Literature DB >> 20496189

Analysis of fatal road traffic crashes in Ghana.

Williams Ackaah1, David O Adonteng.   

Abstract

The major objective of this study was to identify the risk factors associated with fatal road traffic crashes (RTCs) and to propose remedial measures to address them. Fatal RTC data for the period 2005-2007 in Ghana were analysed using the Micro-computer Accident Analysis Package (MAAP) software. Other transport-related research works were reviewed and incorporated in the article. The study showed that pedestrians accounted for 42% of all road traffic fatalities and nearly one-third (33%) of these crashes occurred during the early night-time hours. Children alone constituted almost one-third of all pedestrian fatalities. The occupants of goods vehicles accounted for 12% of all road traffic fatalities although goods vehicles constitute just about 9% of the total motor vehicle population in Ghana. Pedestrians, especially children bear a disproportionately high share of road traffic fatalities in Ghana. The risk of being killed as a pedestrian in traffic is exacerbated during night time. Excessive vehicular speeds, inappropriate use of goods vehicles for passenger transport, excessive loading and inadequate trauma care are the key contributory risk factors to the high number of road traffic fatalities. Concerted efforts spanning education, engineering, enforcement and trauma care are needed to stem the rise in fatal crashes in Ghana.

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Year:  2011        PMID: 20496189     DOI: 10.1080/17457300.2010.487157

Source DB:  PubMed          Journal:  Int J Inj Contr Saf Promot        ISSN: 1745-7300


  5 in total

1.  Risk factors for fatal and nonfatal road crashes in iran.

Authors:  Mohammadreza Mehmandar; Hamid Soori; Mosa Amiri; Reza Norouzirad; Mehdi Khabzkhoob
Journal:  Iran Red Crescent Med J       Date:  2014-08-05       Impact factor: 0.611

2.  Analyzing Factors Associated with Fatal Road Crashes: A Machine Learning Approach.

Authors:  Ali J Ghandour; Huda Hammoud; Samar Al-Hajj
Journal:  Int J Environ Res Public Health       Date:  2020-06-09       Impact factor: 3.390

3.  A comparative study on machine learning based algorithms for prediction of motorcycle crash severity.

Authors:  Lukuman Wahab; Haobin Jiang
Journal:  PLoS One       Date:  2019-04-04       Impact factor: 3.240

4.  Exploring the Injury Severity Risk Factors in Fatal Crashes with Neural Network.

Authors:  Arshad Jamal; Waleed Umer
Journal:  Int J Environ Res Public Health       Date:  2020-10-14       Impact factor: 3.390

Review 5.  The burden of road traffic crashes, injuries and deaths in Africa: a systematic review and meta-analysis.

Authors:  Davies Adeloye; Jacqueline Y Thompson; Moses A Akanbi; Dominic Azuh; Victoria Samuel; Nicholas Omoregbe; Charles K Ayo
Journal:  Bull World Health Organ       Date:  2016-04-21       Impact factor: 9.408

  5 in total

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