Diadegma semiclausum is an important parasitoid wasp and biological control agent of diamondback moth, Plutella xylostella. Diadegma semiclausum is naturally infected with the endosymbiotic bacterium Wolbachia. In other hymenopterans, Wolbachia can influence host reproduction and fitness, but its effects in D. semiclausum are unknown. Preliminary experiments suggested Wolbachia could not be cured from D. semiclausum with antibiotics, and multi-generational exposure to antibiotics ultimately crashed lines. This led us to infer that Wolbachia is essential for reproduction. We then examined phenotypic effects following partial suppression of Wolbachia using six antibiotic concentrations (0, 0.06, 0.6, 6, and 60 mg ml⁻¹ tetracycline and rifampicin) and a non-injected control. In the exposed G0 generation, control wasps exhibited negative associations between longevity and Wolbachia density, suggesting that high Wolbachia densities reduced life span. Antibiotic-treated G0 wasps exhibited reduced Wolbachia densities but did not exhibit associations between Wolbachia density and longevity. In the next G1 generation, Wolbachia densities generally recovered across treated groups and converged on longevity patterns seen in the control group. We highlight the resilience of the Wolbachia infection in D. semiclausum wasps but also its potential costs at a high density. Future work could explore whether artificial selection could produce strains that exhibit better performance under mass-rearing conditions.
On May 22, 2020, Pakistan International Airlines flight PK-8303 crashed in Karachi, resulting in 97 fatalities. Many victims' remains were burned, fragmented, or commingled, making visual or fingerprint identification impossible. A rapid and reliable Disaster Victim Identification (DVI) process was initiated, integrating advanced forensic genetic techniques with kinship analysis to meet urgent humanitarian, cultural, and legal needs. Biological samples, including bone fragments, teeth, and soft tissue, were collected under strict chain-of-custody procedures. DNA was extracted using optimized protocols for degraded material, quantified via real-time PCR, and profiled with a high-sensitivity autosomal STR multiplex kit (GlobalFiler™). Y-STR and mitochondrial DNA sequencing were employed for cases where nuclear DNA was insufficient. Reference samples from 92 families were analyzed, and kinship likelihood ratios were calculated using dedicated forensic software. Within 16 days, complete identifications were made for 96 victims, with partial matches supporting the remaining case. The combined application of sensitive STR systems, lineage markers, and probabilistic kinship analysis significantly improved recovery rates from compromised remains and reduced turnaround times compared to standard DVI workflows. This case demonstrates how technological advances in forensic genetics can meet the humanitarian imperative of victim identification in mass disasters while ensuring legally robust outcomes.
Road traffic crashes pose a serious public safety challenge, particularly due to fatal and serious injuries. Although machine learning (ML) has been widely used for crash severity prediction, many studies remain accuracy-oriented and insufficiently address class imbalance, decision thresholds, and probabilistic reliability. This study proposes a safety-oriented and explainable ML framework for predicting killed or seriously injured (KSI) crashes using nationwide United Kingdom traffic accident data from 2020-2024. Crash severity is reformulated as a binary classification task distinguishing slight injury crashes from KSI outcomes, aligning model objectives with road safety priorities. A Light Gradient Boosting Machine (LightGBM) model is developed with imbalance handling using SMOTE, safety-oriented decision threshold optimization, and probability calibration. Model performance is evaluated using ROC-AUC, precision-recall analysis, confusion matrices, the Brier score, and a utility-based evaluation metric, while interpretability is ensured through SHapley Additive exPlanations (SHAP). Results show that default threshold settings fail to adequately detect severe crashes. At an optimized threshold of 0.35, the model achieves a Recall(KSI) of 0.605 - representing a substantial 73% improvement compared to conventional configurations - while maintaining acceptable precision. In addition, probability calibration confirms reliable risk estimation (Brier score = 0.190), supporting risk-based interpretation. Comparative analysis demonstrates that the SMOTE-based model provides a more balanced and operationally effective trade-off than class-weighted learning. SHAP analysis identifies speed limit, road class, lighting conditions, and urban context as key variables associated with KSI risk. The findings highlight the importance of safety-oriented learning design and context-aware performance interpretation for effective, risk-based traffic safety management.
