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.
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