Assessing heterogeneity in factors influencing three-wheeled motorized rickshaws crash outcomes between weekdays and weekends.

Zia Ur Rehman, Jiang Chaozhe, Emmanuel Kofi Adanu, Arshad Jamal, Yahya Almarhabi, Md Kamrul Islam, Hassan M Al-Ahmadi

Abstract read

In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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1citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

Modeling Farm Equipment Vehicle crash injury severity using random parameters logit and multi-class support vector machine in a developing country.Scientific reports · 2026
Article

4 · The record

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5 · Who and what money

Authors and funding

7 authors.

Zia Ur RehmanSchool of Transportation and Logistics, Southwest Jiaotong University, Chengdu, 610031, Sichuan, China.

Jiang ChaozheSchool of Transportation and Logistics, Southwest Jiaotong University, Chengdu, 610031, Sichuan, China. jiangchaozhe@swjtu.edu.cn.

Emmanuel Kofi AdanuDepartment of Civil, Construction and Environmental Engineering, University of Alabama, Tuscaloosa, USA.

Arshad JamalDepartment of Civil Engineering, College of Engineering, Qassim University, Buraydah, 51452, Saudi Arabia. a.jamal@qu.edu.sa.

Yahya AlmarhabiDepartment of Surgery, Faculty of Medicine, King Abdulaziz University, Jeddah, 21589, Saudi Arabia.

Md Kamrul IslamDepartment of Civil and Environmental Engineering, College of Engineering, King Faisal University, Al Ahsa, 31982, Saudi Arabia. maislam@kfu.edu.sa.

Hassan M Al-AhmadiDepartment of Civil and Environmental Engineering, Interdisciplinary Research Center for Smart Mobility and Logistics, King Fahd University of Petroleum and Minerals, Dhahran, 31261, Saudi Arabia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Traffic crashes involving three-wheeled motorized rickshaws (3-WMRs) pose considerable public health risk in developing countries. Although prior research has thoroughly investigated the effects of risk factors such as weather, road conditions, and driver characteristics on 3-WMR collisions, the effect of the day of the week (weekday versus weekend) has been relatively underexplored. The study uniquely identifies the temporal heterogeneity in 3-WMR injury severity risk factors between weekday and weekend crashes. By integrating psychological and sociological theories, it also aims to understand how risk perception and sensation seeking behaviors contribute to various injury outcomes in 3-WMR crashes. The current study aims to fill this gap by investigating the risk factors contributing to injury severity in such crashes, employing a random parameters multinomial logit model with heterogeneity in means and variance. The study conducted an empirical analysis using traffic crash data spanning six years (2017-2022) from RESCUE 1122 in Rawalpindi, Pakistan. The findings identify several statistically significant factors that influence major injuries, including single-vehicle crashes, teenage drivers, rainy weather, peak traffic hours, wrong U-turns, speeding, and driver distraction. The study's findings demonstrated the importance of considering temporal heterogeneity, thereby offering differentiated policy recommendations for weekends and weekdays, ensuring more tailored and effective safety interventions for 3-WMR users.

Indexed as

Accidents, TrafficAutomobile DrivingMotorcyclesWounds and InjuriesAdolescentAdultFemaleHumansMaleMiddle AgedPakistanRisk FactorsTime FactorsWeatherYoung Adult3-wheeled motorized rickshawsInjury severityMultinomial logit modelRisk factorsTraffic safetyUnobserved heterogeneity

Identifiers

PMID40269111

DOI10.1038/s41598-025-97847-x

PMCPMC12019157

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LicenceCC BY-NC-ND

Read underepoch 390

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.