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


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.

Publication DOI: https://doi.org/10.1504/IJCAT.2015.069329
Divisions: College of Engineering & Physical Sciences > School of Informatics and Digital Engineering > Mathematics
College of Engineering & Physical Sciences > Systems analytics research institute (SARI)
Additional Information: © Inderscience
Uncontrolled Keywords: driving knowledge,licensing renewal,probabilistic decision support system,uncertainty,Computer Science Applications,Electrical and Electronic Engineering,Industrial and Manufacturing Engineering,Software,Information Systems,Computer Networks and Communications
Publication ISSN: 1741-5047
Full Text Link: http://www.inde ... php?artid=69329
Related URLs: http://www.scop ... tnerID=8YFLogxK (Scopus URL)
PURE Output Type: Article
Published Date: 2015
Authors: Awad, Wa'El H.
Herzallah, Randa (ORCID Profile 0000-0001-9128-6814)



Version: Accepted Version

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