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Purpose: Taiwan, which has a rate of high vehicle ownership, faces significant challenges in managing trauma caused by traffic collisions. In Taiwan, traffic collisions contribute significantly to morbidity and mortality, with a high incidence of severe bleeding trauma. The shock index (SI) and the modified shock index (MSI) have been proposed as early indicators of hemodynamic instability. In this study, we aimed to assess the efficacy of SI and MSI in predicting adverse outcomes in patients with trauma following traffic collisions. Methods: This retrospective cohort study was conducted at Chi Mei Hospital from January 2015 to December 2020. The comprehensive analysis included 662 patients, with data collected on vital signs and outcomes such as mortality, blood transfusion, emergent surgical intervention (ESI), transarterial embolization (TAE), and intensive care unit (ICU) admission. Optimal cutoff points for SI and MSI were identified by calculating the Youden index. Logistic regression analysis was used to assess outcomes, adjusting for demographic and injury severity variables. Results: An SI threshold of 1.11 was associated with an increased risk of mortality, while an SI of 0.84 predicted the need for blood transfusion in the context of traffic collisions. Both SI and MSI demonstrated high predictive power for mortality and blood transfusion, with acceptable accuracy for TAE, ESI, and ICU admission. Logistic regression analyses confirmed the independence of SI and MSI as risk factors for adverse outcomes, thus, providing valuable insights into their clinical utility. Conclusions: SI and MSI are valuable tools for predicting mortality and blood transfusion needs in patients with trauma due to traffic collisions. These findings advance the quality of care for patients with trauma during their transition from the emergency room to the ICU, facilitating prompt and reliable decision-making processes and improving the care of patients with trauma. [ABSTRACT FROM AUTHOR]

INTRODUCTION: Growing research indicates transportation injury surveillance using police collision reporting alone underrepresents injury to vulnerable groups, including pedestrians, cyclists, and people of color. This reflects differing reporting patterns and non-clinicians challenge in accurately evaluating injury severity. To our knowledge, San Francisco is the first U.S. city to link and map hospital and police injury data. Analysis of linked data injury patterns informs interventions supporting traffic fatality and injury prevention goals. METHODS: Injury and fatality records 2013-2015 were collected from San Francisco Police, Emergency Medical Services (EMS), Medical Examiner, and Zuckerberg San Francisco General Hospital (ZSFG). Probabilistic linkage was conducted using LinkSolv9.0 on match variables collision/admission time, name, birthdate, sex, travel mode, and geographic collision location. RESULTS: From 2013-2015, this study identified 17,000+ transportation-related injuries on public roadways in San Francisco. Twenty-six percent (n = 4,415) appeared in both police and ZSFG sources. Linked injury records represent 39% of police records (N = 11,403) and 43% of hospital records (N = 10,223). Among hospital records, 34% of cyclist, 38% of motor vehicle occupant, 61% of pedestrian, and 54% of motorcyclist records linked with a police record. Linkage rate varied by travel mode even after controlling for injury severity. Transportation-injured ZSFG-treated patients lacking police reports were more often cyclists, male, Hispanic or Black, and less often occupants of motor vehicles compared to those with injuries captured only in police reports. CONCLUSIONS: Incorporating hospital and EMS spatial data into injury surveillance systems historically reliant on police reports offers trifold benefits. First, linkage captures injuries absent in police data, adding data on populations empirically vulnerable to injury. Second, it improves injury severity assessment. Finally, linked data better informs and targets interventions serving injury-burdened populations and road users, advancing transportation injury prevention. PRACTICAL APPLICATIONS: Linkage closes data gaps, improving ability to quantify injury and develop evidence-based interventions for vulnerable groups.

Abstract Background Understanding demographic profiles is essential to the assessment of health burden imposed by motor vehicle crashes (MVCs) on pregnant women. However, Asian studies that have examined it are lacking. The study aimed to describe the demographic characteristics and prevalence of MVCs involving pregnant women in Taiwan. Methods A cross-sectional study conducted by the Taiwan Birth Notification dataset from 2008 to 2017 was linked with the police-reported traffic collision registry to identify pregnant women involved in MVCs. The pregnant women were categorized according to their gestational age, age at delivery, the role of road user (driver, passenger, or pedestrian), and vehicle types (car, two-wheeled motor vehicle, or others). A chi-square test was performed for the significance test. Results A total of 22,134 (1.13%) pregnant women were involved in MVCs in the study period. Two-wheeled motor vehicle (47.9%) and driver (81.4%) were the mainly reported vehicle type and road user at the crash scenes, respectively. The majority of MVCs occurred in pregnant women aged 28–34 years. The number of MVCs rapidly declined after 37 weeks of gestation, especially two-wheeled motor vehicle or car crashes. However, the number of pedestrian victims climbed up during the third trimester. Conclusion Pregnant women are susceptible to MVCs regardless of their gestational age, role of a road user, or type of vehicle. The findings of this study emphasize the need for increased awareness of traffic collision prevention among pregnant women aged 28–34. In addition, improving pedestrian safety is essential for the reduction of pregnant victims.