Motorcycles are a common mode of transport in major Cameroonian cities, contributing to a rising burden of injuries among both users and pedestrians. These groups differ in exposure, mechanisms, and vulnerability, yet both bear a disproportionately high injury burden. However, comparative data on their epidemiological patterns and outcomes remain scarce. To support targeted prevention policies, we analysed trauma registry data to describe demographic, crash, injury, clinical, and outcome characteristics across both populations. This was a retrospective analysis of the Cameroon Trauma Registry (CTR), which collects information on injured patients presenting to 10 hospitals across seven of the 10 regions of Cameroon. Patients presenting with motorcycle-related injuries between June 1st 2022 and May 31st 2023 were assessed for demographic, crash, injury, clinical patterns of care and outcomes variables and compared using χ² or Fisher's exact tests for categorical data. The analysis was done using R version 4.2.1. A total of 2,757 motorcycle-related injury patients were included from the CTR database, including 2,339 (84.8%) motorcycle users and 418 (15.2%) pedestrians. Motorcycle users were mostly aged 15-34 years (59.1%) and males (83.0%), while pedestrians were frequently aged ≥60 years (23.4%) and females (37.8%). Helmet use among motorcycle users was low (3.0%). Alcohol involvement was more frequent among users (14.2%) than pedestrians (7.4%, p = 0.001). Most injuries occurred during work for users (33.1%) and during leisure for pedestrians (77.5%, p < 0.001). Severe multi-region injuries (abbreviated injury severity ≥3) were more common in users (21.9%) than pedestrians (16.1%, p = 0.014). Hospital admissions were high in both motorcycle users (60.2%) and pedestrians (58.4%); 6.2% required intensive care, and 2.2% underwent immediate surgery. Functional outcomes were similar: 44.8% had minor and 19.9% had major disability at discharge; 3.6% died during hospitalization. Motorcycle-related injuries in Cameroon disproportionately affect young male motorcycle users, with low helmet use, higher rates of alcohol use and severe trauma, together with older female pedestrians. Despite differing profiles, both motorcycle users and pedestrians face high disability and hospitalization rates. Targeted safety strategies are urgently needed to address these overlapping and distinct risks.
The prevalence of road traffic accidents (RTAs) worldwide is a significant cause of death, with alcohol being a considerable risk factor. This scoping review aims to assess the prevalence of alcohol use among RTA victims and explore factors influencing these accidents. Using PubMed and Google Scholar, studies on alcohol intake and its association with RTAs were identified, focusing on observational or exploratory quantitative research published in English-language peer-reviewed journals, examining country, setting, prevalence, and blood alcohol concentration levels. A total of 33 studies (2005-2024) were included, with 2 focusing on RTA prevalence among alcohol users and 31 on alcohol use among RTA victims. Studies were predominantly retrospective. The prevalence of alcohol use among RTA victims varied between 18% and 78%, with higher rates in countries where alcohol is legal. Male RTA victims and pedestrians were more likely to have alcohol in their system, with many exceeding legal BAC limits. Alcohol use is highly prevalent among individuals involved in RTAs and severity is exacerbated by BAC levels exceeding legal limits; and pedestrians are equally vulnerable. Strengthening enforcement, preventive measures, and advanced technologies, along with global policies, can reduce alcohol-related accidents.
