Literature DB >> 32535248

Unobserved heterogeneity and the effects of driver nationality on crash injury severities in Saudi Arabia.

Asim Alogaili1, Fred Mannering2.   

Abstract

This paper investigates factors that significantly contribute to the injury severity of different drivers of different nationality backgrounds. Using the data from Riyadh, Saudi Arabia, a random parameters multinomial logit model of driver-injury severity was estimated to explore the effects of a wide range of variables on driver injury-severity outcomes. With three possible outcomes (no injury, injury, fatality), only single-vehicle crashes are considered and crashes involving domestic (Saudi) and international (non-Saudi) drivers were modeled separately. Model estimation results show that a wide range factors significantly affect the injury severity outcomes in single-vehicle crashes including driver attributes (such as nationality and age), vehicle characteristics (such as make, model and year of manufacture), driver actions (such as speeding and preoccupation on driving), and other factors (such as location and time of the accident); and that the influence that these variables have on injury-severity probabilities vary considerably between Saudi and non-Saudi drivers. While Saudi Arabia is rather unique because of the large numbers of non-national drivers, the results suggest that different nationalities, with their different cultural, educational and, behavioral backgrounds, may affect risk-taking behavior and resulting crash-injury severities.
Copyright © 2020 Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  Saudi Arabia; crash injury severity; nationality influences; random parameters logit

Year:  2020        PMID: 32535248     DOI: 10.1016/j.aap.2020.105618

Source DB:  PubMed          Journal:  Accid Anal Prev        ISSN: 0001-4575


  2 in total

1.  Monitoring Road Accidents and Injuries Using Variance Chart under Resampling and Having Indeterminacy.

Authors:  Muhammad Aslam; Mohammed Albassam
Journal:  Int J Environ Res Public Health       Date:  2021-05-14       Impact factor: 3.390

2.  Crash severity analysis of vulnerable road users using machine learning.

Authors:  Md Mostafizur Rahman Komol; Md Mahmudul Hasan; Mohammed Elhenawy; Shamsunnahar Yasmin; Mahmoud Masoud; Andry Rakotonirainy
Journal:  PLoS One       Date:  2021-08-05       Impact factor: 3.240

  2 in total

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