Background: Understanding demographic profiles is essential to the assessment of health burden imposed by motor vehicle crashes (MVCs) on pregnant women. However, Asian studies that have examined it are lacking. The study aimed to describe the demographic characteristics and prevalence of MVCs involving pregnant women in Taiwan. Methods: A cross-sectional study conducted by the Taiwan Birth Notification dataset from 2008 to 2017 was linked with the police-reported traffic collision registry to identify pregnant women involved in MVCs. The pregnant women were categorized according to their gestational age, age at delivery, the role of road user (driver, passenger, or pedestrian), and vehicle types (car, two-wheeled motor vehicle, or others). A chi-square test was performed for the significance test. Results: A total of 22,134 (1.13%) pregnant women were involved in MVCs in the study period. Two-wheeled motor vehicle (47.9%) and driver (81.4%) were the mainly reported vehicle type and road user at the crash scenes, respectively. The majority of MVCs occurred in pregnant women aged 28–34 years. The number of MVCs rapidly declined after 37 weeks of gestation, especially two-wheeled motor vehicle or car crashes. However, the number of pedestrian victims climbed up during the third trimester. Conclusion: Pregnant women are susceptible to MVCs regardless of their gestational age, role of a road user, or type of vehicle. The findings of this study emphasize the need for increased awareness of traffic collision prevention among pregnant women aged 28–34. In addition, improving pedestrian safety is essential for the reduction of pregnant victims. [ABSTRACT FROM AUTHOR]

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Rapid breakthroughs in information technologies have driven substantial developments in artificial intelligence applications, particularly the widespread use of deep learning techniques in domains such as speech, image and text recognition. However, real world data distribution applications suffer from significant problems including data imbalance which can easily lead to machine learning biased towards the side with more data, resulting in inaccurate classification or prediction results. Therefore, effectively addressing data imbalance is a pressing research topic. Generative Adversarial Networks (GAN) addresses data imbalance, but is prone to vanishing gradients. Recent work has thus focused on improving the GAN architecture to resolve this problem. The present research extends these efforts, applying C4.5, Random Forest, Support Vector Machine, K-Nearest Neighbor and Naïve Bayes classification algorithms to a single imbalanced traffic collision dataset to identify methods for improving prediction results. Experimental results show that classification performance significantly improves after data augmentation using Synthetic Minority Oversampling Technique, GAN, Conditional GAN, and Gaussian Discriminant Analysis GAN as compared with the non-augmented dataset. In addition, the Gaussian Discriminant Analysis GAN with Naïve Bayes classifier produces a dataset that optimizes classification performance for traffic accident prediction at highway intersections. [ABSTRACT FROM AUTHOR]

The aim of this study is to develop a model to predict temporal daily collision by integrating of Discrete Wavelet Transform (DWT) and Artificial Neural Network (ANN) algorithms. As a case study, the integrated model was tested on 1097 daily traffic collisions data of Karaj-Qazvin freeway from 2009 to 2013 and the results were compared with the conventional ANN prediction model. In this method, initially, the raw collision data were analyzed, normalized, and classified via Geographical Information System (GIS). Partial Autocorrelation Function (PACF) was also utilized to evaluate the temporal autocorrelation for consecutive existing daily data. The results of this study showed that the proposed integrated DWT-ANN method provided higher predictive accuracy in daily traffic collision than ANN model by increasing coefficient of determination (R2) from 0.66 to 0.82. [ABSTRACT FROM AUTHOR]

The effects of epinephrine administration timing on patients with out-of-hospital cardiac arrest (OHCA) following traffic collisions are unknown. We analyzed the 2013–2019 All-Japan Utstein Registry data of 2024 such patients aged ≥18 years who were resuscitated by emergency medical service (EMS) personnel or bystanders and then transported to medical institutions. Time from 119 call to epinephrine administration was classified into quartiles: Q1 (6–21 min), Q2 (22–26 min), Q3 (27–34 min), and Q4 (35–60 min). Multivariable logistic regression analysis was used to assess the effects of epinephrine administration timing on one-month survival after OHCA. Overall, the one-month survival rates were 3.2% (15/466) in Q1, 1.1% (5/472) in Q2, 1.9% (11/577) in Q3, and 0.2% (1/509) in Q4. Additionally, the one-month survival rate decreased significantly in the Q4 group (adjusted odds ratio, 0.07; 95% confidence interval, 0.01–0.57) compared with the Q1 group, and the probability of one-month survival decreased as the time from the EMS call to epinephrine administration increased (p-value for trend = 0.009). Only four patients (0.9% [4/466]) with the earliest epinephrine administration showed a good neurological outcome. [ABSTRACT FROM AUTHOR]

The lessons learned from each Traffic Collision (TC) will help safety practitioners to avoid similar occurrences in the future. However, few studies and methods have focused specifically on the similar features among different collisions. Thus, the development of a measurement method for investigating the best evidence on the causal factors of TCs was warranted. In this study, a similarity analysis method based on the Analytic Hierarchy Process (AHP) and Similarity (S) theory, the AHP-S method, was constructed. This method was designed to identify the similar elements and similar units of collision scenes according to the analysis criteria and sub-criteria and further to calculate the degree of similarity between recognized similar pairs among TCs. Six TC cases were randomly selected as examples, and the degrees of similarity between cases 1 to 5 and case 6 were calculated separately. The calculation results showed that out of the five collision cases (cases 1–5), case 1 provided the best evidence for analysing the causal factors of case 6. This study promotes the development of quantitative analysis methods for collision incidents and provides an effective evidence-based method for TC avoidance. First published online 17 March 2021

