conference on Weblogs and Social Media (ICWSM).
26. Pas Eric I. Analytically derived classifications of daily travel-activity behavior: description, evaluation, and interpretation. Transport. Res. Record. 1982.
27. Pas Eric I. A flexible and integrated methodology for analytical classification of daily travel-activity behavior. Transport. Sci. 1983, 17(4):405-429.
28. Pas Eric I. The effect of selected sociodemographic characteristics on daily travel-activity behavior. Environ. Plan. A 1984, 16(5):571-581.
29. Recker Wilfred W., McNally Michael G., Root Gregory S. Travel/activity analysis: pattern recognition, classification and interpretation. Transport. Res. Part A: General 1985, 19(4):279-296.
30. Robert Christian The bayesian choice: from decision-theoretic foundations to computational implementation. Springer Texts in Statistics 2001, Springer.
31. Sammour George, Bellemans Tom, Vanhoof Koen, Janssens Davy, Kochan Bruno, Wets Geert The usefulness of the sequence alignment methods in validating rule-based activity-based forecasting models. Transportation 2012, 39(4):773-789.
32. Steyvers Mark. Combining feature norms and text data with topic models. Acta Psychol. 2010, 133(3):234-243.
33. Steyvers, Mark, Griffiths, Tom, 2007. Probabilistic topic models. Handbook of Latent Semantic Analysis, vol. 427(7), pp. 424-440.
34. Wilson Clarke. Activity patterns in space and time: calculating representative Hagerstrand trajectories. Transportation 2008, 35(4):485-499. http://dx.doi.org/10.1007/s11116-008-9162-z.
35. Yang, Fan, Jin, Peter J., Wan, Xia, Li, Rui, Ran, Bin, 2014. Dynamic origin-destination travel demand estimation using location based social networking data. In: The 93rd Annual Meeting of Transportation Research Board.