International Journal of Injury Control and Safety Promotion最新文献

Pedestrians are the most vulnerable road users in the transportation system. Understanding pedestrian behaviour and road safety culture is critical for improving traffic safety in developing countries. The primary goal of this 2-fold study is to provide an investigation of pedestrian behaviour in Egypt, a developing country. The first part of this study validated the applying of Pedestrian Behaviour Scale (PBS) to investigate pedestrian behaviour in Egypt. Confirmatory Factor Analysis (CFA) was applied to 533 participants to assess the pedestrian's behavioural factors based on four validated categories: transgressions, lapses, aggressive and positive behaviours. The second part investigated pedestrian crossing behaviour at five different mid-block locations using video recordings. Then, logistic regression models were conducted to investigate pedestrian crossing safety. Males reported more aggressive behaviour than females. Pedestrians previously involved in a collision committed more transgressions and lapses. The presence of forced pedestrian crossing facilities, such as raised pedestrian crossings or traffic signals, significantly reduced the number of unsafe crossings when compared to uncontrolled pedestrian crossings. The aforementioned findings can be used by policymakers to improve road safety programs, create effective traffic safety campaigns, and enact appropriate laws, which could reduce the number of pedestrian-related crashes.

The media plays a key role in shaping the public's perception of road safety. This study analyzes the newspaper coverage and framing of motor vehicle crashes (MVCs) and road safety in Argentina, South America. The content of 304 articles published by 15 newspapers in November 2020 was reviewed. The results show that episodically framed news stories (focused on a single event or incident) prevail over thematically framed articles. MVCs are presented primarily as 'police' events and tend to receive more coverage when fatalities are involved. There is limited information provided on contextual and risk factors, and road safety advice is rarely included. Speeding, infrastructure, alcohol and other human-related variables are the most cited risk factors. Very few articles mention the use of protective devices (seat-belt, helmet and child restraint system). Although motorcyclists represent 40% of RTC deaths in Argentina, only 20% of the news coverage was about them. News coverage was quite similar in national and regional newspapers. There is an opportunity for the media to help build a better road safety culture, but significant changes in news framing are required. Practical recommendations for editors, journalists and road safety practitioners are provided.

With the development of big data technology and the improvement of deep learning technology, data-driven and machine learning application have been widely employed. By adopting the data-driven machine learning method, with the help of clustering processing of data sets, a recurrent neural network (RNN) model based on Keras framework is proposed to predict the injury severity in urban areas. First, with crash data from 2014 to 2017 in Nevada, OPTICS clustering algorithm is employed to extract the crash injury in Las Vegas. Next, by virtue of Keras' high efficiency and strong scalability, the parameters of loss function, activation function and optimizer of the deep learning model are determined to realize the training of the model and the visualization of the training results, and the RNN model is constructed. Finally, on the basis of training and testing data, the model can predict the injury severity with high accuracy and high training speed. The results provide an alternative and some potential insights on the injury severity prediction.

Despite statistics indicating that China has the world's largest taxi industry, there exists limited research about the relationship between workplace health hazards and taxi driver occupational crashes. In this paper, a cross-sectional survey of taxi drivers in four typical Chinese cities was conducted, and data on their self-reported job stress, health status, and daily risky driving behaviours, together with crash involvement experience in the two years before the survey was collected. Three hypotheses were then developed, and they were verified via multivariate analysis of variance (MANOVA) that the seriousness of drivers' health problems and the frequency of their daily risky driving behaviours could be the accurate predictor of their crash risk of taxi drivers. These factors were subsequently substituted in a bivariate negative binomial (BNB) distribution model to determine the joint rate of at-fault taxi drivers' involvement in property-damage-only (PDO) and personal-injury (PI) crashes. The results offer some useful advice for policy development to decrease and prevent professional taxi drivers from causing severe traffic crashes.

This research examines the injury severity of single-vehicle large-truck crashes in Florida while exploring the role of heterogeneity. A random parameter ordered logit (RPOL) model was applied to 27,505 single-vehicle large-truck crashes from 2007 to 2016 in Florida, and the contributing factors were identified. Random parameters and interaction effects were introduced to the model to determine the heterogeneity and its potential sources. The results suggested that driving speed of 76-120 mph and defective tires were the most influential factors in crash injury severity, increasing the probability of severe crashes. Regarding truckers' attributes, asleep or fatigued conditions and driving under the influence were correlated with a higher possibility of severe crashes. Interestingly, the results showed that truckers from outside the state of Florida were less likely to cause severe single-vehicle large-truck crashes compared to their Floridian counterparts. Y-intersections were also found as a high-risk location for single-vehicle large-truck crashes, leading to more severe outcomes. Regarding heterogeneity, the results indicated that the impacts of driving speed (26-50 mph) and light condition (dark - not lighted) significantly varied among the observations, and these variations could be attributed to driver action, vision obstruction, driver distraction, roadway type and roadway alignment.

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.