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.
Smart Card, Transportation, Time Series, Data Mining, Large Time Series Data
Computer Sciences | Databases and Information Systems | Transportation
Data Management and Analytics
International Conference on Civil and Urban Engineering ICCUE 2014, 29-30 March, Shanghai
City or Country
KAM, Tin Seong and LEE, Roy Ka Wei.
Time-Series Data Mining in Transportation: A Case Study on Singapore Public Train Commuter Travel Patterns. (2014). International Conference on Civil and Urban Engineering ICCUE 2014, 29-30 March, Shanghai. Research Collection School Of Information Systems.
Available at: http://ink.library.smu.edu.sg/sis_research/2100