Evaluation and Spatial Analysis of Road Accidents in Bangladesh: an Emerging and Alarming Issue

Islam, Ashraful; Dinar, Yousuf

doi:10.1007/s40890-021-00118-3

Cited by 16 publications

(4 citation statements)

References 24 publications

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“…Chittagong has been identified as one of the high-risk zones liable for RTI in Bangladesh. 13 Therefore, the present study was designed to explore the pattern of RTI, knowledge of driving rules among drivers and passengers and to do a qualitative investigation among drivers and other stakeholders to find out factors deemed liable behind RTIs.…”

Section: How This Study Might Affect Research Practice or Policymentioning

confidence: 99%

Factors associated with road traffic injury in a high-risk zone of Bangladesh: a mixed-method study

Rozars,

Ahmed,

Sultana

et al. 2024

Inj Prev

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IntroductionRoad traffic injuries are a significant public health issue in low-income and middle-income countries. This study was designed to explore the pattern and factors associated with road traffic injury in a high-risk zone of Bangladesh.MethodThis mixed-method study included a total of 363 road traffic injury victims for the quantitative component, and 10 traffic-related officials and 10 drivers for the qualitative element. Data were collected using a pretested questionnaire, key informant interviews and a focus group discussion using a focus group discussion guide. Quantitative and qualitative analyses were done using Stata V.17 and NVivo V.12, respectively.ResultsMost participants were male, illiterate and young (<30 years) with age averaging 31.50±9.16 years. Of all road traffic injury victims, most had mild (45.18%) injuries, and the least had severe (5.79%) injuries, with head being the most common site (34.44%). The highest proportion of injuries were sustained by motor vehicle drivers (57.58%), followed by cyclists/rickshaw pullers (22.59%) and passengers (19.83%). Most vehicles were new (75.21%), and the rest were old (24.79%). Nearly one-third of the participants did not know about driving rules. The presence of knowledge was associated with less severe injury (p=0.031) compared with the absence of knowledge. The qualitative component of the study identified several factors related to road traffic injury, including driver factors (lack of sleep, bad driving habits and lack of helmets), driving activity factors (ignoring rules, overtaking, crossing speed limits and using bright headlights), road-related factors (broken roads, unplanned curves and angles, the need for spacious streets and the lack of appraisal of previous crash records) and traffic control factors (stringent traffic rules, effective implementation and training on using speed guns).ConclusionThe factors related to road traffic injury identified in this study could be used to plan targeted interventions for road safety improvement.

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Section: How This Study Might Affect Research Practice or Policymentioning

confidence: 99%

Factors associated with road traffic injury in a high-risk zone of Bangladesh: a mixed-method study

Rozars,

Ahmed,

Sultana

et al. 2024

Inj Prev

View full text Add to dashboard Cite

show abstract

“…Rapid population growth in Asia’s developing countries, particularly in South Asia, increased mobility, and increased motorized traffic on high-speed roads, all contribute to the development of RTIs. Almost no day passes without a road traffic accident on their national highways or motorways, resulting in an increasing number of injuries and fatalities and significant economic losses [ 5 , 6 , 7 , 8 , 9 ]. As is the case in other developing countries, Pakistan is increasingly confronted with serious road safety issues.…”

Section: Introductionmentioning

confidence: 99%

Predicting and Analyzing Road Traffic Injury Severity Using Boosting-Based Ensemble Learning Models with SHAPley Additive exPlanations

Dong

Khattak

Ullah

et al. 2022

IJERPH

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Road traffic accidents are one of the world’s most serious problems, as they result in numerous fatalities and injuries, as well as economic losses each year. Assessing the factors that contribute to the severity of road traffic injuries has proven to be insightful. The findings may contribute to a better understanding of and potential mitigation of the risk of serious injuries associated with crashes. While ensemble learning approaches are capable of establishing complex and non-linear relationships between input risk variables and outcomes for the purpose of injury severity prediction and classification, most of them share a critical limitation: their “black-box” nature. To develop interpretable predictive models for road traffic injury severity, this paper proposes four boosting-based ensemble learning models, namely a novel Natural Gradient Boosting, Adaptive Gradient Boosting, Categorical Gradient Boosting, and Light Gradient Boosting Machine, and uses a recently developed SHapley Additive exPlanations analysis to rank the risk variables and explain the optimal model. Among four models, LightGBM achieved the highest classification accuracy (73.63%), precision (72.61%), and recall (70.09%), F1-scores (70.81%), and AUC (0.71) when tested on 2015–2019 Pakistan’s National Highway N-5 (Peshawar to Rahim Yar Khan Section) accident data. By incorporating the SHapley Additive exPlanations approach, we were able to interpret the model’s estimation results from both global and local perspectives. Following interpretation, it was determined that the Month_of_Year, Cause_of_Accident, Driver_Age and Collision_Type all played a significant role in the estimation process. According to the analysis, young drivers and pedestrians struck by a trailer have a higher risk of suffering fatal injuries. The combination of trailers and passenger vehicles, as well as driver at-fault, hitting pedestrians and rear-end collisions, significantly increases the risk of fatal injuries. This study suggests that combining LightGBM and SHAP has the potential to develop an interpretable model for predicting road traffic injury severity.

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“…Unplanned urban growth deteriorates urban dwellers' quality of life by widening the gap between the demand and supply of essential services (Wakode et al, 2014). Under such situations, the urban population gets limited access to critical infrastructure facilities, including health, education, transportation and sanitation (Vermeulen et al, 2015;Saravanan et al, 2016;Islam and Dinar, 2021). Moreover, densely populated urban areas face a much higher risk of rapid transmission of infectious diseases like Covid-19.…”

Section: Introductionmentioning

confidence: 99%

A spatio-temporal assessment and prediction of Ahmedabad’s urban growth between 1990–2030

et al. 2022

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Analyzing long term urban growth trends can provide valuable insights into a city’s future growth. This study employs LANDSAT satellite images from 1990, 2000, 2010 and 2019 to perform a spatiotemporal assessment and predict Ahmedabad’s urban growth. Land Use Land Change (LULC) maps developed using the Maximum Likelihood classifier produce four principal classes: Built-up, Vegetation, Water body, and “Others”. In between 1990–2019, the total built-up area expanded by 130%, 132 km 2 in 1990 to 305 km 2 in 2019. Rapid population growth is the chief contributor towards urban growth as the city added 3.9 km 2 of additional built-up area to accommodate every 100,000 new residents. Further, a Multi-Layer Perceptron — Markov Chain model (MLP-MC) predicts Ahmedabad’s urban expansion by 2030. Compared to 2019, the MLP-MC model predicts a 25% and 19% increase in Ahmedabad’s total urban area and population by 2030. Unaltered, these trends shall generate many socio-economic and environmental problems. Thus, future urban development policies must balance further development and environmental damage.

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Evaluation and Spatial Analysis of Road Accidents in Bangladesh: an Emerging and Alarming Issue

Cited by 16 publications

References 24 publications

Factors associated with road traffic injury in a high-risk zone of Bangladesh: a mixed-method study

Factors associated with road traffic injury in a high-risk zone of Bangladesh: a mixed-method study

Predicting and Analyzing Road Traffic Injury Severity Using Boosting-Based Ensemble Learning Models with SHAPley Additive exPlanations

A spatio-temporal assessment and prediction of Ahmedabad’s urban growth between 1990–2030

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