Background: The aim of this study was to evaluate the performance of the Sequential Organ Failure Assessment (SOFA) score and the newly introduced criteria, traumasis, defined as a SOFA score 2 or more among trauma patients.Methods: Consecutive adult traffic collision patients who were admitted to the study hospital emergency department (ED) from January 2017 to December 2018 were enrolled retrospectively in the study. The primary outcome was in-hospital death. The SOFA score was calculated using relevant initial ED data. Traditional risk scores for trauma patients, such as the injury severity score (ISS), the revised trauma score (RTS), and the trauma injury severity score (TRISS), were also calculated.Results: A total of 927 patients were available for analysis, of whom 46 died (5.0%). The median SOFA score was 1.0 (interquartile range [IQR], 0.0-3.0). A total of 417 patients (45.0%) were identified as having traumasis (SOFA score ≥ 2), of whom 44 died (10.6%). The area under the receiver operating characteristic (AUROC) curve of the SOFA score (0.91; 95% confidence interval [CI] 0.87-0.95) was comparable with that of the TRISS (0.88; 95% CI, 0.84-0.93) and better than that of the ISS(0.81; 95% CI 0.75-0.86) and the RTS (0.82; 95% CI 0.75-0.90). The sensitivity, specificity, positive predictive value and negative predictive value of the traumasis criteria for the primary outcome were 95.7%, 63.0%, 11.9%, and 99.6%, respectively. The net reclassification improvement for the comparison between the traumasis criteria and major trauma criteria (ISS ≥ 15) was 0.69 (95% CI, 0.55-0.82; p < 0.001). The multivariate Cox regression model showed that the SOFA score (adjusted hazard ratio [aHR] 1.52; 95% CI 1.37-1.67) and traumasis (aHR 11.40; 95% CI 2.70-48.13), respectively, was independently associated with higher in-hospital mortality.Conclusion: The SOFA score can be used as a reliable tool for predicting in-hospital death among traffic collision patients. The newly introduced criteria, traumasis, may be used as a risk-stratification and quality-control criteria among patients with trauma, similar to the sepsis criteria among patients with infectious disease. [ABSTRACT FROM AUTHOR]

The emerging technology of automated vehicles (AV) has been rapidly advancing and is accompanied by various positive and negative potentials. The new technology is expected to affect costs mainly by reducing the number of collisions and travel time, as well as improving fuel efficiency and parking benefits. On the other hand, safety outcomes from AV deployment is a critical issue. Ensuring the safety of AVs requires a multi-disciplinary approach that monitors every aspect of these vehicles. The California Department of Motor Vehicles has mandated that AV collision reports be made public in recent years. This study collected the scanned collision reports filed by different manufacturers that are assessing AVs in California (September 2014 to May 2019). The collected data offers critical information on AV collision frequencies and associated contributing factors. This study provides an in-depth exploratory analysis of the critical variables. We demonstrated a variational inference algorithm for Bayesian latent class models. The Bayesian latent class model identified six classes of collision patterns. Classes associated with turning, multi-vehicle collisions, dark lighting conditions with streetlights, and sideswipe and rear-end collisions were also associated with a higher proportion of injury severity levels. The authors anticipate that these results will provide a significant contribution to the area of AV and safety outcomes.

The emerging technology of automated vehicles (AV) has been rapidly advancing and is accompanied by various positive and negative potentials. The new technology is expected to affect costs mainly by reducing the number of collisions and travel time, as well as improving fuel efficiency and parking benefits. On the other hand, safety outcomes from AV deployment is a critical issue. Ensuring the safety of AVs requires a multi-disciplinary approach that monitors every aspect of these vehicles. The California Department of Motor Vehicles has mandated that AV collision reports be made public in recent years. This study collected the scanned collision reports filed by different manufacturers that are assessing AVs in California (September 2014 to May 2019). The collected data offers critical information on AV collision frequencies and associated contributing factors. This study provides an in-depth exploratory analysis of the critical variables. We demonstrated a variational inference algorithm for Bayesian latent class models. The Bayesian latent class model identified six classes of collision patterns. Classes associated with turning, multi-vehicle collisions, dark lighting conditions with streetlights, and sideswipe and rear-end collisions were also associated with a higher proportion of injury severity levels. The authors anticipate that these results will provide a significant contribution to the area of AV and safety outcomes. • Used the most comprehensive automated vehicle collision data (2014–2019) in the analysis. • Provided in-depth exploratory analysis of the critical variables. • Applied Bayesian latent class models to identify collision patterns. • Key crash contributors include turning movement, rear-end collision, multi-vehicle collision, and roadways with no lighting. [ABSTRACT FROM AUTHOR]

• A Bayesian vector auto-regression data analytics approach is proposed. • A Gibbs sampler is used to conduct Bayesian inference for model parameters and unobserved variables. • The model has a fairly superior fit accuracy, and is able to capture the unobserved heterogeneity in the dataset. Traffic collision data are always collected in irregularly spaced and mixed frequency. Conventional treatment on these kinds of data, for instance, aggregating the high-frequency data into the lower frequency, can lead to the loss of relevant information of high-frequency data, and introduce potential temporal instabilities. A novel Bayesian vector autoregression approach is proposed to address this problem. An unevenly-spaced traffic collision data with missing values, containing all collisions in different severities that occurred on the state highways in Washington State from January 2006 to December 2016, is selected in this study the impacts of transportation-, weather- and socioeconomic-related characteristics on traffic collisions. A Gibbs sampler is used to conduct Bayesian inference for model parameters and unobserved high-frequency variables. Results show that the model has a fairly superior fit accuracy, and is able to capture the unobserved heterogeneity in the dataset. The proposed VAR also demonstrates better performance than other missing value imputation techniques, including linear regression, predictive mean matching, k-nearest neighbors, and random forests. This study provides potential in the guidance of model construction that considers the mixed-time-series nature of data. [ABSTRACT FROM AUTHOR]

