Following the research, several recommendations were made concerning the improvement of statewide vehicle inspection regulations.
Evolving as a transport option, shared e-scooters exhibit unique features regarding their physical attributes, operational behaviors, and travel patterns. Safety concerns regarding their use have been voiced, yet effective interventions remain elusive due to the scarcity of available data.
A dataset of rented dockless e-scooter fatalities in US motor vehicle crashes (2018-2019, n=17) was compiled from media and police reports. This was then further corroborated against the National Highway Traffic Safety Administration’s records. A comparative analysis of traffic fatalities during the same period was undertaken using the dataset.
In comparison to fatalities from other transportation methods, e-scooter fatalities exhibit a pattern of being more prevalent among younger males. Among all modes of transport, e-scooter fatalities are more common at night, except for those involving pedestrians. The risk of being killed in a hit-and-run is statistically equivalent for e-scooter users and other vulnerable non-motorized road participants. E-scooter fatalities demonstrated the highest alcohol involvement rate of any mode of transport, but this was not significantly greater than the rate observed among pedestrian and motorcyclist fatalities. Compared to pedestrian fatalities, e-scooter fatalities at intersections showed a higher correlation with crosswalks or traffic signals.
Just like pedestrians and cyclists, e-scooter users have a range of common vulnerabilities. Although e-scooter fatalities share similar demographic profiles with motorcycle fatalities, the circumstances of the crashes exhibit more features in common with incidents involving pedestrians and cyclists. E-scooter fatalities are remarkably different in their characteristics than fatalities from other modes of transportation.
E-scooter usage requires a clear understanding from both users and policymakers as a distinct mode of transport. This study illuminates the similarities and divergences in comparable practices, like ambulation and cycling. Comparative risk insights empower e-scooter riders and policymakers to take actions that effectively reduce fatal accidents.
Users and policymakers need to appreciate the distinct nature of e-scooters as a transport modality. check details This study sheds light on the shared attributes and divergent features of analogous practices, like walking and cycling. E-scooter riders and policymakers can employ the insights gleaned from comparative risk assessments to proactively mitigate the occurrence of fatal accidents.
Transformational leadership's effect on safety has been researched through both generalized (GTL) and specialized (SSTL) applications, with researchers assuming their theoretical and empirical equivalence. In this paper, a reconciliation of the relationship between these two forms of transformational leadership and safety is achieved via the application of paradox theory (Schad, Lewis, Raisch, & Smith, 2016; Smith & Lewis, 2011).
An investigation into the empirical difference between GTL and SSTL is conducted, alongside an assessment of their contributions to both context-free (in-role performance, organizational citizenship behaviors) and context-specific (safety compliance, safety participation) work performance, and the effect of perceived safety concerns on their distinctiveness.
Cross-sectional and short-term longitudinal studies demonstrate that GTL and SSTL, while exhibiting high correlation, are psychometrically distinct. While SSTL demonstrated greater statistical variance in safety participation and organizational citizenship behaviors than GTL, GTL's variance was greater in in-role performance than SSTL's. GTL and SSTL demonstrated a divergence in low-importance contexts, yet remained indistinguishable in high-priority ones.
These conclusions undermine the either/or (versus both/and) approach to assessing safety and performance, encouraging researchers to investigate the varied nature of context-independent and context-dependent leadership, and to refrain from unnecessarily multiplying context-specific leadership measurements.
This research challenges the dichotomy between safety and performance, prompting researchers to appreciate the differences in approaches to leadership in non-specific and specific scenarios and to avoid further, often overlapping, context-specific operational definitions of leadership.
The aim of this study is to elevate the accuracy of forecasting the rate of crashes on roadway sections, thereby enabling predictions of future safety on transportation facilities. check details Statistical and machine learning (ML) methods are diversely employed to model crash frequency, ML approaches often exhibiting superior predictive accuracy. The emergence of heterogeneous ensemble methods (HEMs), encompassing stacking, has led to more precise and dependable intelligent techniques for producing more reliable and accurate predictions.
