Data processing and harmonization for intelligent transportation systems: an application scenario on highway traffic flows

Research output: Chapter in Book/Report/Conference proceedingChapter

1 Citation (Scopus)

Abstract

In the advent of ITS (Intelligent Transportation Systems) the transportation sector generates large volumes of real-time data that needs to be collected, harmonized, interpreted, aggregated, and analysed. To this end, innovative big data processing and mining, together with optimization techniques, need to be developed and applied to better support real-time decision-making capabilities. This chapter presents an ETL (Extract-Transform-Load) architecture for intelligent transportation systems, addressing an application scenario on dynamic toll charging for highways. The ETL approach presented is responsible for preparing the data to be used by traffic prediction services, which will dynamically affect toll prices within different contexts. It relies on the adoption of “big data” technologies, to process and store large volumes of data from heterogeneous sources, provided by different highway operators, and is capable of handling real-time and historical data. The DATEX-II data model is adopted, enabling harmonization of traffic data provided by the highway operators.

Original languageEnglish
Title of host publicationLearning Systems: From Theory to Practice
EditorsV. Sgurev, V. Piuri, V. Jotsov
Place of PublicationCham
PublisherSpringer Verlag
Pages281-301
Number of pages21
ISBN (Electronic)978-3-319-75181-8
ISBN (Print)978-3-319-75180-1
DOIs
Publication statusPublished - 1 Jan 2018

Publication series

NameStudies in Computational Intelligence
PublisherSpringer Verlag
Volume756
ISSN (Print)1860-949X

Keywords

  • Data mining
  • Industry
  • Predictive analytics

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  • Cite this

    Figueiras, P., Guerreiro, G., Silva, R. A., Costa, R., & Jardim-Gonçalves, R. (2018). Data processing and harmonization for intelligent transportation systems: an application scenario on highway traffic flows. In V. Sgurev, V. Piuri, & V. Jotsov (Eds.), Learning Systems: From Theory to Practice (pp. 281-301). (Studies in Computational Intelligence; Vol. 756). Cham: Springer Verlag. https://doi.org/10.1007/978-3-319-75181-8_14