• The environmental contributing factors to elderly pedestrian collision differ from ones to younger pedestrian collision. • Raised medians, 3-way intersections, and street trees make a positive contribution to the safety of elderly pedestrians. • Bus stops increase the odds of an intersection to be a hotspot of elderly pedestrian collisions. • The current road system designed for young, healthy users should be refurbished for older, vulnerable pedestrians. For the elderly, walking is an important, reliable mobility option, since the elderly frequently lose their physical and/or sensory ability to drive as their age increases. However, elderly pedestrians are vulnerable on the streets and are at great risk of injury or death, when involved in a collision. This is due to not only increased frailty but also such issues as reaction speed and confidence on the streets. Therefore, pedestrian safety for older adults is a growing concern. This paper comprehensively examines the relationship between physical conditions and elderly pedestrian safety at the intersection level. By constructing a multinomial logistic regression (MLR) model, this paper identifies the exclusive contributing factors to elderly pedestrian collisions rather than younger pedestrian collisions. The outputs from the model suggest that facilities such as raised median, three-way intersection, street tree, and park and recreational land use improve the safety of elderly pedestrians. They also imply that bus stops increase elderly pedestrian collisions, while the intersections with crosswalks or colored crosswalks do not contribute to elderly pedestrians' safety, but the safety of younger pedestrians. The findings of this paper provide insight to transportation policies like Complete Street and Vision Zero and help to improve the current road system that are designed for automobiles and young, healthy road users. [ABSTRACT FROM AUTHOR]

The automated enforcement systems (AESs), which detect speeding vehicles or vehicles that violate traffic signals, are installed and operated on urban arterial roadways to prevent traffic collisions and reduce injury severity in case of a traffic collision. As it is expensive to install the AES, it will be installed at a site where it is expected to have a great effect. However, as there are no specific guidelines on where it should be installed, it has been installed arbitrarily in high collision concentration locations (HCCLs). The objective of this study is to contribute to the improvement in the efficiency of system operation and budget execution by identifying sites where the enforcement effect of the AES is high, and providing a road manager with the basis of decision-making on system installation. This study classified road sections into clusters based on road environment characteristics and spatial collision occurrence pattern, respectively, and compared collisions statistics and occurrence pattern before and after AES installation to analyze the effect of AES installation. As a result, the study verified that the number of collisions tends to decrease at HCCLs past enforcement sites, while the number of collisions tends to increase at sites where not only red light running but also speeding is enforced after AES installation. In addition, from the perspective of collision severity, it was observed that the severity of collisions was alleviated in most clusters after AES installation. This may be viewed as a positive effect caused by the decrease in vehicle speed due to speeding enforcement. [ABSTRACT FROM AUTHOR]

The movement of animals on roads often causes a collision with vehicles. This not only results in the loss of animal population, but also in social losses - consequences of road accidents. This issue can be dealt with by restricting the movement of animals across roads - most commonly by fencing along roads in combination with construction of overpasses for safe crossing over roads. However, these measures also have their drawbacks - fragmentation of landscape, costs on construction and maintenance. These measures should be designed on the basis of an analysis of migration routes of animals, migration potential of an area (and thus increased movement of animals over a road), and on the basis of characteristics of the traffic flow on a given road. In 2011-2014, EDIP co. together with HBH Projekt have been working on a research project focused on the methodology for optimization of designing measures which should guide/regulate the movement of animals across the road. EVERNIA co. also participates in the project. The project is financially supported by the Technological Agency of the Czech Republic. The outcome will come in the form of a methodology which becomes a part of the regulations for designing roads.

In matched observational studies where treatment assignment is not randomized, sensitivity analysis helps investigators determine how sensitive their estimated treatment effect is to some unmeasured confounder. The standard approach calibrates the sensitivity analysis according to the worst case bias in a pair. This approach will result in a conservative sensitivity analysis if the worst case bias does not hold in every pair. In this paper, we show that for binary data, the standard approach can be calibrated in terms of the average bias in a pair rather than worst case bias. When the worst case bias and average bias differ, the average bias interpretation results in a less conservative sensitivity analysis and more power. In many studies, the average case calibration may also carry a more natural interpretation than the worst case calibration and may also allow researchers to incorporate additional data to establish an empirical basis with which to calibrate a sensitivity analysis. We illustrate this with a study of the effects of cellphone use on the incidence of automobile accidents. Finally, we extend the average case calibration to the sensitivity analysis of confidence intervals for attributable effects. [ABSTRACT FROM AUTHOR]

Objectives To compare the risk of fatal injury across helmet types among collision-involved motorcyclists. Methods We used data from a cohort of motorcyclists involved in police-reported traffic collisions. Eighty-four law enforcement agencies in California collected detailed information on helmet and rider characteristics during collision investigations in June 2012 through July 2013. Multiply-adjusted risk ratios were estimated with log-binomial regression. Results The adjusted fatal injury risk ratio for novelty helmets was 1.95 (95% CI 1.11–3.40, p 0.019), comparing novelty helmets with full-face helmets. The risk ratios for modular, open-face, and half-helmets, compared with full-face helmets, were not significant. Conclusions A more complete understanding of the inadequacy of novelty helmets can be used in educational and law enforcement countermeasures to improve helmet use among motorcycling populations in California and other US states. Law enforcement approaches to mitigating novelty helmet use would seem attractive given that novelty helmets can be visually identified by law enforcement officers with sufficient training. [ABSTRACT FROM AUTHOR]

