Behavioural data mining of transit smart card data: A data fusion approach

Transportation Research Part C: Emerging Technologies

Volumn 46, Issue , 2014, Pages 179-191

  Kusakabe, Takahiko ^a   Asakura, Yasuo ^a  

^a TOKYO INSTITUTE OF TECHNOLOGY   (Japan)

Author keywords

Behavioural analysis; Data fusion; Data mining; Na ve Bayes classifier; Smart card data

Indexed keywords

DATA FUSION; SMART CARDS; SURVEYS;

BAYES CLASSIFIER; BEHAVIOURAL ANALYSIS; FUSION METHODOLOGY; LONG TERM CHANGE; PERSON TRIP SURVEY; PROBABILISTIC MODELING; VALIDATION ANALYSIS; VALIDATION DATA;

DATA MINING;

DATA MINING; EMPIRICAL ANALYSIS; MODEL VALIDATION; NUMERICAL MODEL; PROBABILITY; TRANSPORTATION DEVELOPMENT; TRANSPORTATION PLANNING; BAYESIAN ANALYSIS; TRAVEL BEHAVIOR;

EID: 84904651564     PISSN: 0968090X     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.trc.2014.05.012     Document Type: Article

References (31)

1. Agard, B., Morency, C., Trépanier, M., 2006. Mining public transport user behaviour from smart card data. In: 12th IFAC Symposium on Information Control Problems in Manufacturing - INCOM 2006, Saint-Etienne, France, 17-19 May 1996.
2. Asakura Y., Hato E. Tracking survey for individual travel behaviour using mobile communication instruments. Transp. Res. Part C: Emerg. Technol. 2004, 12(3-4):273-291.
3. Asakura Y., Iryo T., Nakajima Y., Kusakabe T. Estimation of behavioural change of railway passengers using smart card data. Public Transp. 2012, 4(1):1-16.
4. Axhausen K.W., Zimmermann A., Schönfelder S., Rindsfüser G., Haupt T. Observing the rhythms of daily life: a six-week travel diary. Transportation 2002, 29(2):95-124.
5. Axhausen K.W., Löchl M., Schlich R., Buhl T., Widmer P. Fatigue in long-duration travel diaries. Transportation 2007, 34(2):143-160.
6. Bagchi M., White P.R. The potential of public transport smart card data. Transp. Policy 2005, 12(5):464-472.
7. Chu K.K.A., Chapleau R. Enriching archived smart card transaction data for transit demand modeling. Transp. Res. Rec. 2008, 2063:63-72.
8. Draijer G., Kalfs N., Perdok J. Global positioning system as a data collection method for travel research. Transp. Res. Rec. 2000, 1719:147-153.
9. El Faouzi N.-E., Leung H., Kurian A. Data fusion in intelligent transportation systems: progress and challenges - a survey. Inform. Fusion 2011, 12(1):4-10.
10. Fayyad U., Shapiro G.P., Smyth P. From data mining to knowledge discovery in databases. AI Mag. 1996, 17(3):37-54.
11. Golob T.T., Meurs H. Biases in response over time in a seven-day travel diary. Transportation 1986, 13(2):163-181.
12. Mathematical Techniques in Multisensor Data Fusion 1992, Artech House, Norwood, MA, USA. D.L. Hall (Ed.).
13. Kamakura W.A., Wedel M. Statistical data fusion for cross-tabulation. J. Mark. Res. 1997, 35(4):485-498.
14. Kitamura R. Panel analysis in transportation planning: an overview. Transp. Res. Part A: Gener. 1990, 24(6):401-415.
15. Kitamura R., Bovy P.H.L. Analysis of attrition biases and trip reporting errors for panel data. Transp. Res. Part A: Gener. 1987, 21(4-5):287-302.
16. Kusakabe, T., Asakura, Y., 2011. Behavioural data mining for railway travellers with smart card data. In: Second International Workshop on Traffic Data Collection and its Standardisation, Brisbane, Australia, 22-23 September 2011.
17. Kusakabe T., Iryo T., Asakura Y. Estimation method for railway passengers' train choice behavior with smart card transaction data. Transportation 2010, 37(5):731-749.
18. Ma X., Wu Y.-J., Wang Y., Chen F., Liu J. Mining smart card data for transit riders' travel patterns. Transp. Res. Part C: Emerg. Technol. 2013, 36:1-12.
19. Multi-Sensor Data Fusion - An Introduction 2007, Springer-Verlag, Berlin, Germany. H.B. Mitchell (Ed.).
20. Morency C., Trépanier M., Agard B. Measuring transit use variability with smart-card data. Transp. Policy 2007, 14(3):193-203.
21. Murakami E., Wagner D.P. Can using global positioning system (GPS) improve trip reporting?. Transp. Res. Part C: Emerg. Technol. 1999, 7(2-3):149-165.
22. Pas E.I., Koppelman F.S. An examination of the determinants of day-to-day variability in individuals' urban travel behavior. Transportation 1986, 13(2):183-200.
23. Pelletier M., Trépanier M., Morency C. Smart card data use in public transit: a literature review. Transp. Res. Part C: Emerg. Technol. 2011, 19(4):557-568.
24. Rish, I., 2001. An empirical study of the naïve Bayes classifier. In: IJCAI 2001 Workshop on Empirical Methods in Artificial Intelligence, Seattle, Washington, USA, 4-10 August 2001.
25. Seaborn C., Attanucci J., Wilson N. Analyzing multimodal public transport journeys in London with smart card fare payment data. Transp. Res. Rec. 2009, 2121:55-62.
26. Shen L., Stopher P.R. A process for trip purpose imputation from Global Positioning System data. Transp. Res. Part C: Emerg. Technol. 2013, 36:261-267.
27. Trépanier M., Tranchant N., Chapleau R. Individual trip destination estimation in a transit smart card automated fare collection system. J. Intell. Transp. Syst. 2007, 11(1):1-14.
28. Trépanier, M., Morency, C., Blanchette, C., 2009. Enhancing household travel surveys using smart card data. In: Paper Presented at the 88th Annual Meeting of the Transportation Research Board, Washington, DC, 11-15 January 2009.
29. Utsunomiya M., Attanucci J., Wilson N. Potential uses of transit smart card registration and transaction data to improve transit planning. Transp. Res. Rec. 2006, 1971:119-126.
30. Van Wissen L.J.G., Meurs H.J. The Dutch mobility panel: experiences and evaluation. Transportation 1989, 16(2):99-119.
31. Wolf J., Guensler R., Bachman W. Elimination of the travel diary: an experiment to derive trip purpose from GPS travel data. Transp. Res. Rec. 2001, 1768:124-134.