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

The aim of this study is to develop a data fusion methodology for estimating behavioural attributes of trips using smart card data to observe continuous long-term changes in the attributes of trips. The method is intended to enhance understanding of travellers’ behaviour during monitoring the smart card data. In order to supplement absent behavioural attributes in the smart card data, this study developed a data fusion methodology of smart card data with the person trip survey data with the naive Bayes probabilistic model. A model for estimating the trip purpose is derived from the person trip survey data. By using the model, trip purposes are estimated as supplementary behavioural attributes of the trips observed in the smart card data. The validation analysis showed that the proposed method successfully estimated the trip purposes in 86.2% of the validation data. The empirical data mining analysis showed that the proposed methodology can be applied to find and interpret the behavioural features observed in the smart card data which had been difficult to obtain from each independent dataset.

[1]  Henk Meurs,et al.  The Dutch mobility panel: Experiences and evaluation , 1989 .

[2]  Henry Leung,et al.  Data fusion in intelligent transportation systems: Progress and challenges - A survey , 2011, Inf. Fusion.

[3]  Catherine Morency,et al.  Enhancing Household Travel Surveys Using Smart Card Data , 2009 .

[4]  Xiaolei Ma,et al.  Mining smart card data for transit riders’ travel patterns , 2013 .

[5]  Ka Kee Alfred Chu,et al.  Enriching Archived Smart Card Transaction Data for Transit Demand Modeling , 2008 .

[6]  Henk Meurs,et al.  Biases in response over time in a seven-day travel diary , 1986 .

[7]  Frank S. Koppelman,et al.  An examination of the determinants of day-to-day variability in individuals' urban travel behavior , 1986 .

[8]  Padhraic Smyth,et al.  From Data Mining to Knowledge Discovery in Databases , 1996, AI Mag..

[9]  Peter R. Stopher,et al.  A process for trip purpose imputation from Global Positioning System data , 2013 .

[10]  E. Murakami,et al.  Can using global positioning system (GPS) improve trip reporting , 1999 .

[11]  Ryuichi Kitamura,et al.  Analysis of attrition biases and trip reporting errors for panel data , 1987 .

[12]  Randall Guensler,et al.  Elimination of the Travel Diary: Experiment to Derive Trip Purpose from Global Positioning System Travel Data , 2001 .

[13]  Bruno Agard,et al.  Measuring transit use variability with smart-card data , 2007 .

[14]  Martin Trépanier,et al.  Individual Trip Destination Estimation in a Transit Smart Card Automated Fare Collection System , 2007, J. Intell. Transp. Syst..

[15]  Kay W. Axhausen,et al.  Fatigue in long-duration travel diaries , 2007 .

[16]  Nigel H. M. Wilson,et al.  Potential Uses of Transit Smart Card Registration and Transaction Data to Improve Transit Planning , 2006 .

[17]  Nigel H. M. Wilson,et al.  Analyzing Multimodal Public Transport Journeys in London with Smart Card Fare Payment Data , 2009 .

[18]  Wagner A. Kamakura,et al.  Statistical Data Fusion for Cross-Tabulation , 1997 .

[19]  K. Axhausen,et al.  Observing the rhythms of daily life: A six-week travel diary , 2002 .

[20]  Peter White,et al.  The Potential of Public Transport Smart Card Data , 2005 .

[21]  Catherine Morency,et al.  Smart card data use in public transit: A literature review , 2011 .

[22]  Takamasa Iryo,et al.  Estimation of behavioural change of railway passengers using smart card data , 2012, Public Transp..

[23]  Bruno Agard,et al.  MINING PUBLIC TRANSPORT USER BEHAVIOUR FROM SMART CARD DATA , 2006 .

[24]  Takamasa Iryo,et al.  Estimation method for railway passengers’ train choice behavior with smart card transaction data , 2010 .

[25]  Yasuo Asakura,et al.  TRACKING SURVEY FOR INDIVIDUAL TRAVEL BEHAVIOUR USING MOBILE COMMUNICATION INSTRUMENTS , 2004 .

[26]  Ryuichi Kitamura,et al.  Panel Analysis in Transportation Planning: An Overview , 1990 .

[27]  Nelly Kalfs,et al.  Global Positioning System as Data Collection Method for Travel Research , 2000 .

[28]  Irina Rish,et al.  An empirical study of the naive Bayes classifier , 2001 .