Motor vehicle crashes (MVCs) are a leading cause of injury and death in the United States. Community-level factors, such as social vulnerability and urbanicity, have been associated with risk of death; less is known about how these factors impact nonfatal, post-injury outcomes. This study examined the association between social vulnerability and urbanicity with hospital length of stay (LOS) and hospital discharge disposition among MVC patients. Patients aged 18 years and older who were admitted to a Montana regional trauma center with a non-fatal injury following an MVC from 2016 to 2024 were included in the study. The CDC Social Vulnerability Index (SVI) was used to quantify social vulnerability at the census tract level and scores were divided into tertiles representing low, medium, and high vulnerability. Urbanicity was defined using RUCA codes based on patient residence. Generalized estimating equations with a binomial distribution were used to estimate the joint association between SVI and urbanicity with discharge disposition (home vs. facility) and prolonged LOS (≥7 days), controlling for injury severity, patient demographics, and comorbidities. Of the 668 patients, 529 (79%) were discharged home and 179 (27%) had a prolonged LOS. Among metropolitan patients, higher SVI rankings were associated with increased odds of discharge home; patients with medium and high SVI had respectively 2.6 and over 3 times greater odds of being discharged home than low SVI (medium aOR: 2.64; 95% CI: 1.96, 3.57; high aOR: 3.26; 95% CI: 2.52, 4.23). This association was not observed for non-metropolitan patients; however, patients from non-metropolitan had 2 times the odds of a prolonged LOS than those from metropolitan areas regardless of SVI (aOR: 2.03; 95% CI: 1.38, 2.98). The association between social vulnerability and discharge disposition following a MVC differed by urbanicity, and urbanicity was also associated with prolonged LOS. Further research to better understand how sociodemographic factors impact nonfatal injury outcomes can help reduce disparities in care.
Acetabular fractures are managed acutely with internal fixation but when left untreated for more than 3 weeks, these injuries lead to post-traumatic arthritis and osteonecrosis of the hip. Such complications indicate the need for total hip arthroplasty. In the Philippines, no studies have been published documenting the outcomes of managing untreated acetabular fractures with total hip arthroplasty. This study documented five cases of untreated acetabular fractures managed with total hip arthroplasty in Vicente Sotto Memorial Medical Center, Philippines in 2023. The cases included four males and one female ages 21-46 years old. All cases resulted from motorcycle crashes and most presented with untreated posterior wall fractures with posterosuperior, segmental, acetabular defects, and chronic posterior hip dislocations. The chronicity of the fractures ranged from 17 to 32.5 weeks. The cases were managed with acetabular augments when needed, dual mobility cups, and femoral short stems. In the short-term postoperative period, all cases had improved Forgotten Joint Scores and Harris Hip Scores with no incidence of infection, dislocation, or implant failure. Untreated acetabular fractures managed with total hip arthroplasty prevent post-traumatic arthritis and osteonecrosis of the hip. Segmental acetabular defects and chronic hip dislocations present in these cases can be managed with acetabular augments and dual mobility cups. When these injuries present in the young, femoral short stems can be used to preserve the femoral neck and maximize the proximal metaphyseal bone stock.
During the Covid19 pandemic restrictions, overall traffic volume decreased in Finland. Fatigue and sleepiness while driving are common risks factors for fatal motor vehicle accidents. We analyzed the effects of Covid19 pandemic restrictions on the number of Fatal sleepiness-related motor vehicle accidents (FSMVA) during and before the pandemic. All fatal motor vehicle accidents during the years 2016-2022 were studied using Finnish Road Accidents data. Of the 1226 accidents, 235 formed FSMVA group and the others the control group. FSMVA values before the pandemic restrictions were compared with the values during the pandemic period. Statistical analysis was performed with Stata 18.5. The FSMVA proportion of fatal crashes before the pandemic period was 22.7%, and during the pandemic 13.4%(p < 0.001). The COVID years were significantly associated with a lower mortality rate (fatal accidents per million vehicle-kilometers) from FSMVA(p = 0.012). According to logistic regression, the probability of FSMVA was lower in the youngest age group (OR 0.6) and higher in the early morning (OR 2.0) and mid-morning (OR 1.7). Furthermore, the incidence of FSMVA increased when the blood alcohol concentration (BAC) was ≥0.5‰ (OR 2.2). During the pandemic, predictions of FSMVA decreased in the summer months (from 27% to 13%), in the early morning (from 38% to 16%) and in the afternoon (from 21% to 9%) compared to the pre-pandemic era. Furthermore, the FSMVA was observed to be less prevalent during the pandemic, particularly among individuals under the age of 25 (8% versus 21%). Proportion of fatal crashes and mortality rate of FSMVA decreased during the pandemic period compared to the pre-pandemic era. A possible explanation for the results may be the increase in remote work, which effectively reduced drowsy driving during pandemic era.
