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Predictive analytics on open big data for supporting smart transportation services.

Paul Patrick F Balbin1, Jackson C R Barker1, Carson K Leung1

  • 1University of Manitoba, Winnipeg, MB, Canada.

Procedia Computer Science
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Summary
This summary is machine-generated.

This study analyzes open big data on public transit performance to predict bus timeliness. Findings show predictive analytics can improve smart transportation services by understanding early, on-time, and delayed bus patterns.

Keywords:
Predictive analyticsWinnipeg open databig datafrequent patternslarge-scale systemson-time performanceopen datasoftware engineeringtransportation data

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Area of Science:

  • Data Science
  • Transportation Science
  • Computer Science

Background:

  • Big data is rapidly generated, containing valuable, often implicit, information discoverable through data science techniques like data mining.
  • Open big data initiatives are increasing, fostering collaborative analysis across various sectors, including public transit.
  • Public transit is essential, and bus timeliness (early, on-time, or delayed) significantly impacts commuters.

Purpose of the Study:

  • To analyze open big data concerning public transit bus performance.
  • To apply frequent pattern mining and decision-tree classification for predictive analytics.
  • To demonstrate the utility of predictive analytics on open big data for smart transportation.

Main Methods:

  • Utilized frequent pattern mining for data analysis.
  • Employed decision-tree based classification for predictive modeling.
  • Applied these methods to real-world open big data from Winnipeg Transit.

Main Results:

  • Identified patterns in bus performance data (early, on-time, late).
  • Developed predictive models for bus timeliness.
  • Demonstrated the practical application of predictive analytics on open transit data.

Conclusions:

  • Open big data analysis, using techniques like frequent pattern mining and classification, is beneficial for public transit.
  • Predictive analytics on bus performance data can enhance smart transportation services.
  • Understanding early departures is as crucial as predicting delays for commuter satisfaction.