Data mining for enhanced driving effectiveness: an eco-driving behaviour analysis model for better driving decisions

Chia Yu Hsu, Sirirat Sae Lim*, Chin Sheng Yang

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

4 Scopus citations

Abstract

With the growing demand for energy efficient vehicles, automobile companies are constantly searching for better ways to study their customers’ driving behaviour for effective new product design and development. One emerging driving behaviour among modern, eco-friendly drivers is the utilising of advanced vehicle technology for smarter, safer and more fuel-efficient driving. While many eco-driving studies focus on minimising fuel consumption, little attention is paid to how the behaviour of an individual driver and the type of vehicle used impact driving effectiveness. This study addresses this gap by proposing a novel overall drive effectiveness index that uses data mining for better driving decisions. Utilising data mining techniques, the index examines the impact of driving behaviour on driving effectiveness. A novel fuel consumption prediction model based on vehicle speed, engine speed and engine load was constructed. This decision-making support model accurately predicts real-time fuel consumption based on different driving behaviours, and hence, the driving effectiveness. Both the proposed index and fuel consumption model can be used to support decision-making in new product design and development.

Original languageEnglish
Pages (from-to)7096-7109
Number of pages14
JournalInternational Journal of Production Research
Volume55
Issue number23
DOIs
StatePublished - 2 Dec 2017

Keywords

  • big data
  • data mining
  • decision support systems
  • eco-driving
  • overall drive effectiveness

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