This study aim to determine the proportion of traffic rule infractions in Cali, Colombia, in places where a road traffic injury (RTI) occurred. Description of videotaping of sites where a person was injured in an RTI in 2009. Counts of road users and infractions were established for each road user group and were compared using a Z-test. They were found 13,491 users, distributed as follow: 8.9% were pedestrians, 4.6% cyclists, 24.6% motorcyclists, and 61.8% were automobile drivers. The most frequent traffic violation among motorcyclists was transiting on lines designated for other vehicles (55.2%). Among cyclists, the most frequent violations were transiting without a helmet (99.2%) and not wearing the designated vest (100%). Among pedestrians, crossing streets at prohibited places (77.3%), even at sites where a pedestrian bridge was present (72.7%), represented two common violations. Vulnerable road users committed more traffic infractions than automobile drivers (p < 0.001). High rates of traffic rule infractions among vulnerable road users were observed. Studies to better understand the occurrence of these behaviours and the promotion of effective interventions are warranted. [ABSTRACT FROM AUTHOR]

Introduction: In 2012, 10% of Canadians used cannabis and just under half of those who use cannabis were estimated to have driven under the influence of cannabis. Substantial evidence has accumulated to indicate that driving after cannabis use increases collision risk significantly; however, little is known about the extent and costs associated with cannabis-related traffic collisions. This study quantifies the costs of cannabis-related traffic collisions in the Canadian provinces.Methods: Province and age specific cannabis-attributable fractions (CAFs) were calculated for traffic collisions of varying severity. The CAFs were applied to traffic collision data in order to estimate the total number of persons involved in cannabis-attributable fatal, injury and property damage only collisions. Social cost values, based on willingness-to-pay and direct costs, were applied to estimate the costs associated with cannabis-related traffic collisions. The 95% confidence intervals were calculated using Monte Carlo methodology.Results: Cannabis-attributable traffic collisions were estimated to have caused 75 deaths (95% CI: 0-213), 4407 injuries (95% CI: 20-11,549) and 7794 people (95% CI: 3107-13,086) were involved in property damage only collisions in Canada in 2012, totalling $1,094,972,062 (95% CI: 37,069,392-2,934,108,175) with costs being highest among younger people.Discussion: The cannabis-attributable driving harms and costs are substantial. The harm and cost of cannabis-related collisions is an important factor to consider as Canada looks to legalize and regulate the sale of cannabis. This analysis provides evidence to help inform Canadian policy to reduce the human and economic costs of drug-impaired driving. [ABSTRACT FROM AUTHOR]

With the increasing complexity of algorithms and new applications, the design of efficient embedded systems has to integrate efficient communication structures such as Network-on-Chip. Multi-FPGA platforms are considered to be the most appropriate experimental way to emulate and evaluate these large System-on-Chips. The deployment often goes through the Network-on-Chip partitioning on all FPGAs requiring the use of inter-FPGA communication links between routers. The number of external links and their performance restrict the communication bandwidth. Currently, the number of inter-FPGA signals is considered to be a major problem in the Network-on-Chip deployed on multi-FPGAs. As there are more signals to be connected than IOs, inter-FPGA links must be shared between routers leading to significant bottlenecks. As the ratio of the logic capacity to the number of IOs rises slowly for each FPGA generation, this technological bottleneck will be remaining for future system designs. In this paper, we propose a novel architecture for inter-FPGA collision management in the Network-on-Chip partitioned on multi-FPGAs. The structure ensures to efficiently share the external link between several routers with a minimum number of collisions and inter-FPGA bottlenecks. The proposed architecture is easily placed between the Network-on-Chip and the external protocol. The collision management architecture is based on the BackOff algorithm used in Wi-Fi communications and adapted to FPGA platforms. This algorithm balances accesses among all the routers connected with the inter-board interfacing, thereby avoiding collisions. We compare this structure with traditional techniques using experimental and theoretical results. The novel inter-FPGA architecture for the Network-on-Chip based on the BackOff algorithm achieves lower latency with fewer resources compared to other solutions. [ABSTRACT FROM AUTHOR]

Background and objective Abrupt accelerations or decelerations can cause large strain in brain tissues and, consequently, different forms of Traumatic Brain Injury (TBI). In order to predict the effect of the accelerations on the soft tissues of the brain, many different injury metrics have been proposed (typically, an injury metric is a real valued functional of the accelerations). The objective of this article is to make a formal and empirical comparison, in order to identify general criteria for reasonable injury metrics, and propose a general guideline to avoid ill-proposed injury metrics. Methods A medium-sized sample of vehicle–pedestrian collisions, from Post Mortem Human Subject (PMHS) tests, is analyzed. A statistical study has been conducted in order to determine the discriminant power of the usual metrics. We use Principal Component Analysis to reduce dimensionality and to check consistency among the different metrics. In addition, this article compares the mathematical properties of some of these functionals, trying to identify the desirable properties that any of those functionals needs to fulfill in order to be useful for optimization. Results We have found a pair-wise consistency of all the currently used metrics (any two injury metrics are always positively related). In addition, we observed that two independent principal factors explain about 72.5% of the observed variance among all collision tests. This is remarkable because it indicates that despite high number of different injury metrics, a reduced number of variables can explain the results of all these metrics. With regard to the formal properties, we found that essentially all injury mechanisms can be accounted by means of scalable, differentiable and convex functionals (we propose to call minimization suitable injury metric any metric having these three formal properties). In addition three useful functionals, usable as injury metrics, are identified on the basis of the empirical comparisons. Conclusions The commonly used metrics are highly consistent, but also highly redundant. Formal minimal conditions of a reasonable injury metric has been identified. Future proposals of injury metrics can benefit from the results of this study. [ABSTRACT FROM AUTHOR]

