International Journal of Injury Control and Safety Promotion最新文献

Speeding increases the likelihood and severity of road traffic crashes, but many riders do not consider speeding as a serious safety issue. By using belief-based variables derived from the Theory of Planned Behaviour (i.e. behavioural beliefs, normative beliefs, and control beliefs), this study investigated the socio-cognitive determinants of speeding intentions among Vietnamese adolescents operating motorized/electrified two-wheelers. 189 adolescent powered two-wheeled riders in Ho Chi Minh City participated in a cross-sectional survey. The results lend clear support to the Expectancy-Value approach since belief-based product factors (e.g. outcome beliefs x outcome evaluations) significantly and independently contributed to the prediction of speeding intentions. Speeding intentions were mostly influenced by behavioural beliefs, followed by normative beliefs and control beliefs, respectively. This study not only proves the Expectancy-Value approach as an appropriate framework for the investigation of speeding intentions but also supports authorities in the formulation and execution of more effective interventions for reducing speeding among adolescent powered two-wheeled riders in Vietnam. Instead of motivation-oriented methods, there is a need for strategies that stimulate the translation of good intentions into the desirable behaviour, and encourage adolescents not to relapse in case they are exposed to risk facilitating circumstances. Yet, besides focussing on person-specific dispositions towards speeding, policy makers are advised to adopt a more broadly encompassing systemic approach with inclusion of safe roads, safe vehicles, improved post-crash care, and shared stakeholder responsibilities.

Drawing on the core idea of Propensity Score Matching, this study proposes a new concept named Historical Traffic Violation Propensity to describe the driver's historical traffic violations, and combines the new concept with an improved mutual information-based feature selection algorithm to construct a method for screening key traffic violations from the perspective of expressing driver's accident risk. The validation analysis based on the real data collected in Shenzhen demonstrated that drivers' state of Historical Traffic Violation Propensity on 19 key traffic violations screened have a stronger predictive ability of their subsequent accidents compared to the level in existing research. The positive state of Historical Traffic Violation Propensity on 'Drinking', 'Parking in dangerous areas', 'Wrong use of turn lights', 'Violating prohibited and restricted traffic regulations', and 'Disobeying prohibition sign' will increase the probability of a driver's subsequent accident by more than 1.7 times. The research provides directions to more efficiently and accurately capture the driver's accident risk through historical traffic violations, which is valuable for identifying high-risk drivers as well as the key psychological or physical risk factors that manifest in daily driving activities and lead to subsequent accidents.

China has experienced remarkable achievements in terms of reducing the number of extraordinarily severe traffic crashes (ESTCs) that cause more than 10 deaths each crash. However, ESTCs still occur occasionally and result in extremely adverse social impacts. This study aims at investigating the common characteristics, characteristic patterns, and changes of characteristics of ESTCs in China with the expectation to learn from the past and act for the future. A total of 373 ESTCs occurred in 2004-2019 were collected, and characteristics of driver factors, road factors, vehicle factors, environment factors, and other factors were analyzed through the multiple correspondence analysis (MCA). The results show that run off road crashes, not qualified drivers, improper driving, large bus, overload, class II highway, and straight road sections are the most common categories of characteristics. In addition, four underlying characteristic patterns are identified through the MCA. Significant changes in characteristics and characteristic patterns are also found, and these changes are the results of various law enforcement, safety policies, educational interventions, and engineering interventions. It is also inferred that the specific law enforcement targeting to certain category of characteristics is more effective than the corresponding safety campaigns or policies in terms of ESTC prevention.

Road accidents remain a serious problem and directly affect drivers. Therefore, the perspectives of drivers are important in improving road safety. The objectives of this study are to empirically examine damage due to road accidents using the willingness-to-pay (WTP) approach and to analyze the factors that influence WTP at the driver and district levels. This study obtained data on WTP derived from car drivers across Thailand, which covers 96 districts. The value of statistical life was 824,344 USD per fatality (2,296 million USD annually). The results of Multilevel Structural Equation Modeling revealed a statistically important insight. At the driver level, the Health Belief Model and sociodemographic exert influence on the intention to pay. The demographic factor that has the greatest influence on perceived risk and leads to a high intention to pay is the working age group (γ = 0.826). However, when considering the HBM, perceived susceptibility (γ = 0.901) emerges as the most valuable factor influencing drivers' concerns about road accidents. On the other hand, district-level factors have a negative influence on the intention to pay for road safety measures. Among these factors, the law enforcement (γ = -0.555) practices implemented by local authorities have the most significant impact on drivers' perspectives and intentions regarding WTP. This finding can be used as a guideline for budget allocation and policy recommendation for policymakers in improving road safety according to the area contexts.

