On Learning Fuel Consumption Prediction in Vehicle Clusters

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)


Identifying granular patterns of differentiation and learning predictors of product performance are key drivers to capitalize on competitive market segments. In this paper, we propose an approach to identify granular product patterns by using Hierarchical Clustering, and to learn predictors of product performance from historical data by using Genetic Programming. Computational experiments using more than twenty thousand vehicle models collected over the last thirty years shows (1) the feasibility to identify vehicle differentiation at different levels of granularity by hierarchical clustering, and (2) the good predictive ability of learned fuel consumption predictors in vehicle cluster. We believe our approach introduces the building blocks to further advance on studies regarding product differentiation and market segmentation by using data-intensive approaches.

Original languageEnglish
Title of host publicationProceedings - 2018 IEEE 42nd Annual Computer Software and Applications Conference, COMPSAC 2018
EditorsClaudio Demartini, Sorel Reisman, Ling Liu, Edmundo Tovar, Hiroki Takakura, Ji-Jiang Yang, Chung-Horng Lung, Sheikh Iqbal Ahamed, Kamrul Hasan, Thomas Conte, Motonori Nakamura, Zhiyong Zhang, Toyokazu Akiyama, William Claycomb, Stelvio Cimato
PublisherIEEE Computer Society
Number of pages6
ISBN (Electronic)9781538626665
Publication statusPublished - 2018 Jun 8
Event42nd IEEE Computer Software and Applications Conference, COMPSAC 2018 - Tokyo, Japan
Duration: 2018 Jul 232018 Jul 27

Publication series

NameProceedings - International Computer Software and Applications Conference
ISSN (Print)0730-3157


Other42nd IEEE Computer Software and Applications Conference, COMPSAC 2018


  • Clustering
  • Fuel consumption estimation
  • Genetic programming
  • Prediction
  • Vehicle
  • Vehicle clustering

ASJC Scopus subject areas

  • Software
  • Computer Science Applications


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