International Journal of Injury Control and Safety Promotion最新文献

Pedestrians are the most vulnerable road users and pedestrian crashes are a major concern both for their number and their severity. In Italy, pedestrians account for 34% of the road fatalities in urban area. To improve pedestrian safety, this study is aimed at analysing the roadway, environmental, vehicle, driver and pedestrian-related factors that are associated with fatal pedestrian crashes in Italy and providing insights for the development of effective countermeasures. This study used an econometric model, the mixed logit model, and a machine learning algorithm, the association rules, to analyse 101,032 pedestrian crashes that occurred in Italy. Study results identified several factors associated with fatal pedestrian crashes. The mixed logit identified 46 significant indicator variables (1 with random parameter), and the association rules provided 119 valid rules. F-measure and G-mean showed higher prediction performance of the mixed logit over the association rules. Study results recommend using both models as complementary approaches since their combination is effective in providing meaningful insights about pedestrian crash contributory factors and their interdependencies. To address the contributory factors identified by the study, behavioural/engineering pedestrian safety countermeasures are recommended. The findings provided new insights for transportation agencies to develop effective countermeasures for pedestrian safety improvement.

A better understanding of the underlying factors to the choice of seatbelt use could contribute to the policy solutions, which consequently enhance the rate of seatbelt usage. To achieve that goal, it is important to obtain unbiased and reliable results by employing a valid statistical technique. In this paper, the latent class (LC) model was extended to account for unobserved heterogeneity across parameters within the same class. The random parameter latent class, or mixed-mixed (MM) model, is an extension of the mixed and LC models by adding another layer to the LC model, with an objective of accounting for heterogeneity within a same class. The results indicated that although the LC model outperformed the mixed model, the standard LC model did not account for the whole heterogeneity in the dataset and adding an extra layer for changing the parameter across the observations result in an improvement in a model fit. The results indicated that seatbelt status of the driver, vehicle type, day of a week, and driver gender are some of factors impacting whether or not passengers would wear their seatbelts. It was also observed that accounting for day of a week, drivers' gender, and type of vehicle heterogeneities in the second layer of the MM model result in a better fit, compared with the LC technique. The results of this study expand our understanding about factors to the choice of seatbelt use while capturing extra heterogeneity of the front-seat passengers' choice of seatbelt use. This is one of the earliest studies implemented the technique in the context of the traffic safety, with individual-specific observations.

Safety is a critical concern in underground mining. Operations should be designed according to a vision of zero accident and fatality. A refuge chamber is a station where worker lives are maintained throughout an emergency until they are saved. Given that workers perform their duties in different areas of underground, the number of refuge chambers and their locations are of paramount importance as workers must be able to access them rapidly. Furthermore, the task of refuge chamber localization needs to consider the fact that the number of workers in a different area and the structural characteristics and stability conditions of these areas will not be identical. This paper aims to determine the optimal locations of the refuge chambers in the underground network considered as a tree. Initially, a formula quantifying the importance of each worksite is developed. Then, two main strategies are presented as feasible localization solutions: A recursive solution to the 'Absolute Weighted P-centre Problem' and a spiral approach to the 'Total Covering problem'. The functionalities of the proposed approaches are demonstrated by case studies. Results show that the proposed methods can find viable locations for refuge chambers while fulfilling varied maximum safety distances of each working area.

Cooking-related child burn injury causes a greater health burden in low-and-middle-income countries. Therefore, a community-based cross-sectional study was conducted among 5830 under-five-years old children in a resource-limited community in Northwest Ethiopia to determine the prevalence and risk factors of this child health problem. Data were collected by trained nurses using a questionnaire and the logistic regression analysis method was applied to identify factors linked with burn injury. Injury prevalence was 6.2% (95% CI:5.5-6.8); and this burden was linked with several risk factors such as lower literacy status of caretakers [AOR = 2.21 (95% CI:1.05-4.67)], overcrowding [AOR = 2.35(95% CI:1.25-4.43], lack of separate kitchen [AOR =2.19 (95% CI:1.56-3.07)], using traditional cookstove [AOR = 2.04 (95% CI:1.23-3.36)], and lack of child supervision [AOR = 2.27 (95% CI:1.63-3.17)]. In conclusion, children experience a high burden of burn injury. Thus, stakeholders should work to reduce child burn injury by modifying the aforementioned risk factors.