Traffic collision analysis is essential for reducing traffic injuries. While most traditional approaches focus on the time dimension of traffic collisions, the recent past has witnessed a growing awareness of the spatial dimension in a geographical context. In this paper, 70 studies on the application of GIS to the spatial analysis of traffic collisions are reviewed. The purpose of the paper is to provide a systematic analysis of the major advancements in applied GIS for studying traffic collisions in space since the mid-1970s. [ABSTRACT FROM AUTHOR]

Filtering out the noise in traffic collision data is essential in reducing false positive rates (i.e., requiring safety investigation of sites where it is not needed) and can assist government agencies in better allocating limited resources. Previous studies have demonstrated that denoising traffic collision data is possible when there exists a true known high collision concentration location (HCCL) list to calibrate the parameters of a denoising method. However, such a list is often not readily available in practice. To this end, the present study introduces an innovative approach for denoising traffic collision data using the Ensemble Empirical Mode Decomposition (EEMD) method which is widely used for analyzing nonlinear and nonstationary data. The present study describes how to transform the traffic collision data before the data can be decomposed using the EEMD method to obtain set of Intrinsic Mode Functions (IMFs) and residue. The attributes of the IMFs were then carefully examined to denoise the data and to construct Continuous Risk Profiles (CRPs). The findings from comparing the resulting CRP profiles with CRPs in which the noise was filtered out with two different empirically calibrated weighted moving window lengths are also documented, and the results and recommendations for future research are discussed. [ABSTRACT FROM AUTHOR]

Introduction: Growing research indicates transportation injury surveillance using police collision reporting alone underrepresents injury to vulnerable groups, including pedestrians, cyclists, and people of color. This reflects differing reporting patterns and non-clinicians' challenge in accurately evaluating injury severity. To our knowledge, San Francisco is the first U.S. city to link and map hospital and police injury data. Analysis of linked data injury patterns informs interventions supporting traffic fatality and injury prevention goals., Methods: Injury and fatality records 2013-2015 were collected from San Francisco Police, Emergency Medical Services (EMS), Medical Examiner, and Zuckerberg San Francisco General Hospital (ZSFG). Probabilistic linkage was conducted using LinkSolv9.0 on match variables collision/admission time, name, birthdate, sex, travel mode, and geographic collision location., Results: From 2013-2015, this study identified 17,000+ transportation-related injuries on public roadways in San Francisco. Twenty-six percent (n = 4,415) appeared in both police and ZSFG sources. Linked injury records represent 39% of police records (N = 11,403) and 43% of hospital records (N = 10,223). Among hospital records, 34% of cyclist, 38% of motor vehicle occupant, 61% of pedestrian, and 54% of motorcyclist records linked with a police record. Linkage rate varied by travel mode even after controlling for injury severity. Transportation-injured ZSFG-treated patients lacking police reports were more often cyclists, male, Hispanic or Black, and less often occupants of motor vehicles compared to those with injuries captured only in police reports., Conclusions: Incorporating hospital and EMS spatial data into injury surveillance systems historically reliant on police reports offers trifold benefits. First, linkage captures injuries absent in police data, adding data on populations empirically vulnerable to injury. Second, it improves injury severity assessment. Finally, linked data better informs and targets interventions serving injury-burdened populations and road users, advancing transportation injury prevention., Practical Applications: Linkage closes data gaps, improving ability to quantify injury and develop evidence-based interventions for vulnerable groups., (Published by Elsevier Ltd.)

Abstract: Purpose: This study aimed to determine the severity of injuries to each body region of child pedestrians struck by different types of vehicles. Methods: We collected in-depth data from the Institute for Traffic Accident Research and Data Analysis on pedestrians younger than 13 years with any bodily injuries from collisions with vehicles between 1993 and 2004. Results: Sixty-eight patients with a mean age of 6.9 ± 2.4 years were included in the study. In collisions, vehicles caused higher Abbreviated Injury Scale (AIS) scores than those from impact with the road. Injury Severity Score and AIS values were higher with one-box or sports utility vehicles compared with those in sedan vehicles, but the differences were not statistically significant. The mean AIS score of head injuries was significantly higher with one-box or sports utility vehicles than that with sedans (1.6 ± 2.1 vs 0.5 ± 1.1, P < .05). The mean AIS score of the lower extremities was significantly higher with sedans than that with one-box or sports utility vehicles (1.2 ± 1.0 vs 0.5 ± 0.9, P < .05). Conclusions: The type and severity of injuries in child–car collisions vary by type of vehicle and pedestrian kinematics. [Copyright &y& Elsevier]

Objectives: Road traffic collisions (RTC) result in a significant number of preventable deaths worldwide. In 2010, the United Nations General Assembly launched, "The Decade of Action for Road Safety (2011-2020)" with the stated goal to "reduce road traffic deaths and injuries by 50% by 2020." This study aims to analyze trends in RTC numbers and subsequent deaths with respect to road safety laws in Nigeria and to suggest suitable interventions., Methods: Annual reports for the period 2007-2017 were obtained from the Federal Road Safety Corps of Nigeria. These reports were analyzed for trends in RTC, including reported causes, fatalities, injuries, and casualties., Results: Overall total injuries, casualties, and fatalities increased by 74.7%, 61.2%, and 9.6%, respectively. Analysis showed that the 3 main causes of RTC were speed violation, loss of control, and dangerous driving., Conclusions: Although current trends do not suggest that Nigeria will accomplish its initial goal of decreasing fatalities by 50% by 2020, there has been a reduction in the number of crashes resulting from dangerous driving. Further interventions such as implementing automated speed monitoring, collaboration, and data sharing between federal and regional agencies, and improving the state of road networks should be implemented to decrease fatalities further.

