Abstract—The adoption of smart cards technologies and
automated data collection systems (ADCS) in transportation
domain had provided public transport planners opportunities to
amass a huge and continuously increasing amount of time-series
data about the behaviors and travel patterns of commuters.
However the explosive growth of temporal related databases has
far outpaced the transport planners’ ability to interpret these
data using conventional statistical techniques, creating an
urgent need for new techniques to support the analyst in
transforming the data into actionable information and
knowledge. This research study thus explores and discusses the
potential use of time-series data mining, a relatively new
framework by integrating conventional time-series analysis and
data mining techniques, to discover actionable insights and
knowledge from the transportation temporal data. A case study
on the Singapore public train transit will also be used to
demonstrate the time-series data-mining framework and
methodology.
Index Terms—Time-series data mining, smart card, big data,
transportation.
The authors are with Singapore Management University, Singapore
(e-mail: roylee.2013@phdis.smu.edu.sg).
[PDF]
Cite: Roy Ka-Wei Lee and Tin Seong Kam, "Time-Series Data Mining in Transportation: A Case Study
on Singapore Public Train Commuter Travel Patterns," International Journal of Engineering and Technology vol. 6, no. 5, pp. 431-438, 2014.
