Context aware ontology‐based hybrid intelligent framework for vehicle driver categorization

Journal article


Sarwar, Sohail, Zia, Saad, Ul Qayyum, Zia, Iqbal, Muddesar, Safyan, Muhammad, Mumtaz, Shahid and García‐Castro, Raul (2019). Context aware ontology‐based hybrid intelligent framework for vehicle driver categorization. Transactions on Emerging Telecommunications Technologies.
AuthorsSarwar, Sohail, Zia, Saad, Ul Qayyum, Zia, Iqbal, Muddesar, Safyan, Muhammad, Mumtaz, Shahid and García‐Castro, Raul
Abstract

In public vehicles, one of the major concerns is driver's level of expertise for its direct proportionality to safety of passengers. Hence, before a driver is subjected to certain type of vehicle, he should be thoroughly evaluated and categorized with respect to certain parameters instead of only one‐time metric of having driving license. These aspects may be driver's expertise, vigilance, aptitude, experience years, cognition, driving style, formal education, terrain, region, minor violations, major accidents, and age group. The purpose of this categorization is to ascertain suitability of a driver for certain vehicle type(s) to ensure passengers' safety. Currently, no driver categorization technique fully comprehends the implicit as well as explicit characteristics of drivers dynamically. In this paper, machine learning–based dynamic and adaptive technique named D‐CHAITs (driver categorization through hybrid of artificial intelligence techniques) is proposed for driver categorization with an objective focus on driver's attributes modeled in DriverOntology. A supervised mode of learning has been employed on a labeled dataset, having diverse profiles of drivers with attributes pertinent to drivers' perspectives of demographics, behaviors, expertise, and inclinations. A comparative analysis of D‐CHAIT with three other machine learning techniques (fuzzy logic, case‐based reasoning, and artificial neural networks) is also presented. The efficacy of all techniques was empirically measured while categorizing the drivers based on their profiles through metrics of accuracy, precision, recall, F‐measure performance, and associated costs. These empirical quantifications assert D‐CHAIT as a better technique than contemporary ones. The novelty of proposed technique is signified through preprocessing of feature attributes, quality of data, training of machine learning model on more relevant data, and adaptivity

This is the peer reviewed version of the following article: Context aware ontology‐based hybrid intelligent framework for vehicle driver categorization, which has been published in final form at https://doi.org/10.1002/ett.3729. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions.

KeywordsArtificial Neural Networks; Case Based Reasoning;; Fuzzy Logic; Machine Learning; Vehicle Driver Categorization
Year2019
JournalTransactions on Emerging Telecommunications Technologies
PublisherWiley
ISSN2161-3915
Digital Object Identifier (DOI)doi:https://www.doi.org/10.1002/ett.3729
Publication dates
Online29 Aug 2019
Publication process dates
Accepted20 Jul 2019
Deposited23 Dec 2019
Accepted author manuscript
License
CC BY-NC 4.0
File Access Level
Open
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https://openresearch.lsbu.ac.uk/item/88v44

Accepted author manuscript

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