Road traffic injuries cost countries 3% of their annual GDP. In developing countries like India, every year around 150,000 people die on roads. The type of vehicles involved in a crash contribute majorly to the outcome of casualty (injury/death). Barring few studies, literature are less regarding the role of vehicle as perpetrator and victim on road crash fatalities. Historical crash data has been used in the present study to examine the role of vehicles (both as perpetrator & victim). The study reveals that victim's effect is more as compared to perpetrator/accused for determining the outcome of crash. Heavy vehicles as perpetrator, and self-hitting vehicles along with pedestrians as victims have higher fatality rates. Binary logistic regression and artificial neural network (ANN) has been utilized for developing prediction models. Binary logistic model predicted around 75% of outcomes correctly with default cut-off value (0.5). However, based on reported crash data, where 19% of total crashes lead to deaths, 0.19 has been proposed as cut-off value which increases the accuracy of the predictions. Accuracy of ANN technique directly depends on the number of crashes reported for a definite pair of perpetrator and victim and the type of validation technique used (Holdback/K-Fold) along with the type of hidden layer chosen for the study based on different types of sigmoid activation function. ROC curves in ANN suggest that the analysis can predict 75% of the outcomes which can be increased by deleting the pairs of vehicles which are present/have occurred in very less number. A comparison has been made between the two techniques based on their advantages and limitations. The developed models can be used as safety indicators based on composition of traffic flow on urban roads.

Animal vehicle crash is a critical yet often under-emphasized safety concern of Louisiana. During 2014-2018, over 14,000 animal-related crashes cost Louisiana more than $520 million. To identify multiple key contributing factors and their association patterns, this study applied association rules mining in the dataset of animal-related roadway crashes that occurred during 2014-2018. Since high proportions of animal-related crashes involve complaint and no injury of vehicle occupants, separate analyses were performed for KAB (fatal, severe, and moderate injury) and CO (possible/complaint and no injury) crashes. Top rules ordered by higher lift values were interpreted and compared to implicate the quantified likelihood of crash patterns. KAB rules presented the likelihood of associations of characteristics such as unlighted dark conditions, interstate and parish roads, a wide range of speed limits, residential and open country locations, normal and rainy weather conditions, light trucks, young drivers, etc. The majority of CO crash patterns were associated with interstates, straight segments, normal driver conditions, clear weather, unlighted dark conditions, open country locations, a speed limit of 97 km/h or higher, etc. Findings in this study and their implications supported by prior studies are expected to be beneficial in strategic planning for identifying implementable countermeasures for animal-vehicle crashes.

Road safety audit (RSA) is a method used to proactively identify safety risks in road projects from planning, design, and construction stages. The effectiveness of the implementation of RSAs varies across the globe. This paper provides an understanding of road safety auditor's opinions and attitudes by using data collected from an online questionnaire filled by 408 road safety experts with varying demographic and technical backgrounds. Results demonstrated that majority of respondents agreed that RSA is an effective tool to improve road safety. Compared to high-income countries, respondents from low- and middle-income countries had less knowledge about RSA and less access to RSA guidelines. The results also indicate that many road safety auditors have a misunderstanding of the independence requirement for conducting RSA. The results of this paper can be used to inform and develop improved guidance to improve RSA generally and promote RSA as an effective road safety engineering tool.

A driving simulator study was utilized to evaluate a potential countermeasure for speeding behaviour in order to reduce speed spillover. The behaviour of 56 subjects was observed. Participants drove a freeway for 5300 metres before exiting onto a three-kilometre urban arterial. Field data from trajectory speed profiles validated the diving simulator results. The impact of the Crash Fact Sign (CFS), a sign that provides information about the number of crashes/fatalities that occurred on that particular road, and Warning Sign (W.S.) on regions affected by speed spillover was investigated. Each subject was asked to drive in four different scenarios: (1) an additional speed limit sign (SLS-2); (2) Warning Sign (W.S.); (3) Crash Fact Sign (CFS); and (4) Crash Fact Sign with Additional Speed Limit Sign (CFS&SLS-2). The study finds CFS&SLS-2 to be the most effective countermeasure because it produces significant average speed reductions, reached 7.8 km, in the area under speed spillover effect. Furthermore, the effect of a traffic signal at the exit urban arterial on speed spillover behaviour was investigated. The speed spillover effect was observed at longer distances when drivers did not stop at the signalized intersection than when drivers stopped.