Failure to meet road safety targets has necessitated urgent actions from stakeholders worldwide, especially in developing countries like India. Road safety of motorized two-wheelers (MTWs), one of India's most preferred travel modes for urban commutes, is in danger and witnessing threatening figures of fatalities and injuries. Most of the studies in the domain of MTW safety were conducted in developed countries, with very limited research in countries having a significant proportion of MTWs. The present work investigates police-reported crash data to identify the contributory factors of motorized two-wheeler crash severity. Data from MTW crash-prone areas were selected from Delhi, which is leading in road traffic fatalities among the million-plus urban cities in India. A binary logistic regression model was developed using the data for 2016-2018 period. The model results show that the odds of fatal motorized two-wheeler crashes increase when the following circumstances apply: crash occurs on underpasses; involves bus, truck, heavy motor vehicle (lorry, crane) as the striking vehicle; when hit-and-run type of crash occurs and when older age-group (> = 55) riders are involved. Finally, based on the findings, countermeasures were suggested to facilitate policymakers and traffic enforcement agencies, in improving the road safety situation of MTW users.

Road traffic mortalities (RTMs) and injuries are among the leading causes of human fatalities worldwide, particularly in low-and middle-income countries like Iran. Using an interrupted time series analysis, we investigated three interventional points (two government-mandated fuel price increases and increased traffic ticket fines) for their potential relation to RTMs. Our findings showed that while the overall trend of RTMs was decreasing during the study period, multiple individual provinces showed smaller reductions in RTMs. We also found that both waves of government-mandated fuel price increases coincided with decreases in RTMs. However, the second wave coincided with RTM decreases in a smaller number of provinces than the first wave suggesting that the same type of intervention may not be as effective when repeated. Also, increased traffic ticket fines were only effective in a small number of provinces. Potential reasons and solutions for the findings are discussed in light of Iran's Road Safety Strategic Plan.

The distraction affects driving performance and induces serious safety issues. To better understand distracted driving, this study examines the influence of distracted driving on overall driving performance. This paper analyzes the distraction behavior (mobile phone use, entertainment activities, and passenger interference) under three driving tasks. The statistical results show that viewing or sending messages is common during driving. Smoking, phone calls, and talking to passengers are evident in cruising, ride request and drop-off, respectively. Then, overall driving performance is proposed based on velocity, longitudinal acceleration (longacc) and yaw_rate. It is divided into three categories, high, medium, and low, by k-means algorithms. The average speed increases from low to high performance; however, the longacc and yaw_rate decrease. Finally, the influence of distracted driving on overall driving performance is analyzed using C4.5 algorithm. The result shows that when time is peak, the probability of high performance (HP) is higher than off-peak. The possibility of HP increases with the increase of duration; the number of, talking to passengers, listening to music or radio, eating; the duration of, viewing or sending messages, phone calls; but reduces with the increase of the number of phone calls. These findings provide theoretical support for driving performance evaluation.

A lack of research exists concerning the heterogeneity of the occupational injuries of slum dwellers across industries which has a close link with health expenditure and hence livelihood. It necessitates analysing their occupational injuries and associated out-of-pocket health expenditures. Multi-stage random sampling is used to collect the primary data and the logit model is used for data analyses. Permanent non-fatal injuries in the civil-mechanical industries and temporary non-fatal injuries in textile industries are common. The share of health expenditure of the injured workers seeking medical consultations remains 59% of their average monthly income. Average monthly income, parental occupation, types of industry, job security, risk intensity, and salary basis are significant estimates of occupational injuries. The differences in the nature and extent of the occupational injuries of the workers across industries in the light of the socio-demographic and working environment context provide significant insight into the policy implications.