About: Identifying Heavy Goods Vehicle Driving Styles in the United Kingdom

Not logged in : Login

(Sponging disallowed)

Facets (new session)
Description
Metadata
Settings
- Rule:
- Inverse Functional Properties:
- "Same As":

About: Identifying Heavy Goods Vehicle Driving Styles in the United Kingdom Goto Sponge NotDistinct Permalink

An Entity of Type : bibo:AcademicArticle, within Data Space : linkeddata.uriburner.com:28898 associated with source document(s)

Attributes	Values
type	http://eprints.org/ontology/ArticleEPrint http://eprints.org/ontology/EPrint Article Academic Article
seeAlso	HTML Summary of #77039 Identifying Heavy Goods Vehicle Driving Styles in the United Kingdom
sameAs	Identifying Heavy Goods Vehicle Driving Styles in the United Kingdom
http://eprints.org/ontology/hasAccepted	Identifying Heavy Goods Vehicle Driving Styles in the United Kingdom (PDF)
http://eprints.org/ontology/hasDocument	Identifying Heavy Goods Vehicle Driving Styles in the United Kingdom (PDF) Identifying Heavy Goods Vehicle Driving Styles in the United Kingdom (Other) Identifying Heavy Goods Vehicle Driving Styles in the United Kingdom (Other) Identifying Heavy Goods Vehicle Driving Styles in the United Kingdom (Other) Identifying Heavy Goods Vehicle Driving Styles in the United Kingdom (Other) Identifying Heavy Goods Vehicle Driving Styles in the United Kingdom (Other)
dc:hasVersion	Identifying Heavy Goods Vehicle Driving Styles in the United Kingdom (PDF)
Title	Identifying Heavy Goods Vehicle Driving Styles in the United Kingdom
described by	https://demo.openlinksw.com/about/id/entity/https/www.cs.kent.ac.uk/people/staff/akj22/materials/CO644/export_kar_RDFN3.n3 https://demo.openlinksw.com/about/id/entity/https/raw.githubusercontent.com/annajordanous/CO644Files/main/export_kar_RDFN3.n3
Date	2019-08-27
Creator	Grazziela P Figueredo Christian Wagner Robert I John Peer-Olaf Siebers Jimiama MM Mase Jonathan M Garibaldi Mohammad Mesgarpour Utkarsh Agrawal D. Soria
status	peer reviewed published
Publisher	IEEE
abstract	Although driving behavior has been largely studied amongst private motor vehicles drivers, the literature addressing heavy goods vehicle (HGV) drivers is scarce. Identifying the existing groups of driving stereotypes and their proportions enables researchers, companies, and policy makers to establish group-specific strategies to improve safety and economy. In addition, insight into driving styles can help predict drivers' reactions and therefore enable the modeling of interactions between vehicles and the possible obstacles encountered on a journey. Consequently, there are also contributions to the research and development of autonomous vehicles and smart roads. In this paper, our interest lies in investigating driving behavior within the HGV community in the United Kingdom (U.K.). We conduct analysis of a telematics dataset containing the incident information on 21 193 HGV drivers across the U.K. We are interested in answering two research questions: 1) What groups of behavior are we able to uncover? 2) How do these groups complement current findings in the literature? To answer these questions, we apply a two-stage data analysis methodology involving consensus clustering and ensemble classification to the dataset. Through the analysis, eight patterns of behavior are uncovered. It is also observed that although our findings have similarities to those from previous work on driving behavior, further knowledge is obtained, such as extra patterns and driving traits arising from vehicle and road characteristics.
Is Part Of	IEEE Transactions on Intelligent Transportation Systems https://kar.kent.ac.uk/id/repository
Subject	QA Mathematics (inc Computing science)
list of authors	https://kar.kent.ac.uk/id/eprint/77039#authors
issue	9
volume	20
is topic of	https://www.cs.kent.ac.uk/people/staff/akj22/materials/CO644/export_kar_RDFN3.n3 https://raw.githubusercontent.com/annajordanous/CO644Files/main/export_kar_RDFN3.n3
is primary topic of	HTML Summary of #77039 Identifying Heavy Goods Vehicle Driving Styles in the United Kingdom

Faceted Search & Find service v1.17_git144 as of Jul 26 2024

Alternative Linked Data Documents: iSPARQL | ODE Content Formats:

RDF

ODATA

Microdata

About

OpenLink Virtuoso version 08.03.3331 as of Aug 25 2024, on Linux (x86_64-ubuntu_noble-linux-glibc2.38-64), Single-Server Edition (378 GB total memory, 14 GB memory in use)
Data on this page belongs to its respective rights holders.
Virtuoso Faceted Browser Copyright © 2009-2024 OpenLink Software