International Journal of Injury Control and Safety Promotion最新文献

This study systematically explores the cause of the increase in single-vehicle speeding crash injury severities in California during and after the stay-at-home order. 27,696 speeding crashes on both highways and non-highways before-and-after the order are selected from the California Highway Patrol system. Specific countermeasures and implications of heterogeneity in means and variances are analyzed based on marginal effects. Out-of-sample simulations are employed to address two fundamental causes of the rise in injury severities: a shift in driver behaviors and the overrepresentation of riskier drivers. Results indicate that a shift towards more aggressive driving behaviors is the main reason for the increments of injury severities on highways after the order. The overrepresentation of riskier drivers is identified as the main cause during the order (both roadways) and on non-highways after the order. Since the predicted proportions on non-highway models before and during the order are closer compared to highways, this further suggests that local drivers are more inclined to violate the restriction and travel within neighborhoods during the order, which could contribute to the selectivity of riskier drivers. The findings of behavior shifts and sample selectivity issues provide valuable insights for future stay-at-home order practice, restriction improvement, and complementary policy development.

Road safety is a significant developmental delinquent, an issue of public health, and a major global cause of demise and injury. Several research and review articles were focused on road safety concerning road characteristics, vehicle characteristics, and environment. However, a review article concerning land use, budget, and legislation towards road safety needs to be included. The present study reviewed literatures and reports on road safety concerning land use, budget, and legislation. The method used for review was "Preferred reporting items for systematic reviews and meta-analyses (PRISMA)". Initially, the paper discussed the impact of accidents concerning land use. The parameters such as crash type, crash cause, crash severity, traffic violation, infrastructural risk, operational hours, Traffic analysis Zone (TAZ) level crashes, on -network factors and medical service response were influenced by land use. Commercial area was more inclined to risk than other land use types. Later, budget section was examined, and it revealed that fuel subsidies, cost-effective strategies, and advertising budgets had a notable impact on reducing crashes. Road safety budget allocation strategies were impacted by significant parameter such as policy scenario and the performance indicator. Over 50% of road crash costs were related to injuries, while 10% of accident costs were related to delays. Indexes of road safety were reliant on budget. The study concluded by discussing the impact of legislation- changes to the traffic laws were successful in reduction of crashes, however, formulation of laws might or might not be successful in lowering casualities. Furthermore, "being aware of the laws" reduced collisions, as well public was persuaded in formulation of law for road safety. Finally, examination of three factors such as land use, budget, and legislation-found an incredible impact on road safety.

Signalized intersections are the areas where traffic crashes with severe injuries frequently happen. Although existing studies have explored the factors affecting crash injury severity at signalized intersections, intricate causal relationships between factors often fail to be captured. Thus, usage of Bayesian network reveals factors contributing to injury severity and the causal relationships between them, with the use of crash data extracted from the Crash Report Sampling System in 2021. The K2 algorithm and Expectation-Maximization algorithms are adopted for structure learning and parameter learning in Bayesian networks, respectively. The results indicate that 1) factors such as speeding, drunk driving, and use of airbags can significantly affect the injury severity, 2) causal relationships exist between distraction, running the red signal, collision type, and crash injury severity, and 3) compared to the random parameter logit model and random forest, Bayesian network has better accuracy in predicting the crash injury severity. The findings can serve to propose effective traffic safety intervention measures to reduce the injury severity of crashes at signalized intersections.

Earthquakes are among the most devastating natural disasters, often resulting in significant loss of life and widespread injuries. Crush syndrome (CS), a systemic manifestation of muscle injury due to prolonged compression, is a critical condition commonly seen in earthquake survivors. This study examines the clinical outcomes of patients with crush syndrome (CS) and acute kidney injury (AKI) following the 2023 Kahramanmaras earthquake in Turkey. Of the 321 survivors hospitalized, 143 required intensive care. The study found that children were more likely to develop CS, while adults had longer hospital stays. CS was associated with higher rates of complications, including compartment syndrome, the need for fasciotomy, and mortality. The findings highlight the importance of early detection and treatment of CS and AKI in disaster survivors to improve outcomes and reduce mortality in future earthquakes.

Motorcycles are a popular low-cost personal transport mode. Despite their convenience, motorcycles are significantly more dangerous than other modes of transport, accounting for up to 39% of road fatalities in low-income countries. Speeding is among the most common factors causing road accidents. Thus, this research extends the theory of planned behavior to investigate young motorcyclists' speeding behavior by incorporating the latent variables of hedonic motivation and transport policy interventions using data collected through a questionnaire survey conducted among young motorcyclists in Lahore, Pakistan. Purpose-based sampling method was deployed to collect 394 responses. The results indicated that speeding attitudes (SA), perceived behavioral control (PBC), hedonic motivation (HM), and policy intervention (PI) variables are strong predictors of speeding intentions (SI), which act as a mediator of speeding behavior (SB). While HM positively affects SB, and the PI variable negatively influences SB. Moreover, unmarried and employed respondents are positively associated with SB. This research has provided important insights on how to improve young motorcyclists' safe behavior, which can be utilized by policymakers to make informed decisions to enhance road safety in Pakistan and other developing economies with similar socio-economic dynamics, with motorcycles as a popular low-cost personal travel mode.

