Driving licensing renewal policy using neural network-based probabilistic decision support system

Wa'El H. Awad, Randa Herzallah*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

This paper investigates neural network-based probabilistic decision support system to assess drivers' knowledge for the objective of developing a renewal policy of driving licences. The probabilistic model correlates drivers' demographic data to their results in a simulated written driving exam (SWDE). The probabilistic decision support system classifies drivers' into two groups of passing and failing a SWDE. Knowledge assessment of drivers within a probabilistic framework allows quantifying and incorporating uncertainty information into the decision-making system. The results obtained in a Jordanian case study indicate that the performance of the probabilistic decision support systems is more reliable than conventional deterministic decision support systems. Implications of the proposed probabilistic decision support systems on the renewing of the driving licences decision and the possibility of including extra assessment methods are discussed.

Original languageEnglish
Pages (from-to)155-163
Number of pages9
JournalInternational Journal of Computer Applications in Technology
Volume51
Issue number3
DOIs
Publication statusPublished - 2015

Bibliographical note

© Inderscience

Keywords

  • driving knowledge
  • licensing renewal
  • probabilistic decision support system
  • uncertainty

Fingerprint Dive into the research topics of 'Driving licensing renewal policy using neural network-based probabilistic decision support system'. Together they form a unique fingerprint.

Cite this