International Journal of Injury Control and Safety Promotion最新文献

A variety of road hazard perception training programmes have been proposed recently, based on the assumption that these skills contribute to lower crash rates across different countries. However, the long-term effectiveness of suggested programmes has been under-investigated. The main objective of this study is to explore the long-term effectiveness of online hazard perception training for experienced drivers and examine the moderating role of driving self-efficacy. Fifty-six experienced drivers (21 males and 35 females) were assigned to the experimental (n = 31) or the control (n = 25) group. The experimental group received two 45 min session interventions; the control group received no intervention. The effectiveness of the programme was tested by the change in scores of Lithuanian hazard prediction test (HPT) LHP₁₂ that was conducted before training (pre-test), immediately after training (post-test) and six months after training (follow-up). The twelve-item Adelaide Driving Self-Efficacy Scale (ADSES; George et al., 2007) was used to measure self-reported driving self-efficacy at the pre-test. The results revealed a significant increase in hazard prediction scores immediately after training, but the short-term effect of training decayed at follow-up. Experienced drivers with higher self-efficacy developed better hazard prediction skills during training. The results confirmed short-term effectiveness of the programme.

The number of deaths due to road accident is increasing day by day and has become an alarming global problem over the decades. India, with her rising motorization is no stranger to this global catastrophe. In this paper two relatively simple yet powerful and versatile techniques for forecasting time series data, autoregressive integrated moving average method (ARIMA) and exponential smoothing method are used to forecast the number of deaths due to road accidents in India from the year 2022-2031. The results based on the two methods are compared and it is found that they are in sync with each other and pre-existing literature. Furthermore, this is a unique attempt to use two time series analysis techniques on the same data and carry out a comparative analysis. The data was collected from the annual report of Ministry of Road Transport and Highways, India (2020) and Accidental Deaths & Suicides in India (ADSI) Report of National Crime Record Bureau (2021). After examining all the probable models, it is observed that ARIMA (2, 2, 2) model and exponential smoothing (M, A, N) model are suitable for the given data. Amongst the two, ARIMA (2, 2, 2) model has a lower AIC and BIC value. Thus, this comes out to be the best model as per our model selection criterion. Further, the study also reveals an upward trend of number of road accidental deaths for the upcoming 10 years in India.

Understanding of how injuries occur plays an effective role in accident learning and prevention. Existing frameworks focus on crucial information but ignore their causal relationships, which can lead to an incomplete understanding of the injury process. In this study, the descriptive framework of injury data (DFID) is expanded and combined with accident causation models used to elaborate on the causality of each injury factor. Subsequently, the injury process description ontology (IPD-Onto) based on DFID (extension) is established through a seven-step method developed by Stanford University. The IPD-Onto divides injury cases into five unified classes and constructs the injury process through the object properties. The ontology-based description of the injury process (with causal relationships) provides additional description and interpretation capabilities that are understandable by human experts or computers. The results of the Protégé DL query show that the ontology-based method enables the machine to interpret the injury process.

Pedestrian casualties are a severe domestic as well as international problem. This study analyses the spatial distribution of pedestrian casualties to define contributory factors and delineate the means for their prediction. Three years of crash data were collected along with other factors and analysed using kernel density estimation (KDE), spatial autocorrelation (Moran's I), cluster K-Means, spatial regression, and general linear regressions (GLM). Kernel density estimate defines a cluster of pedestrian deaths within 1250 meters. According to Moran's I, 17/22 attributes about casualties, road networks, demographics, and land use have positive values, indicating similar importance clustering. The spatial pattern of pedestrian casualties is random and insignificant and does not change with time. Casualties are negatively related to the surrounding attributes, indicating a tendency towards dispersion. A K-Means analysis of multiple variables revealed that when variables included in the clustering were higher, the variance explanation percentage was lower. In the multi-variable GLM assuming Poisson distribution, the road network length alone or with the house permits combined were the best predictors of casualties. Classic regressions were not significantly enhanced by spatial dimension, and none of the autoregressive coefficients were significant. The predictions from the Poisson-based GLM model are similar to the classic regressions.