Motorcycle crashes are a major contributor to road traffic fatalities in Cambodia, where motorcycles represent the dominant mode of transportation. Given the spatial dependence and heterogeneity inherent in crash data, this study examines spatial associations between built environment characteristics, climatic factors, and motorcycle crash frequency across 197 districts in Cambodia in 2019. Global Moran's Index was used to assess spatial autocorrelation in crash frequency and explanatory variables. After evaluating the distributional properties of crash counts and multicollinearity among predictors, several regression models were estimated and compared, including Ordinary Least Squares regression (OLS), Poisson regression (PR), Negative Binomial regression (NBR), and Geographically Weighted Negative Binomial Regression (GWNBR). The results indicate that the GWNBR model outperforms global models by more effectively capturing spatial heterogeneity in the relationships between environmental factors and motorcycle crash frequency. Several variables exhibit relatively consistent spatial association patterns across districts: road length, road density, residential land use proportion, and precipitation are positively associated with motorcycle crash frequency in many locations, whereas population density, intersection density, and the number of annual rainy days are predominantly negatively associated. By revealing spatially varying association patterns in motorcycle crashes, this study provides evidence to support geographically differentiated approaches to motorcycle safety analysis and planning in Cambodia and other low- and middle-income countries.
Pedestrian crashes are a major concern in the Northern Territory (NT), Australia, but the factors associated with these crashes differ from those in other Australian jurisdictions. This study aims to provide a comprehensive investigation of pedestrian crashes in the NT. Using police crash records and hospitalisation data, we conducted a retrospective observational analysis. We describe crash factors and calculate standardised rate ratios to compare involvement by gender, age and Aboriginal and Torres Strait Islander status. Stepwise logistic regression was used to explore crash factors contributing to fatal and 'severe injury collision' outcomes. Male pedestrians were 1.43 times (95% CI: 1.22-1.66) more likely to be involved in crashes and 1.38 times more likely to be hospitalised (95% CI: 1.23-1.56). Across all age groups, Aboriginal and Torres Strait Islander pedestrians had higher crash involvement and fatality rates. Among those aged 35-44 years, the likelihood of being involved in a fatal crash increased 35-fold (95% CI: 8.81-305.88), while severe injury risk increased 14-fold (95% CI: 8.83-23.81). Most crashes occurred during clear weather and light traffic, on straight, flat, sealed and dry roads. Factors that influence the severity of injuries in a pedestrian crash are visibility, road speed limit and driver and pedestrian alcohol and drug use. Improving lighting on roads with speed limits ≥ 70 km/h should be prioritised, or speed limits reduced where lighting upgrades are not feasible. Keeping people safe while they are intoxicated is a priority for reducing pedestrian injuries and deaths.
Child restraint systems (CRS) can effectively prevent injuries to children in road traffic crashes. However, in China, the rates of CRS ownership and use remain relatively low. We conducted a community-based intervention trial with an integrated multimodal intervention from August 2024 to March 2025 in five communities in Huangpu District, Shanghai. A total of 501 participants were recruited (201 in the intervention group, 300 in the control group). The intervention included (1) distribution of CRS educational brochures and health education materials, (2) dissemination of online educational articles, and (3) Artificial intelligence-assisted (AI-assisted) voice calls delivering standardized reminders and safety education. The control group received only pamphlets on dietary nutrition. A total of 501 participants completed the study. After the intervention, the rate of CRS ownership showed no significant difference between the intervention and control groups (90.05% versus 91.33%, p = 0.74). However, the rate of consistent CRS use was significantly higher in the intervention group (50.75%) than in the control group (38.33%) (p < 0.01). The intervention group also showed fewer orientation-related installation errors than the control group (0.00% versus 12.55%, p < 0.01). Logistic regression with interaction terms indicated no significant intervention effect on ownership rate (OR = 0.97, 95% CI: 0.41-2.30), but a significant increase in consistent use rate (OR = 2.18, 95% CI: 1.25-3.82). Further analysis showed that ownership rate was associated with child age, parental education level, vehicle price, and travel frequency, while consistent use rate was associated with household registration, travel frequency, and travel distance. An integrated community-based intervention combining conventional health education with AI-assisted follow-up improved consistent CRS use and reduced orientation-related installation errors. This approach may be a useful complement to current CRS promotion strategies.