The epidemiological and clinical characteristics, treatments, and outcomes of patients with traumatic out-of-hospital cardiac arrests (OHCAs) following traffic collisions have not been adequately investigated in Japan. We analyzed the All-Japan Utstein Registry data of 918 pediatric patients aged <20 years with OHCAs following traffic collisions who were resuscitated by bystanders or emergency medical service personnel and were subsequently transported to hospitals between 2013 and 2019. Multiple logistic regression analysis was used to assess factors potentially associated with 1-month survival after OHCA. The 1-month survival rate was 3.3% (30/918), and the rate of neurologically favorable outcomes was 0.7% (60/918). The proportion of 1-month survival of all OHCAs after traffic collision origin did not significantly increase (from 1.9% (3/162) in 2013 to 4.5% (5/111) in 2019), and the adjusted odds ratio (OR) for a 1-year increment was 1.13 (95% confidence interval (CI) 0.93 to 1.37). In a multivariate analysis, ventricular fibrillation arrests and pulseless electrical activity (PEA) were significant predictors of 1-month outcome after OHCAs due to traffic collision. From a large OHCA registry in Japan, we demonstrated that 1-month survival after OHCAs due to traffic collision origin was approximately 3%, and some children even gained full recovery of neurological function. [ABSTRACT FROM AUTHOR]

The 1-month survival rate from out-of-hospital cardiac arrest (OHCA) of cardiac origin has reportedly improved recently, at ≥5%. However, the characteristics of patients with OHCA after a traffic collision have not been adequately evaluated in Japan. We analyzed the All-Japan Utstein Registry data of 12,577 adult patients aged ≥ 20 years with OHCA due to traffic collisions who were resuscitated by emergency medical service personnel or bystanders and were then transported to medical institutions between 2013 and 2019. Multiple logistic regression analysis was used to assess factors potentially associated with the 1-month survival rate after OHCA. The 1-month survival rate was 1.4% (174/12,577). The proportion of 1-month survival of all OHCAs after a traffic collision origin did not increase significantly (from 1.6% [30/1919] in 2013 to 1.8% [25/1702] in 2019), and the adjusted odds ratio for 1-year increments was 1.04 (95% confidence interval, 0.96–1.12). In multivariate analysis, men who received ventricular fibrillation, pulseless electrical activity, intravenous fluid replacement, or early emergency medical service response and had a traffic collision during daytime had significantly favorable 1-month outcomes. In Japan, the 1-month survival after OHCA of a traffic collision origin was lower than that of a cardiac origin and remains stable. [ABSTRACT FROM AUTHOR]

Study Design: Best evidence synthesis. Objective: To undertake a best evidence synthesis on the burden and determinants of whiplash-associated disorders (WAD) after traffic collisions. Summary of Background Data: Previous best evidence synthesis on WAD has noted a lack of evidence regarding incidence of and risk factors for WAD. Therefore there was a warrant of a reanalyze of this body of research. Methods: A systematic search of Medline was conducted. The reviewers looked for studies on neck pain and its associated disorders published 1980-2006. Each relevant study was independently and critically reviewed by rotating pairs of reviewers. Data from studies judged to have acceptable internal validity (scientifically admissible) were abstracted into evidence tables, and provide the body of the best evidence synthesis. Results: The authors found 32 scientifically admissible studies related to the burden and determinants of WAD. In the Western world, visits to emergency rooms due to WAD have increased over the past 30 years. The annual cumulative incidence of WAD differed substantially between countries. They found that occupant seat position and collision impact direction were associated with WAD in one study. Eliminating insurance payments for pain and suffering were associated with a lower incidence of WAD injury claims in one study. Younger ages and being a female were both associated with filing claims or seeking care for WAD, although the evidence is not consistent. Preliminary evidence suggested that headrests/car seats, aimed to limiting head extension during rear-end collisions had a preventive effect on reporting WAD, especially in females. Conclusion: WAD after traffic collisions affects many people. Despite many years of research, the evidence regarding risk factors for WAD is sparse but seems to include personal, societal, and environmental factors. More research including, well-defined studies with accurate denominators for calculating risk, and better consideration of confounding factors, are needed. [ABSTRACT FROM AUTHOR]

Best evidence synthesis. To undertake a best evidence synthesis on the burden and determinants of whiplash-associated disorders (WAD) after traffic collisions. Summary of Background Data. Previous best evidence synthesis on WAD has noted a lack of evidence regarding incidence of and risk factors for WAD. Therefore there was a warrant of a reanalyze of this body of research. A systematic search of Medline was conducted. The reviewers looked for studies on neck pain and its associated disorders published 1980 –2006. Each relevant study was independently and critically reviewed by rotating pairs of reviewers. Data from studies judged to have acceptable internal validity (scientifically admissible) were abstracted into evidence tables, and provide the body of the best evidence synthesis. The authors found 32 scientifically admissible studies related to the burden and determinants of WAD. In the Western world, visits to emergency rooms due to WAD have increased over the past 30 years. The annual cumulative incidence of WAD differed substantially between countries. They found that occupant seat position and collision impact direction were associated with WAD in one study. Eliminating insurance payments for pain and suffering were associated with a lower incidence of WAD injury claims in one study. Younger ages and being a female were both associated with filing claims or seeking care for WAD, although the evidence is not consistent. Preliminary evidence suggested that headrests/car seats, aimed to limiting head extension during rear-end collisions had a preventive effect on reporting WAD, especially in females. WAD after traffic collisions affects many people. Despite many years of research, the evidence regarding risk factors for WAD is sparse but seems to include personal, societal, and environmental factors. More research including, well-defined studies with accurate denominators for calculating risk, and better consideration of confounding factors, are needed. [ABSTRACT FROM AUTHOR]

