Analysing the Variability of Transit Users Behaviour with Smart Card Data

This paper proposes various measures regarding the variability of travel behaviours of transit users. The analyses are performed with smart card data collected over a ten months period. The variability in terms of boarding per day and new stops frequented with the days of travel on the transit network is examined. Data mining techniques are then used to classify days of travel according to the similarity of the boarding time periods. In this view, the use of two specific smart cards is examined in more details. These experiments first show that the behaviours of regular transit users evolve with time both in terms of transit stops frequented and time of boarding. Hence, variability of behaviours also changes for various user types

[1]  Katherine M. Shelfer,et al.  Smart card evolution , 2002, CACM.

[2]  Michael J. A. Berry,et al.  Data mining techniques - for marketing, sales, and customer support , 1997, Wiley computer publishing.

[3]  Ronen Feldman,et al.  The Data Mining and Knowledge Discovery Handbook , 2005 .

[4]  Dan Braha,et al.  Data Mining for Design and Manufacturing , 2001, Massive Computing.

[5]  Andrew Kusiak,et al.  Data Mining for Selection of Manufacturing Processes , 2005, The Data Mining and Knowledge Discovery Handbook.

[6]  E. I. Pas.  Weekly travel-activity behavior , 1988 .

[7]  K. Axhausen,et al.  Introduction: Habitual travel choice , 2003 .

[8]  Roger Clarke,et al.  Person location and person tracking - Technologies, risks and policy implications , 2001, Inf. Technol. People.

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

[10]  Kay W. Axhausen,et al.  How routine is a routine?: An analysis of the day-to-day variability in prism vertex location , 2005 .

[11]  Jun Ma,et al.  A dynamic analysis of person and household activity and travel patterns using data from the first two waves in the Puget Sound Transportation Panel , 1997 .

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

[13]  B. Agard,et al.  IMPROVING MANUFACTURING QUALITY BY RE-SEQUENCING ASSEMBLY OPERATIONS: A DATA-MINING APPROACH , 2005 .

[14]  Sarabjot Singh Anand,et al.  Decision support using data mining , 1998 .

[15]  A. K. Pujari,et al.  Data Mining Techniques , 2006 .

[16]  M. Hofmann,et al.  Transfer journey identification and analyses from electronic fare collection data , 2005, Proceedings. 2005 IEEE Intelligent Transportation Systems, 2005..

[17]  Eric I. Pas,et al.  A Flexible and Integrated Methodology for Analytical Classification of Daily Travel-Activity Behavior , 1983 .

[18]  Bruno Agard,et al.  BUSINESS PROCESS REENGINEERING WITH DATA MINING IN REAL ESTATE CREDIT ATTRIBUTION: A CASE STUDY , 2006 .

[19]  Bruno Agard,et al.  MANUFACTURING PLANT LAYOUT SUPPORTED WITH DATA MINING TECHNIQUES , 2007 .

[20]  Frank Montgomery,et al.  DERIVING THE CONSTANCY OF TRAFFIC FLOW COMPOSITION FROM VEHICLE REGISTRATION DATA , 1984 .

[21]  Ricco Rakotomalala,et al.  TANAGRA : un logiciel gratuit pour l'enseignement et la recherche , 2005, EGC.

[22]  K. Axhausen,et al.  Habitual travel behaviour: Evidence from a six-week travel diary , 2003 .