The present paper compares motorized two-wheeler (MTW) and passenger car's interactions with the rest of the traffic in urban roads while performing overtaking and filtering maneuvers. To better understand filtering maneuvers of motorcyclists and car drivers, an attempt was made to propose a new measure, i.e. pore size ratio. Additionally, the factors affecting lateral width acceptance for motorcyclists and car drivers while overtaking and filtering were studied using advanced trajectory data. A regression model was developed to predict the significant factors affecting motorcyclist's and car driver's decisions to accept lateral width with the adjacent vehicle while performing overtaking and filtering maneuvers. Finally, a comparative analysis between machine learning and the probit model revealed that, in the present case, machine learning models perform better than the probit model in terms of the model's discernment power. The findings of this study will help ameliorate the power of existing microsimulation tools.

Accidents occur due to a series of interactions between deficiencies within the various levels of a sociotechnical system. Quantifying the relationship between upper and lower levels helps develop accident countermeasures focusing on significant organisational latent conditions. This study explores the relationship between the causal factors of accidents within Ghanaian mines using SEM. Data obtained from the analysis of incident reports using HFACS-GMI were quantified to enable its use in the SEM software, as SEM calculations cannot be done using a 0/1 description. The study also tests five hypotheses, including the basic assumption of the HFACS model. The case study results showed that organisational factors significantly influence workplace/individual conditions; upper causal categories do not only influence adjacent immediate lower causal categories, and partial correlations exist between causal categories with a particular level. Based on the SEM model from LISERL, an accident causation path diagram was developed. The diagram reveals that leadership flaws, the technological environment and adverse physiological/mental states were the mediating factors in accident causation within the mines. The operational process has a prominent position in the organisational factors tier and is an essential factor in the entire accident system. Therefore, accident countermeasures should be directed to addressing operational deficiencies.

Globally, the increase in pedestrian fatalities due to road traffic crashes (RTCs) on transport networks has been a major concern. In low- and middle-income countries (LMICs), pedestrians face a high risk due to RTCs on the rural highway network. The safety evaluation methods, such as observational before-after, empirical Bayes, full Bayes, and cross-sectional methods have been used to identify risk factors of RTCs. However, these methods are data-intensive and have associated limitations. Thus, this study employed a matched case-control method to identify the risk factors of fatal pedestrian crashes. This study utilized crash, traffic volume, speed, geometric, and roadside environment data of a 175 km six-lane rural highway in India. The identified major risk factors, such as clear zone width, the presence of habitation, service roads, and horizontal curve sections, increase the likelihood of a fatal pedestrian crash. This study provides specific insights for modifying the speed limit of highway sections passing through habitation. On such highway sections, designers should shift focus to pedestrian safety. It also suggests that the service road design needs to be reconsidered from a pedestrian safety viewpoint. The proposed method can be used in any other setting having similar traffic and socio-economic conditions.

The article aims to investigate the influence of risk exposure factors on the frequency of road crashes from January to August 2020 in Ciudad Juarez, Mexico. It is a longitudinal study with four data sets: road crashes, population and housing census, location of economic activities, and road network information. Specifically, this study investigates the relationship between exposure factors - demographics, main roads and land use - and road crashes. A mixed method analysis was employed, (1) spatial analysis using GIS techniques; and (2) a negative binomial spatial regression model. The results showed a strong spatial dependence (0.274; p-value 0.00) of road crashes in the census tracts, and this effect was statistically significant (0.007) in the spatial regression model. In the model, a high probability (<0.05) of road crashes in the census tracts was found with the population aged 15 to 65 years, the length of main roads and the level of road coverage (Engel index), land uses with economic activities of an industrial and commercial character. The findings of this study successfully capture the social, economic, and urban conditions during the January-August 2020 period in the context of the COVID-19 pandemic. This new knowledge could help create preventive plans and policies to address the frequency of road crashes.