This research focuses on the application of Artificial Intelligence (AI) methodologies to the problem of classifying vehicles involved in lethal pedestrian collisions. Specifically, the vehicle type is predicted on the basis of traumatic injury suffered by casualties, exploiting machine learning algorithms. In the present study, AI-assisted diagnosis was shown to have correct prediction about 70% of the time. In pedestrians struck by trucks, more severe injuries were appreciated in the facial skeleton, lungs, major airways, liver, and spleen as well as in the sternum/clavicle/rib complex, whereas the lower extremities were more affected by fractures in pedestrians struck by cars. Although the distinction of the striking vehicle should develop beyond autopsy evidence alone, the presented approach which is novel in the realm of forensic science, is shown to be effective in building automated decision support systems. Outcomes from this system can provide valuable information after the execution of autoptic examinations supporting the forensic investigation. Preliminary results from the application of machine learning algorithms with real-world datasets seem to highlight the efficacy of the proposed approach, which could be used for further studies concerning this topic. [ABSTRACT FROM AUTHOR]

Road traffic collisions (RTC) analysis is almost a daily activity in many autopsy room. Especially when analyzing an RTC with multiple occupants in the car, it can be necessary to distinguish driver from front seat passenger in order to provide the judicial authority with elements useful to understand and to prove who was driving, considering the criminal and civil responsabilities that may derive from it. Despite this, it is beyond doubt that there is enormous difficulty in providing such information. The aim of this paper is then to evaluate whether it is possible to differentiate driver and front seat passenger in case of fatal collisions using a mathematical model based on injury pattern alone. Autopsy reports concerning 90 drivers and 60 front-seat passengers were analyzed. Statistical analysis was used to detect injuries capable of discriminating between driver and passenger, considering skin, skeletal and visceral injuries. Results show that certain skin injuries, fractures and internal organ lesions are possibly associated with drivers and front seat passenger status and the overall injury pattern seems to be able to provide useful information. A mathematical model is presented. The process to distinguishing driver from front seat passenger following fatal motor vehicle collision may use multiple sources of information, including autopsy injury pattern analysis. [ABSTRACT FROM AUTHOR]

This paper explores the tenuous link between speeding behavior and accident causation, one that has not been well established in the international literature to date. Taking advantage of established engineering conventions and formulae, we were able to set up an a priori hypothesis suitable for testing. Utilizing this formal scientific method (which GIS researchers have been criticised for not using) we establish a statistical link for this relationship. Our methodology can be used to support all police intervention strategies, including the controversial photo radar systems. The results from our research have been entered into a GIS in order to create a map for spatial display. This map illustrates the relative probability or risk of collision occurrence resulting from speeding at all intersections and interchanges within the scope of the study. It is suggested that this methodology could easily be maintained with periodic updates of data, thus creating a dynamic model from which to monitor traffic safety within the city. Furthermore, this model can be utilized to study specific strategies, allowing for the scrutiny of before, during and after effects. The study area is the entire city of Calgary, Alberta, Canada, and includes all traffic collisions occurring during the year of 1994. [ABSTRACT FROM PUBLISHER]

Filtering out the noise in traffic collision data is essential in reducing false positive rates (i.e., requiring safety investigation of sites where it is not needed) and can assist government agencies in better allocating limited resources. Previous studies have demonstrated that denoising traffic collision data is possible when there exists a true known high collision concentration location (HCCL) list to calibrate the parameters of a denoising method. However, such a list is often not readily available in practice. To this end, the present study introduces an innovative approach for denoising traffic collision data using the Ensemble Empirical Mode Decomposition (EEMD) method which is widely used for analyzing nonlinear and nonstationary data. The present study describes how to transform the traffic collision data before the data can be decomposed using the EEMD method to obtain set of Intrinsic Mode Functions (IMFs) and residue. The attributes of the IMFs were then carefully examined to denoise the data and to construct Continuous Risk Profiles (CRPs). The findings from comparing the resulting CRP profiles with CRPs in which the noise was filtered out with two different empirically calibrated weighted moving window lengths are also documented, and the results and recommendations for future research are discussed., (Published by Elsevier Ltd.)

The objective of this study was to clarify the relationship between injury severity in bicyclists involved in traffic accidents and patient outcome or type of vehicle involved in order to propose effective measures to prevent fatal bicycle injuries. Hospital records were reviewed for all patients from 2007 to 2010 who had been involved in a traffic accident while riding a bicycle and were subsequently transferred to the Shock Trauma Center of Dokkyo Medical University Koshigaya Hospital. Patient outcomes and type of vehicle that caused the injury were examined. The mechanism of injury, Abbreviated Injury Scale (AIS) score, and Injury Severity Score (ISS) of the patient were determined. A total of 115 patients' records were reviewed. The mean patient age was 47.1 ± 27.4 years. The average ISS was 23.9, with an average maximum AIS (MAIS) score of 3.7. The ISS, MAIS score, head AIS score, and chest AIS score were well correlated with patient outcome. The head AIS score was significantly higher in patients who had died (mean of 4.4); however, the ISS, MAIS score, and head AIS score did not differ significantly according to the type of vehicle involved in the accident. The mean head AIS scores were as high as 2.4 or more for accidents involving any type of vehicle. This study provides useful information for forensic pathologists who suspect head injuries in bicyclists involved in traffic accidents. To effectively reduce bicyclist fatalities from traffic accidents, helmet use should be required for all bicyclists.