This study utilizes Stacking to model crash rates on five-lane undivided (5T) sections of urban and suburban arterial roads. We evaluate Stacking's predictive ability by juxtaposing it with parametric models (Poisson and negative binomial), and three advanced machine learning approaches (decision tree, random forest, and gradient boosting), each playing the role of a base learner. Employing a precise weighting methodology when integrating individual base-learners through the stacking technique, the propensity for biased predictions resulting from variations in individual base-learners' specifications and prediction accuracy is prevented. Data pertaining to crashes, traffic patterns, and roadway inventories were systematically collected and combined from 2013 to 2017. Datasets for training (spanning 2013-2015), validation (2016), and testing (2017) were established by separating the data. check details Employing training data, five individual base learners were trained, and their predictions on validation data were then used to train a meta-learner.
Statistical models show that crash rates rise with the number of commercial driveways per mile, but fall as the average distance from fixed objects increases. The variable importance rankings from individual machine learning models show a remarkable similarity. Out-of-sample performance assessments of different models or approaches reveal a marked superiority for Stacking over the other methods evaluated.
Conceptually, stacking learners provides superior predictive accuracy compared to a single learner with particular restrictions. Implementing stacking strategies systemically enhances the identification of more effective countermeasures.
A practical advantage of stacking learners is the improvement in prediction accuracy, as opposed to relying on a single base learner with a particular configuration. The systemic use of stacking strategies helps to discover more fitting countermeasures.
Fatal unintentional drownings in the 29-year-old population were examined by sex, age, race/ethnicity, and U.S. Census region from 1999 to 2020, with this study highlighting the trends.
The Centers for Disease Control and Prevention's WONDER database served as the source for the extracted data. Individuals aged 29 who died of unintentional drowning were identified by applying International Classification of Diseases, 10th Revision codes V90, V92, and W65-W74. Extracted from the data were age-adjusted mortality rates, categorized by age, sex, race/ethnicity, and U.S. Census region. To evaluate the overall trend, simple five-year moving averages were used, and Joinpoint regression models were fitted to estimate average annual percentage changes (AAPC) and annual percentage changes (APC) in AAMR during the study's timeframe. Using Monte Carlo Permutation, 95% confidence intervals were calculated.
The grim statistics indicate that 35,904 people, 29 years of age, died from accidental drowning in the United States between 1999 and 2020. Residents of the Southern U.S. census region had a relatively high mortality rate, with an AAMR of 17 per 100,000 and a 95% confidence interval of 16-17. Unintentional drowning deaths exhibited a statistically stable trend from 2014 through 2020, with an average proportional change of 0.06 (95% confidence interval -0.16 to 0.28). Across age groups, genders, racial/ethnic backgrounds, and U.S. census regions, recent trends have either decreased or remained steady.
Improvements in unintentional fatal drowning rates have been observed in recent years. These results confirm the continued need for expanded research and more effective policies to maintain a consistent decrease in these trends.
The number of unintentional fatal drownings has decreased significantly over recent years. These results demonstrate the persistent requirement for more research and policy reform to achieve and sustain a decrease in the observed trends.
The year 2020, a period marked by unprecedented events, saw the rapid spread of COVID-19, leading most nations to institute lockdowns and confine their populations, aiming to curb the exponential rise in cases and deaths. To this point, only a small number of studies have examined the consequences of the pandemic for driving practices and highway safety, typically looking at data gathered over a restricted timeframe.
This descriptive study correlates road crash data with driving behavior indicators, examining the impact of the stringency of response measures in Greece and the Kingdom of Saudi Arabia. An approach using k-means clustering was also used in an attempt to find meaningful patterns.
Speeds showed an increase, reaching up to 6% during lockdown periods, in contrast with a notable increment of approximately 35% in harsh events, compared to the post-confinement period, across both countries.