Title: Driving licensing renewal policy using neural network-based probabilistic decision support system
Authors: Wa'el H. Awad; Randa Herzallah
Addresses: Faculty of Engineering Technology, Department of Civil Engineering, Al Balqa' Applied University, P.O. Box 15008, Amman 11134, Jordan ' Non-linearity and Complexity Research Group, Aston University, Birmingham B4 7ET, UK
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.
Keywords: driving knowledge; driving licensing renewal; probabilistic DSS; decision support systems; uncertainty; renewal policy; neural networks; reliability; driving licences; demographics; simulation; written driving exams; knowledge assessment; driver assessment; Jordan; case study.
International Journal of Computer Applications in Technology, 2015 Vol.51 No.3, pp.155 - 163
Received: 08 May 2021
Accepted: 12 May 2021
Published online: 11 May 2015 *