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

Awad, Wa'El H. and Herzallah, Randa (2015). Driving licensing renewal policy using neural network-based probabilistic decision support system. International Journal of Computer Applications in Technology, 51 (3), pp. 155-163.

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.

Publication DOI: https://doi.org/10.1504/IJCAT.2015.069329
Divisions: Engineering & Applied Sciences > Mathematics
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
Published Date: 2015

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