Prehospital whole blood (PHWB) transfusion improves outcomes in trauma patients, but blood products are a scarce and costly resource. We hypothesized that massive transfusion protocol (MTP) activation could be an indicator for trauma patients who might benefit from PHWB, and we used geo-mapping to identify high need zones. We retrospectively analyzed trauma registry data from five trauma centers in Omaha and Lincoln, Nebraska, including all patients who had MTP activated in the trauma bay from 6/1/2019-3/31/2025. Assault and motor vehicle crash (MVC) data was collected from the Nebraska Department of Transportation and local police databases. Incidence of MTP, assaults, and MVCs was mapped to identify the highest need zones. Chi-square tests of independence and Pearson and Spearman correlations compared MTP incidence by ZIP Code Tabulated Areas (ZCTA's) with known trauma events. A total of 338 MTP patients from Omaha and 89 from Lincoln were included. Geo-mapping revealed a greater need for PHWB in the downtown centers of both cities. Tests of independence showed significant associations between MTP incidence (Omaha: χ² = 741.22, df = 28, p < 0.001; Lincoln: χ² = 43.75, df = 13, p < 0.001). Spearman and Pearson correlations showed a positive linear correlation between MTP incidence and trauma incidence. Geo-mapping MTP data strongly correlated with known traumas, supporting MTP activation as a surrogate marker for PHWB need. This offers a novel method for cities to plan PHWB programs by determining high need zones and ensuring equitable and cost-effective distribution of scarce resources.
1. Older cars without WPS were linked to higher long-term WAD and PMI, especially in men. 2. WPS offer more protection to men than women in rear-end collisions. 3. Long-term WAD leads to sustained increases in sickness absence and disability days. 4. Newer cars without WPS still show increased risk for men. 5. Improved WPS design is needed to better protect women in crashes. The online version contains supplementary material available at 10.1186/s12889-026-27404-2. Whiplash-associated disorders (WAD) are common injuries from car crashes, with a significant proportion leading to permanent medical impairment (PMI), especially among women and in rear-end impacts. While previous research has shown mixed results regarding work incapacity following WAD, severe cases may lead to long-term health and functional consequences. This study aimed to examine the occurrence of long-term WAD – based on insurance compensation, future PMI, and work incapacity (sickness absence (SA) and disability pension (DP)) among front seat occupants in rear-end crashes, and to assess how seat design with or without whiplash protection systems (WPS) relates to these outcomes. The cohort (N = 14,363) was selected from car crashes occurring 2001–2013, reported to the Folksam Insurance Group. Injury- and car-related information was linked with nationwide register data regarding sociodemographics, inpatient and specialized outpatient healthcare, SA, and DP. The SA + DP days/year were calculated in relation to the crash date. Logistic regression analyses were performed with variables combining WPS and car model year of introduction (< 1998 and ≥ 1998) for long-term WAD, PMI, and > 90 days of all diagnoses and whiplash-specific SA/DP in year two after the crash. Analyses were stratified by sex. Among women, 18% sustained long-term WAD and 12% resulted in PMI. Among men,13% and 9%, respectively. Among occupants with long-term WAD, there was a substantial increase in mean SA/DP days/year, WAD specific and all diagnoses, after the crash and remained high three years after. Compared to occupants in cars with WPS, occupants in older cars (< 1998) without WPS were more likely to have long-term WAD (Women: OR:1.8, 95% confidence intervals (CI):1.5–2.2; Men: OR:2.2, 95% CI:1.7–2.8), and PMI (Women: OR:1.4, 95% CI:1.1–1.8; Men: OR:2.3, 95% CI:1.6–3.3). Among men, also occupants in newer cars without WPS were more likely to have long-term WAD (OR:1.6; 95% CI:1.2-2.0) and PMI (OR:1.9; 95% CI;1.3–2.7). Among men, but not women, those injured in older cars without WPS were more likely to have > 90 days SA/DP in year two following the crash (OR:1.8; 95% CI: 1.2–2.6). WPS were associated with a lower risk of long-term WAD, PMI, and prolonged SA/DP, particularly among men, with substantial less benefit for women. These sex differences highlight the need to improve WPS performance, especially for women. The online version contains supplementary material available at 10.1186/s12889-026-27404-2.
