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http://hdl.handle.net/1942/39316
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DC Field | Value | Language |
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dc.contributor.author | Outay, Fatma | - |
dc.contributor.author | ADNAN, Muhammad | - |
dc.contributor.author | Gazder, Uneb | - |
dc.contributor.author | Baqueri, Syed Fazal Abbas | - |
dc.contributor.author | Awan, Hammad Hussain | - |
dc.date.accessioned | 2023-01-24T10:34:25Z | - |
dc.date.available | 2023-01-24T10:34:25Z | - |
dc.date.issued | 2023 | - |
dc.date.submitted | 2023-01-19T12:19:43Z | - |
dc.identifier.citation | International Journal of Injury Control and Safety Promotion, 30 (2) , p. 282-293 | - |
dc.identifier.issn | 1745-7300 | - |
dc.identifier.uri | http://hdl.handle.net/1942/39316 | - |
dc.description.abstract | Motorcycle accident studies usually rely upon data collected from road accidents collected through questionnaire surveys/police reports including characteristics of motorcycle riders and contextual data such as road environment. The present study utilizes big data, in the form of vehicle trajectory patterns collected through GPS, coupled with self-reported road accident information along with motorcycle rider characteristics to predict the likelihood of involvement of a motorcyclist in an accident. Random Forest-based machine learning algorithm is employed by taking inputs based on a variety of features derived from trajectory data. These features are mobility-based features, acceleration event-based features, aggressive overtaking event-based features and motorcyclists socio-economic features. Additionally, the relative importance of features is also determined which shows that aggressive overtaking event-based features have more impact on motorcycle accidents as compared to other categories of features. The developed model is useful in identifying risky motorcyclists and implementing safety measures focused towards them. | - |
dc.description.sponsorship | This research was supported by Zayed University Research Cluster grant #R17075. Authors acknowledge the support provided by Mr. Hakeem Ahmed (Manager, Motorcycle Safety Program, Karachi, Pakistan), to provide access to trajectory data of motorcyclists along with their relevant information and accident statistics. | - |
dc.language.iso | en | - |
dc.publisher | TAYLOR & FRANCIS LTD | - |
dc.rights | 2023 Informa UK Limited, trading as Taylor & Francis Group | - |
dc.subject.other | Naturalistic driving based big data | - |
dc.subject.other | motorcycle accident prediction | - |
dc.subject.other | random forest | - |
dc.subject.other | machine learning | - |
dc.subject.other | Karachi | - |
dc.title | Random forest models for motorcycle accident prediction using naturalistic driving based big data | - |
dc.type | Journal Contribution | - |
dc.identifier.epage | 293 | - |
dc.identifier.issue | 2 | - |
dc.identifier.spage | 282 | - |
dc.identifier.volume | 30 | - |
local.bibliographicCitation.jcat | A1 | - |
dc.description.notes | Gazder, U (corresponding author), Univ Bahrain, Dept Civil Engn, Zallaq, Bahrain. | - |
dc.description.notes | ugazder@uob.edu.bh | - |
local.publisher.place | 2-4 PARK SQUARE, MILTON PARK, ABINGDON OR14 4RN, OXON, ENGLAND | - |
local.type.refereed | Refereed | - |
local.type.specified | Article | - |
dc.identifier.doi | 10.1080/17457300.2022.2164310 | - |
dc.identifier.pmid | 36597803 | - |
dc.identifier.isi | 000907235600001 | - |
dc.contributor.orcid | Gazder, Uneb/0000-0002-9445-9570; OUTAY, Fatma/0000-0002-9300-6270; | - |
dc.contributor.orcid | Adnan, Muhammad/0000-0002-1386-2932; Awan, Hammad | - |
dc.contributor.orcid | Hussain/0000-0002-5654-2074 | - |
dc.identifier.eissn | 1745-7319 | - |
local.provider.type | wosris | - |
local.description.affiliation | [Outay, Fatma] Zayed Univ, Coll Technol Innovat CTI, Dubai, U Arab Emirates. | - |
local.description.affiliation | [Adnan, Muhammad] Hasselt Univ, Transportat Res Inst IMOB, Hasselt, Belgium. | - |
local.description.affiliation | [Gazder, Uneb] Univ Bahrain, Dept Civil Engn, Zallaq, Bahrain. | - |
local.description.affiliation | [Baqueri, Syed Fazal Abbas] DHA Suffah Univ, Dept Civil Engn, Karachi, Pakistan. | - |
local.description.affiliation | [Awan, Hammad Hussain] Univ Lahore, Islamabad Campus, Islamabad, Pakistan. | - |
local.uhasselt.international | yes | - |
item.fulltext | With Fulltext | - |
item.fullcitation | Outay, Fatma; ADNAN, Muhammad; Gazder, Uneb; Baqueri, Syed Fazal Abbas & Awan, Hammad Hussain (2023) Random forest models for motorcycle accident prediction using naturalistic driving based big data. In: International Journal of Injury Control and Safety Promotion, 30 (2) , p. 282-293. | - |
item.accessRights | Open Access | - |
item.contributor | Outay, Fatma | - |
item.contributor | ADNAN, Muhammad | - |
item.contributor | Gazder, Uneb | - |
item.contributor | Baqueri, Syed Fazal Abbas | - |
item.contributor | Awan, Hammad Hussain | - |
crisitem.journal.issn | 1745-7300 | - |
crisitem.journal.eissn | 1745-7319 | - |
Appears in Collections: | Research publications |
Files in This Item:
File | Description | Size | Format | |
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Auteursversie.pdf | Peer-reviewed author version | 964.42 kB | Adobe PDF | View/Open |
Random forest models for motorcycle accident prediction using naturalistic driving based big data.pdf Restricted Access | Published version | 1.72 MB | Adobe PDF | View/Open Request a copy |
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