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Soft Computing

, Volume 22, Issue 17, pp 5707–5718 | Cite as

Using machine learning and big data approaches to predict travel time based on historical and real-time data from Taiwan electronic toll collection

  • Shu-Kai S. Fan
  • Chuan-Jun Su
  • Han-Tang Nien
  • Pei-Fang Tsai
  • Chen-Yang Cheng
Focus

Abstract

As the technology in automation and computation advances, traffic data can be easily collected from multiple sources, such as sensors and surveillance cameras. To extract value from the huge volumes of available data requires the capability to process and extract patterns in large datasets. In this paper, a machine learning method embedded within a big data analytics platform is constructed by using random forests method and Apache Hadoop to predict highway travel time based on data collected from highway electronic toll collection in Taiwan. Various prediction models are then developed for highway travel time based on historical and real-time data to provide drivers with estimated and adjusted travel time information.

Keywords

Big data Random forests Electronic toll collection (ETC) Travel time prediction Apache Hadoop 

Notes

Acknowledgements

This study was partially funded by the Ministry of Science and Technology (Taiwan) Grant: MOST 105-2221-E-027-052 -MY3.

Compliance with ethical standards

Conflict of interest

All the authors of this paper declare that they have no conflict of interest.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2017

Authors and Affiliations

  • Shu-Kai S. Fan
    • 1
  • Chuan-Jun Su
    • 2
  • Han-Tang Nien
    • 1
  • Pei-Fang Tsai
    • 1
  • Chen-Yang Cheng
    • 1
  1. 1.Department of Industrial Engineering and ManagementNational Taipei University of TechnologyTaipei CityTaiwan, ROC
  2. 2.Department of Industrial Engineering and ManagementYuan Ze UniversityTaoyuan CityTaiwan, ROC

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