Crash data provide objective safety metrics but are rare and often unsuitable for proactive safety management. In contrast, traffic conflict indicators (e.g., time-to-collision, TTC) offer continuous measures of proximity to collision but require thresholds to separate routine from safety-critical events. Extreme Value Theory (EVT)-based approaches define statistically defensible thresholds from the tail behavior of conflict indicators, but these thresholds are not explicitly tied to observable maneuver adaptations. This study instead models drivers' discrete maneuver adjustments under varying conflict indicator levels and extracts candidate behavioral thresholds (CBTs) from the resulting maneuver-response probability profiles. A Latent Class Logit Kernel (LC-LK) framework is proposed to identify CBTs by modeling drivers' maneuver choice under conflict. The LC-LK model distinguishes between low- and high-risk behavioral classes and allows each driver to express a probabilistic mixture of both states, capturing intra-driver heterogeneity. This heterogeneity refers to variation in the behavior of the same driver under different levels of conflict severity (as indexed by the indicator). The framework also incorporates correlation in alternatives through logit-kernel structured error components that represent shared unobserved influences among maneuvers involving similar kinematic adjustments (deceleration, acceleration, or turning). This design produces probability curves that describe how the likelihood of high-risk maneuvers changes with conflict indicator values. From these profiles, CBTs such as inflection points, crossovers, and tail-based thresholds can be derived. The framework is guided by four behavioral hypotheses: (i) drivers simultaneously exhibit varying degrees of membership in both low- and high-risk behavioral classes; (ii) class membership shifts systematically with conflict indicator values; (iii) this relationship often follows a logistic shape, with stable behavior across safe conditions and rapid transitions once critical values are reached; and (iv) even in free-flow conditions, drivers maintain a baseline level of caution. Application to naturalistic roundabout trajectories demonstrated the framework's diagnostic power. For TTC, stable behavioral inflections were observed between 0.8-1.1s, indicating clear transitions from low- to high-risk driving. In contrast, a modified variant of TTC (MTTC2) produced unstable and implausible thresholds (≈34s). This divergence suggested that not all indicators support identifiable behavioral transitions. One tentative interpretation, which we report cautiously, is that indicators that require more complex information (e.g., MTTC2) may be harder for drivers to process during routine driving. Simpler indicators such as TTC appear to yield more stable behavioral patterns, but further evidence is required to substantiate this explanation. The proposed framework is intended to complement and not replace EVT. The LC-LK model produces multiple CBTs, each capturing different aspects of behavioral transitions. Some CBTs showed consistency with EVT-derived thresholds and others diverged substantially. A systematic investigation is needed to determine which CBT should be used or how behavioral thresholds should be integrated with statistical and practice-based thresholds.
Wrong-turn violations in safety-critical spaces such as road roundabouts are a type of traffic violation that can lead to traffic congestion and increase the risk of road crashes. Although many researchers have focused on detecting various traffic violations, wrong-turn violations have not received enough attention. This may be due to a lack of relevant datasets. This study aims to address this gap. We developed a deep learning-based approach to detect wrong-turn traffic violations at roundabouts. The proposed system captures video from strategically placed cameras at roundabouts, which is then fed into an artificial intelligence (AI) model capable of detecting vehicles committing wrong-turn violations in real time. For this purpose, we utilized the popular You Only Look Once (YOLO) algorithm. Due to the absence of an existing dataset for this specific type of violation, we created our own. Images were collected and annotated from local roundabouts in Peshawar, Pakistan. The YOLO model was trained on this dataset and evaluated using standard performance metrics, including accuracy and recall. The results suggest that the proposed approach has strong potential for refinement and real-world implementation.