Cyclist crashes are a growing concern in Malaysia due to cyclists' vulnerability and infrastructural risks. This study identifies demographic and contextual factors influencing risky cyclist behaviours in Kuala Lumpur, Cyberjaya, and Shah Alam, focusing on helmet non-use, reflective clothes non-use, light non-use, riding two abreast, and riding outside designated lanes. Field observations (2,665) were conducted at midblock and intersection locations. Results show helmet non-use is more common during morning peak hours, among road bike users, in parks, and on weekends. Riding two abreast is frequent on weekdays and less common among female cyclists. Riding outside designated lanes increases during rainy conditions and in areas without street parking, but decreases on weekdays, during recreational activities, and in parks. At intersections, helmet non-use is prevalent during morning peak hours, among mountain and road bike users, and during recreational activities. These findings can inform targeted interventions like awareness programs, infrastructure improvements, policy enforcement, and promoting safety gear to enhance cyclist safety in Malaysia.

A recently published impact evaluation overstates the benefits of IRAP protocols in reducing traffic injuries. This ICoRSI Position Statement clarifies the biases in the methods used in this study and how its findings should be interpreted. It further describes the potential value of road design in improving road safety in LMICs but highlights the urgent need for research on developing infrastructure interventions and evaluating them using real-world data from LMICs.

The management of road safety relies on data from road traffic crashes to identify priorities, monitor trends and evaluate interventions. Both police and hospital records are important sources of information on crashes that result in injury; however, both are known to be incomplete, with the quality and completeness of data being lower in low- and middle-income countries. The aim of this study is to estimate the magnitude of the underreporting of crashes in Dire Dawa City, Ethiopia, as a case study that may be applicable elsewhere. In addition, it gives an opportunity to understand the discrepancies between police and hospital records in Dire Dawa City and how the data systems work in the city. This research compared data on traffic collisions resulting in injury from July 2014 to February 2019 across police and hospital databases and used the capture-recapture technique to estimate the actual numbers of crashes and the degree of under-recording in both sources. It was found that there was substantial under-recording in both sources, with the degree of under-recording varying by urban/rural area, gender, age, road user category and injury severity, as well as by source within these variables. The police figures were lower than the hospital figures, and in all cases but three (rural areas, passengers and serious injury crashes), both sources had more unmatched than matched cases. In addition, the analysis discovered undocumented deaths and injuries in both databases. To summarize, police capture more death instances, but hospitals capture more serious injury cases. The capture-recapture strategy predicted a greater number of instances than currently recorded by police and hospitals. This demonstrates a major under-reporting of crash data from both sources. This level of under-recording can lead to less effective road safety management and evaluation. Replication of this research in other parts of Ethiopia could provide information on local practices that are more or less successful in reducing the level of under-recording, and such results may have implications for other countries with similar problems.

Statistical modeling and data-driven studies on bicycle accidents are widespread, however, explanations of the underlying mechanisms remain limited, particularly regarding the impact of key risk factors on the bicycle crash frequency across different crash severities. This study aims to examine the effects of various risk factors on the frequency of bicycle crashes using Random Forest and Shapley Additive Explanations (RF-SHAP), taking into account the different crash severity levels. Data from three years of London crash data (2017 to 2019) is utilized. Population demographics, land use, road infrastructure, and traffic flows, are collected in Greater London. In addition to providing superior predictive accuracy, our proposed method identified critical risk factors at different levels of severity associated with bicycle crashes. The distinct contribution of this study is the identification of the primary factors influencing the severity of bicycle collisions in London through the use of RF-SHAP. The study quantifies both the main and interactive effects of various severity risk factors on bicycle collisions. Results suggest that the proportion of building areas and population density are most critical to bicycle crash numbers in different severity levels. Also, the interaction effects of the risk factors on bicycle crashes are revealed. Specifically, results reveal a negative correlation between traffic flow and overall bicycle crash frequency when the average road network connectivity is below 2.25. After controlling the population density, the proportion of residential areas shows a three-stage pattern of influence on the slight injury crash frequency. Furthermore, a boundary value of 6.3 is identified for the safety impact of road density on fatal and severely-injured bicycle crashes. Study findings should provide insights into cost-effective safety countermeasures for bicycle infrastructures, traffic controls, and safety education. Bicycle safety can be improved through these measures over the long term.

Public buses and taxis play crucial roles in urban transportation. Ensuring their safety is of paramount importance to develop sustainable communities. This study investigated the significant factors contributing to the injury severity of bus-taxi crashes, using the crash data recorded by the police in Hong Kong from 2009 to 2019. To account for the unobserved heterogeneity, the random parameters logistic model with heterogeneity in means was elaborately developed. The results revealed that taxi driver age, bus age, traffic congestion, and taxi driver behavior had significantly heterogeneous effects on the injury severity of bus-taxi crashes and that the mean value of the random parameter for severe traffic congestion was likely to increase if the taxi's age was <5 years. Taxi driver gender, rainfall, time of day, crash location, bus driver behavior, and collision type were found to significantly affect the bus-taxi crash severity. Specifically, female taxi drivers, old taxis, rainfall, midnight, improper manipulation of bus and taxi drivers, head-on and sideswipe collision types, and non-intersections were associated with a higher likelihood of fatal and severe crashes. Based on our findings, targeted countermeasures were proposed to mitigate the injury severity of bus-taxi crashes.