International Journal of Injury Control and Safety Promotion最新文献

The current work presented a comparative analysis of traffic demand and safety skills before and after control measures during the COVID-19 epidemic, acquired time-series change data curves, and constructed a prediction model after determining the trend of traffic demand over time. From a data analysis perspective, the paper draws some interesting conclusions about long span, coarse sampling studies. In terms of the study population, the paper did focus on the specificity of the global epidemic. Kuwait was selected as a case study. Traffic demand analysis was conducted using a Structural Equation Model (SEM), Auto-Regressive Integrated Moving Average (ARIMA), and safety skills questionnaire along with flow charts and demographic variables. These methods were utilized to study the impact of COVID-19 on traffic congestion and safety skills as well as to forecast the future traffic volumes. Results showed that traffic congestion had a significant reduction during COVID-19 as a result of the preventive safety measures taken to control the spread of the virus. Such reduced traffic volume was associated with a decrease in traffic violations and an increase in the safety skills and PM skills of drivers.

In China, bicycles are a popular mode of transportation for senior citizens. A disproportionate number of traffic-related fatalities and injuries involve cyclists. The violation of cycling laws is a significant cause of cyclist crashes. Few studies have analyzed the cycling violation behaviour of seniors. Therefore, it is essential to examine the factors that influence older individuals' intention to engage in cycling violation behaviours. In this study, the effects of social-demographic characteristics, the exogenous constructs in the health belief model (HBM), and the theory of planned behaviour (TPB) on senior cyclists' violation intention were investigated using hierarchical regression analysis. Interviews were conducted with older cyclists in urban areas of Wuhan City, all above 60 years of age. The results showed that very little variance in behavioural intention could be explained by social-demographic factors. The TPB has a significantly greater capacity than the HBM to explain variance in behavioural intention. Perceived susceptibility, perceived benefit, cues to action, subjective norm and attitude significantly impacted behavioural intention, whereas perceived severity, perceived barrier and self-efficacy did not.

Inadequate regional road safety studies have been conducted in developing countries like Iran. Regarding regional road safety indices (RSIs), a significant disparity between Iranian provinces was observed. Thus, it was aimed to evaluate the regional RSIs in Iran and identify their influencing factors and potential hot spots. Data on regional road crashes, fatalities, demographics, transportation, health institutions, economics, education, and fuel consumption rates were collected. The association between the variables was evaluated using correlation analysis. Using Moran's I and local Moran indices, provinces' spatial distributions were evaluated. Hot spot analysis was used to identify factors influencing RSIs. Significant correlations between the variables were detected. A vast local cluster in terms of fatality per injury (as a crash severity index) was identified in the country's southeast. The distribution patterns of provinces in terms of seven RSIs were cluster-like. Variable groups, including road length, demographic, income, education, and geographic, influence RSIs in hot or cold spot regions. Crashes were severe in underdeveloped and remote provinces. Increasing income and education levels make it possible to reduce crash severity indices in this country. A positive Moran's I index does not guarantee the existence of significant local cluster cores in a country.

Drivers traversing the horizontal curves are expected to complete the deceleration manoeuvre on the tangent and transition curve and maintain a constant speed upon reaching the curve. However, this may not be true for the horizontal curves constituting a two-lane undivided rural highway passing through mountainous terrain. The objective of this study is to investigate the speed variability on a two-lane rural highway passing through mountainous terrain and to identify its determinants. The continuous speed profiles of vehicles traversing the curves were extracted using the video image processing technique. Individual speed profiles, as well as the operating speed profiles obtained through quantile regression, indicate a significant speed variability on the horizontal curve. Speed variability on the curve was modelled in terms of the 85^th percentile of maximum speed difference (MaxΔ₈₅V) using the Robust Weighted Least Square (RWLS) Method. The findings indicate that the curvature change rate, length of the curve and the speed at the point of curvature affect the maximum speed difference on a curve. The findings also suggest that the operating speed estimated based on the spot speed data collected at the curve centre might lead to erroneous estimation of design and operating speed consistencies.

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.