In road environments with large Autonomous Vehicle (AV) fleets and higher SAE automation levels, reliable crash data are often unavailable, making direct safety assessment infeasible. In such cases, traffic simulation offers a valuable alternative for evaluating safety. This study conducts a spatial modelling analysis to predict crash hotspot occurrences under different AV deployment scenarios. The study combines microsimulation-derived conflict data, a quantitative crash-risk formulation, validated using field crash data, based on Time-To-Collision (TTC) thresholds, and spatial statistical analysis using the Getis-Ord Gi* statistic to detect statistically significant hotspots of elevated crash risk. The resulting hotspots were further analysed using a binomial Generalised Additive Model (GAM) to quantify the impact of automation, roadway and spatial factors on the probability that a conflict event occurs within a hotspot area. Results show that automation significantly alters the spatial distribution of crash risk, leading to a gradual reduction and spatial diffusion of hotspots as AV penetration increases. However, a temporary rise in the probability that conflict events occur within hotspot areas occurs under moderate automation shares, highlighting the transitional instability of mixed-traffic conditions. Intersections and other high-interaction areas remained the most critical locations, while congested segments were associated with a higher probability that conflict events occur within hotspot areas. The proposed framework supports data-informed planning and policy decisions during the transition toward automated urban mobility.
Hazard perception plays a pivotal role in preventing road accidents. Despite control measures in developing countries, crash and injury rates remain high, indicating limitations in current strategies. Framed within Endsley's Situational Awareness Theory, this study examined associations between crash history, traffic penalties, and demographic factors (age and education) with hazard perception among Iranian professional drivers. This cross-sectional study included 220 Iranian professional car drivers (age range: 23-75 years; M = 48.3, SD = 10.4). Participants completed a demographic questionnaire and the standardized Hazard Perception Test (HPT) validated for the Iranian context. Data were analyzed using univariate tests and a General Linear Model (GLM). The mean hazard perception score was remarkably low at 35.60 ± 15.68 (out of 100), with an average error rate of 3.68 ± 1.91 missed hazards. Drivers with no crash history in the past three years scored 11.68 points higher on average than those with crash involvement (p < 0.001). Higher penalty frequency was associated with lower hazard perception scores (p < 0.001). In the GLM, crash history (β = 11.68, 95% CI: 8.14-15.21, p < 0.001) and penalty frequency (β = -3.62, p < 0.001) remained significant predictors, while age, education, gender, and driving experience showed no independent association (all p > 0.05). This performance level is substantially lower than scores typically reported in high-income countries with mandatory HPT in licensing (often >50-60% among experienced drivers). Crash and penalty history were strongly linked to poorer hazard perception, highlighting behavioral factors as key risk markers. The HPT effectively distinguished high- from low-risk drivers, supporting its use as a screening tool for targeted interventions in settings with limited systemic protections. These findings extend Situational Awareness theory to low- and middle-income contexts and emphasize the need for context-specific training.
Variable Speed Limits (VSL) are primarily designed to enhance traffic safety on hazardous roadways by dynamically adjusting speed limits based on real-time traffic and weather conditions. Most previous studies have focused on using VSLs to improve safety, mitigate congestion, and reduce environmental impact, assuming a securely connected VSL network. But what would be the impact of cyberattacks on VSL systems on traffic safety and network efficiency. This paper investigates the impact of intentional disruptions on VSL signs within traffic networks. We present a threat model to identify vulnerabilities in the VSL communication network and potential access points for attackers. An analytical accident model was developed, and various VSL disruption scenarios were simulated using a case study of Highway 1 in British Columbia, Canada, in SUMO (Simulation of Urban MObility) to assess the potential for crashes and safety problems resulting from VSL disruptions. Surrogate Safety Measures (SSM) were employed as key measures of safety concerns. Our findings highlight notable risks, including up to 56% additional conflicts, posed by these intentional disruptions. A robustness assessment using Monte Carlo simulation runs of a scenario with heterogeneous driver behaviors demonstrated that the safety impact is consistent and not random seed-dependent. Across runs, intentional disruptions still yielded significant increases in conflicts, with an average rise of 24.95%, confirming the stability of the results. Various mitigation strategies are discussed to enhance traffic safety and resilience against VSL manipulation.
