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http://hdl.handle.net/1942/42356
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DC Field | Value | Language |
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dc.contributor.author | Zahid, Muhammad | - |
dc.contributor.author | Habib, Muhammad | - |
dc.contributor.author | Ijaz, Muhammad | - |
dc.contributor.author | Ameer, Iqra | - |
dc.contributor.author | Ullah, Irfan | - |
dc.contributor.author | AHMED, Tufail | - |
dc.contributor.author | He, Zhengbing | - |
dc.date.accessioned | 2024-02-09T09:01:14Z | - |
dc.date.available | 2024-02-09T09:01:14Z | - |
dc.date.issued | 2024 | - |
dc.date.submitted | 2024-01-30T17:37:23Z | - |
dc.identifier.citation | Traffic Injury Prevention, | - |
dc.identifier.issn | 1538-9588 | - |
dc.identifier.uri | http://hdl.handle.net/1942/42356 | - |
dc.description.abstract | Objective: Motorcycle crashes often result in severe injuries on roads that affect people's lives physically, financially, and psychologically. These injuries could be notably harmful to drivers of all age groups. The main objective of this study is to investigate the risk factors contributing to the severity of crash injuries in different age groups. Methods: This Objective is achieved by developing accurate machine learning (ML) based prediction models. This research examines the relationship between potential risk factors of motorcycle-associated crashes using (ML) and Shapley Additive explanations (SHAP) technique. The SHAP technique further helped interpreting ML methods for traffic injury severity prediction. It indicates the significant non-linear interactions between dependent and independent variables. The data for this study was collected from the Provincial Emergency Response Service RESCUE 1122 for the Rawalpindi region (Pakistan) over three years (from 2017 to 2020). The Synthetic Minority Oversampling Technique (SMOTE) is employed to balance injury severity classes in the pre-processing phase. Results: The results demonstrate that age, gender, posted speed limit, the number of lanes, and month of the year are positively associated with severe and fatal injuries. This research also assesses how the modeling framework varies between the ML and classical statistical methods. The predictive performance of proposed ML models was assessed using several evaluation metrics, and it is found that Catboost outperformed the XGBoost, Random Forest (RF) and Multinomial Logit (MNL) model. Conclusion: The findings of this study will assist road users, road safety authorities, stakeholders, policymakers, and decision-makers in obtaining substantial and essential guidance for reducing the severity of crash injuries in Pakistan and other countries with prevailing conditions. | - |
dc.language.iso | en | - |
dc.publisher | TAYLOR & FRANCIS INC | - |
dc.subject.other | Machine learning (ML) | - |
dc.subject.other | Catboost | - |
dc.subject.other | shapley additive explanations (SHAP) | - |
dc.subject.other | traffic safety | - |
dc.subject.other | injury severity | - |
dc.title | Factors affecting injury severity in motorcycle crashes: Different age groups analysis using Catboost and SHAP techniques | - |
dc.type | Journal Contribution | - |
dc.identifier.epage | 10 | - |
dc.identifier.spage | 1 | - |
local.format.pages | 10 | - |
local.bibliographicCitation.jcat | A1 | - |
dc.description.notes | Habib, MF (corresponding author), North Dakota State Univ NDSU, Upper Great Plains Transportat Inst UGPTI, Fargo, ND 58105 USA. | - |
dc.description.notes | faisal.habib@ndsu.edu | - |
local.publisher.place | 530 WALNUT STREET, STE 850, PHILADELPHIA, PA 19106 USA | - |
local.type.refereed | Refereed | - |
local.type.specified | Article | - |
local.bibliographicCitation.status | Early view | - |
dc.identifier.doi | 10.1080/15389588.2023.2297168 | - |
dc.identifier.pmid | 38261528 | - |
dc.identifier.isi | 001147142100001 | - |
dc.identifier.eissn | 1538-957X | - |
local.provider.type | - | |
local.description.affiliation | [Zahid, Muhammad] Ecole Polytech Montreal, Dept Civil Geol & Min Engn, Montreal, PQ, Canada. | - |
local.description.affiliation | [Habib, Muhammad Faisal] North Dakota State Univ NDSU, Upper Great Plains Transportat Inst UGPTI, Fargo, ND 58105 USA. | - |
local.description.affiliation | [Ijaz, Muhammad] Southwest Jiaotong Univ, Sch Transportat & Logist, Chengdu, Peoples R China. | - |
local.description.affiliation | [Ameer, Iqra] Penn State Univ, Div Sci & Engn, Abington, PA USA. | - |
local.description.affiliation | [Ullah, Irfan] Dalian Maritime Univ, Transportat Engn Coll, Dalian, Peoples R China. | - |
local.description.affiliation | [Ullah, Irfan] ILMA Univ, Dept Business & Adm, Karachi, Pakistan. | - |
local.description.affiliation | [Ahmed, Tufail] Hasselt Univ, Transportat Res Inst IMOB, Hasselt, Belgium. | - |
local.description.affiliation | [He, Zhengbing] Massachusetts Inst Technol MIT, Senseable City Lab, Cambridge, MA USA. | - |
local.uhasselt.international | yes | - |
item.fulltext | With Fulltext | - |
item.fullcitation | Zahid, Muhammad; Habib, Muhammad; Ijaz, Muhammad; Ameer, Iqra; Ullah, Irfan; AHMED, Tufail & He, Zhengbing (2024) Factors affecting injury severity in motorcycle crashes: Different age groups analysis using Catboost and SHAP techniques. In: Traffic Injury Prevention,. | - |
item.accessRights | Restricted Access | - |
item.contributor | Zahid, Muhammad | - |
item.contributor | Habib, Muhammad | - |
item.contributor | Ijaz, Muhammad | - |
item.contributor | Ameer, Iqra | - |
item.contributor | Ullah, Irfan | - |
item.contributor | AHMED, Tufail | - |
item.contributor | He, Zhengbing | - |
crisitem.journal.issn | 1538-9588 | - |
crisitem.journal.eissn | 1538-957X | - |
Appears in Collections: | Research publications |
Files in This Item:
File | Description | Size | Format | |
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Factors affecting injury severity in motorcycle crashes.pdf Restricted Access | Published version | 1.68 MB | Adobe PDF | View/Open Request a copy |
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