Traffic big data prediction and visualization using Fast Incremental Model Trees-Drift Detection (FIMT-DD)

Ari Wibisono, Wisnu Jatmiko, Hanief Arief Wisesa, Benny Hardjono, Petrus Mursanto

Research output: Contribution to journalArticlepeer-review

68 Citations (Scopus)


Information extraction using distributed sensors has been widely used to obtain information knowledge from various regions or areas. Vehicle traffic data extraction is one of the ways to gather information in order to get the traffic condition information. This research intends to predict and visualize the traffic conditions in a particular road region. Traffic data was obtained from Department of Transport UK. These data are collected using hundreds of sensors for 24 h. Thus, the size of data is very huge. In order to get the behavior of the traffic condition, we need to analyze the huge dataset which was obtained from the sensors. The uses of conventional data mining methods are not sufficient to use, due to the process of knowledge building that should store data temporary in the memory. The fact that data is continuously becoming larger over time, therefore we need to find a method that could automatically adapt to process data in the form of streams. We use method called FIMT-DD (Fast Incremental Model Trees-Drift Detection) to analyze and predict the very large traffic dataset. Based on the prediction system that we have developed, we also visualize the prediction of traffic flow condition within generated sensor point in the real map simulation.

Original languageEnglish
Pages (from-to)33-46
Number of pages14
JournalKnowledge-Based Systems
Publication statusPublished - 1 Feb 2016


  • Data stream
  • Intelligent traffic systems
  • Traffic prediction
  • Traffic visualization


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