A Bayesian analysis of the impact of post-crash care on road mortality in Sub-Saharan African countries

Soro, Wonmongo Lacina; Wayoro, Didier

doi:10.1016/j.iatssr.2017.01.001

Cited by 9 publications

(3 citation statements)

References 31 publications

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“…Informative priors depend on previous studies, expert knowledge, data recovered expressly from past information analysis, or both (50). In contrast, noninformative priors are vague priors often used in the absence of informative priors for the model parameters (3,(52)(53)(54). Noninformative priors were used in this study because there was no prior knowledge of the expected effect.…”

Section: Bayesian Model Frameworkmentioning

confidence: 99%

Influence of Built Environment on Pedestrian Crashes: A Case Study of Miami-Dade County

Soto¹,

Kitali

Raihan

et al. 2022

Transportation Research Record

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The objective of this study was to identify pedestrian crash hotspots and assess the built environment to identify factors that increase the probability of a roadway corridor becoming a hotspot. Therefore, this research attempted to identify the pedestrian hotspots and then answer the question, do locations with a “specific built environment” have a higher probability of becoming a pedestrian crash hotspot? The study used 4 years of pedestrian crash data (2011 to 2014) from Miami-Dade County, one of the top three leading counties in the United States with the highest pedestrian fatalities. Pedestrian crash hotspots were first identified in the ArcGIS environment, integrating spatial analysis. The Bayesian complementary log-log (cloglog) model was then used to develop a hotspot risk prediction model, in which the likelihood of a corridor becoming a hotspot was linked to the built environment, and to demographic and socioeconomic factors. The density of bus stops, shopping centers, healthcare facilities, hotels, alcohol sales establishments, households without vehicles, traffic volume, presence of sidewalks, and presence of medians were found to significantly increase the likelihood of a corridor becoming a pedestrian crash hotspot. The methodological framework and findings of this study could be used while developing site-specific proactive measures to improve pedestrian safety.

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Section: Bayesian Model Frameworkmentioning

confidence: 99%

Influence of Built Environment on Pedestrian Crashes: A Case Study of Miami-Dade County

Soto¹,

Kitali

Raihan

et al. 2022

Transportation Research Record

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“…When establishing the Bayesian network model, the researchers have comprehensively considered the decision variables of solving the problem and the relationship among various factors. ey have used the reasoning ability of BN to analyze the multiattribute decision-making problem in an uncertain environment [37].…”

Section: Introductionmentioning

confidence: 99%

Critical Factors Analysis of Severe Traffic Accidents Based on Bayesian Network in China

Chen

Yang

2020

Journal of Advanced Transportation

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The purpose of this study is to minimize the negative influences of the severe traffic accidents in China by profoundly analyzing the complex coupling relations among accident factors contributing to the single-vehicle and multivehicle traffic accidents with the Bayesian network (BN) crash severity model. The BN model was established by taking the critical factors identified with the improved grey correlation analysis method as node variables. The severe traffic accident data collected from accident reports published in China were used to validate this model. The model’s efficiency was validated objectively by comparing the conditional probability obtained by this model with the actual value. The result shows that the BN model can reflect the real relations among factors and can be seen as the target network for the severe traffic accidents in China. Besides, based on BN’s junction tree engine, five-factor combination sequences for the number of deaths and three-factor combination sequences for the number of injuries were ranked according to the severity degree to reveal the critical reasons and reduce the massive traffic accidents damage.

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“…Sustainable Development Goal (SDG) 3.6, calls for a halving of the number of global deaths and injuries from road traffic crashes. While preventive measures can decrease the number of RTCs, it is also important to invest in post-crash care to save more lives (Soro and Wayoro, 2017). Yet, in Sub-Saharan Africa (SSA), where the problem is most acute, very few countries have developed systematic and sustainable Emergency Medical Services (EMS) systems at scale (World Bank, 2021).…”

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confidence: 99%

Trauma Incidence and Emergency Medical Services in Malawi

2022

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Some rights reserved.This work is a product of contributions by staff of The World Bank. The findings, interpretations, and conclusions expressed in this work do not necessarily reflect the views of The World Bank, its Board of Executive Directors, or the governments they represent. The World Bank does not guarantee the accuracy of the data included in this work. The boundaries, colors, denominations, and other information shown on any map in this work do not imply any judgment on the part of The World Bank concerning the legal status of any territory or the endorsement or acceptance of such boundaries. Nothing herein shall constitute or be considered to be a limitation upon or waiver of the privileges and immunities of The World Bank, all of which are specifically reserved.

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A Bayesian analysis of the impact of post-crash care on road mortality in Sub-Saharan African countries

Cited by 9 publications

References 31 publications

Influence of Built Environment on Pedestrian Crashes: A Case Study of Miami-Dade County

Influence of Built Environment on Pedestrian Crashes: A Case Study of Miami-Dade County

Critical Factors Analysis of Severe Traffic Accidents Based on Bayesian Network in China

Trauma Incidence and Emergency Medical Services in Malawi

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