Systematic video analysis of events is a widely applied method for examining the mechanisms and underlying causes of sports injuries. Yet, the use of multiple raters poses a considerable challenge, as achieving high inter-rater reliability in video-based assessments is inherently difficult. This study evaluated the inter- and intra-rater reliability of video analysis for identifying events leading to potential injuries in winter sports, focusing on snowboard cross (SBX) and ski cross (SX). A team of four (4) raters reviewed the video footage. 644 situations were reviewed, categorized by parameters such as crash type, course trajectory, and competitor behaviour. A standardized process was established for training the raters to classify defined situations as Crash (CR), Time of no return (TNR), Rank Shift (RS), Out of balance (OOB), Contact (CT), Avoided Contact (ACT). Inter-rater reliability was assessed using Fleiss' Kappa and Cronbach's Alpha, while Cohen's Kappa was used to evaluate intra-rater reliability. Categories with distinct, easily identifiable outcomes, such as time of no return and crash, exhibited high inter-rater reliability. Only minor differences exist in the literal interpretation of the values for inter-rater reliability between Cronbach's Alpha and Fleiss' Kappa. Categories with more nuanced interpretation, such as out-of-balance situations and athlete contact, showed moderate reliability. In contrast, categories like avoided contact showed lower reliability values Intra-rater reliability ranges from fair to moderate across all raters. Clearly identifiable events such as CR and TNR were recognized perfectly, while the other categories show a more ambiguous pattern. This study advances the field of sports analysis by proposing a standardized methodology for video analysis in sports with high injury incidence, specifically SBX and SX. Categories with very clear definitions of situations were identified with high inter-rater reliability (CR TNR). Others were classified with moderate accuracy across raters (RS, OOB, CT), whereas some categories could not be reliably distinguished (ACT), even following structured training. The same pattern could also be observed in the intra-rater reliability. This method allows for a higher volume of cases to be reliably analysed, which could inform more robust injury prevention strategies.
The velocity of urban functional expansion often outpaces the evolution of safety governance, creating a critical spatiotemporal mismatch that undermines traffic safety. However, existing studies typically rely on static snapshots, failing to capture the dynamic migration of these traffic accident risks or decode the evolving underlying mechanisms behind the governance deficit. To investigate the spatiotemporal evolution of this mismatch and analyze its underlying behavioral and structural causes, this research proposed a novel diagnostic framework integrating Geographically Weighted Regression with Random Forest interpretation. Using a decade of crash data (2013-2022) from a rapidly urbanizing Chinese city, we reconstructed the spatiotemporal trajectory of high-risk mismatch zones, where observed crash frequencies systematically exceed the levels predicted by static built environment factors. The empirical results indicate that this safety-governance mismatch manifests through three critical dimensions: spatiotemporal migration, mechanism heterogeneity, and spatial variation in crash patterns. First, spatially, the center of gravity of safety risks has shifted systematically from the mature city center to the expanding suburban fringe, empirically verifying the lagging governance hypothesis. Second, temporally, the dominant risk factors have fundamentally shifted. Behavioral factors and conflict patterns consistently dominate the risk hierarchy over the decade, while infrastructure factors remain marginal. This confirms that the governance deficit is a management deficit rather than a physical infrastructure gap. Finally, through mechanism deciphering, a differential diagnosis isolates the unique crash patterns of these high-risk zones. Unlike the congestion-induced passive errors in the core, these fringe zones are characterized by Improper Operation and severe Vehicle-Non-Motor conflicts, reflecting a critical gap in managing mixed traffic flows. The successful application of this diagnostic framework demonstrates its efficacy in identifying the blind spots of traditional static management. These findings challenge the traditional "build-first, manage-later" paradigm. Instead, a dynamic, precision-based governance framework is advocated to synchronize safety management with the spatiotemporal evolution of urban risks.