Heavy-duty trucks (HDTs) are a dominant source of urban nitrogen oxide (NOX) emissions, yet their in-use emissions often diverge from regulatory limits. We present a multi-source data fusion framework to map truck NOX emissions at high resolution and evaluate targeted mitigation strategies for sustainable urban management. Using Shanghai as a case study, we integrate over one billion trajectory records, 2,513 plume-chasing tests, and remote on-board diagnostics (OBD) data from 40,726 HDTs, fused to derive emission factor distributions and activity levels. These data feed into a dynamic, road-level, hourly inventory disaggregated by emission standard and usage pattern. Results reveal strong spatial heterogeneity, with freight corridors and port-related links as hotspots, and highly skewed distributions within the fleet: the top 20% of trucks contribute 44% of total NOX, including not only China IV vehicles but also China V and poorly performing China VI models. We compared two control strategies: phasing out all China IV trucks versus targeted removal of the highest-emitting 20%. Under an idealized high-emitter prioritization scenario, the targeted strategy achieves ∼40% greater overall reduction and delivers larger benefits on major freight corridors. These findings highlight the potential of multi-source big data for targeted emission management, offering an effective pathway toward cleaner, more sustainable cities.
This review aims to fill a critical gap-phytosanitation methods for inert surfaces, such as farming equipment, containers, tools, and shoes, to mitigate plant pathogen establishment and expansion. Although a core component to food system security and ecosystem stability, especially for mitigating emerging pathogen impacts, this is one of the least studied disease management tools and often lacks robust science-based practices. We herein synthesize what is known about inert surface phytosanitation practices across diverse microbiological and plant pest systems and highlight opportunities for improving both phytosanitation practices and approaches used for phytosanitation science. Basic frameworks are first established for the types of plant pathogen propagules that are spread on surfaces and dispersal risks posed by key inert surface types. This is followed by a discussion of primary surface phytosanitation methods, including physical, chemical, and heat-based approaches. Case studies of nursery/greenhouse and farm equipment phytosanitation are used to demonstrate both systems-strategies for application of phytosanitation best management practices and methods for developing science-based practices using the hazard analysis for critical control points (HACCP) approach. Opportunities for growth discussed throughout include the use of pathogen-specific analyses and epidemiological modeling to improve phytosanitation science and engineering advancements to make phytosanitation practices more efficient. Taken together, it is hoped that this synthesis can both function as a resource for research/extension practitioners looking to develop and improve phytosanitation practices and provide impetus for innovation.
[This corrects the article DOI: 10.3389/fvets.2026.1789484.].
Accurately estimating the causal effect of human factors on crash severity in mountainous freeways is pivotal for developing effective safety strategies. Although previous studies have investigated human factors, they typically focus on estimating average effects under specific conditions, often conflating statistical correlations with causal relationships. Consequently, the underlying causal mechanisms in freeway crashes remain unclear. To address this limitation, this study proposes a dynamic weighted double machine learning framework that integrates LightGBM and XGBoost models to estimate confounder-outcome and treatment-outcome relationships. By optimizing model weights and using non-human-caused crashes as a control group, the effects of five human factors are isolated through counterfactual reasoning. The heterogeneous treatment effects of human factors on crash severity are quantified, and causal relationships are analyzed across variables such as weather, slope, truck traffic volume, and vehicle type. The results reveal significant heterogeneity in crash severity attributable to human factors compared to non-human factors. Inadequate safety distance often co-occurs with other high-risk conditions, amplifying crash severity. Reversing behavior is particularly sensitive to weather conditions. The causal pathways of distracted or fatigued driving and driving in the wrong lane are influenced by daily truck traffic volume and road slope. Additionally, interactions between slope and vehicle type significantly affect the severity of overloaded crashes. These findings underscore the need for targeted interventions addressing specific human factors in high-risk scenarios. Consequently, enforcement against reversing and overloading on steep slopes with high truck volume should be intensified, and heavy trucks should be restricted to right lanes on high-risk segments.
Food trucks (FTs) are becoming increasingly popular in Saudi Arabia. However, inadequate food safety practices and limited consumer awareness of foodborne pathogens may increase the risk of foodborne illnesses associated with consumption from FTs. Therefore, we assessed consumer knowledge, attitudes, associations with sociodemographic characteristics, and dietary patterns among FT customers. A cross-sectional study was conducted in the Makkah region, Saudi Arabia, between March to October 2025. An online questionnaire was completed by 500 adults covering sociodemographic characteristics, food safety knowledge, attitudes, pathogen awareness, and dietary intake. Data were analyzed using the independent t-test, one-way analysis of variance, and linear regression. The mean scores for awareness, knowledge, and attitudes were 16.7 ± 3.8, 14.7 ± 3.3, and 30.8 ± 5.3, respectively. Women had considerably higher knowledge and awareness scores than men (p < 0.0001). Significant correlations were found between food safety knowledge and attitude scores (r  =  0.531, p  <  0.001), food safety knowledge and awareness scores (r  =  0.633, p  <  0.001), and attitude and awareness scores (r  =  0.429, p  <  0.001). A trend toward high fruit consumption was observed among participants with high knowledge and awareness scores. These findings highlight the need for targeted consumer education to improve pathogen awareness among FT customers.
Road traffic crashes remain a leading cause of fatalities worldwide, underscoring the need for accurate data to guide prevention strategies and evidence-based policymaking. However, crash databases often suffer from misclassification, underreporting, and inconsistencies, particularly in alcohol-involved cases, which limits the reliability of safety analyses. This study addresses this issue by identifying and quantifying Misclassified Alcohol-Involved Crashes (MAICs) using a Natural Language Processing (NLP) framework based on the BERT model. The framework analyzed 371,062 crash records from Iowa (2016-2022) and identified 3,895 misclassified alcohol-involved crashes (MAICs) out of 19,177 alcohol-involved cases predicted by the model, corresponding to an overall misclassification rate of 20.35% and a confidence interval of 18.86%-21.85%. To examine the factors contributing to these errors, a mixed-effects Probit Logit regression model was applied, incorporating behavioral, environmental, and roadway attributes. Results indicated that fatal and nighttime crashes were less likely to be misclassified, whereas crashes involving older or younger drivers, heavy trucks, and vulnerable road users showed higher odds of misclassification. A Local Indicators of Spatial Association (LISA) analysis revealed significant county-level clusters of misclassifications, suggesting regional differences in enforcement and reporting practices.
Harmful Algal Blooms (HABs) are pervasive in freshwater and marine waters requiring advances in monitoring, prevention and control of active blooms. Prevention through interception of nutrient runoff that feed HABs is critical to reducing future impacts. This research provides a logical convergence of complementary goals of mitigating the severity of HABs and increasing beneficial use of dredged sediment conveniently sourced from Confined Disposal Facilities (CDFs). This work innovates use of additive manufacturing (AM), or 3D printing, to generate porous, geometrically complex sediment structures to adsorb and intercept nutrients in runoff water prior to discharge into surface water. While this technology application is widely applicable, proof-of-principle of nutrient removal capability is demonstrated using a 3D printed dredged sediment mound that was strategically placed on a 3 by 3-foot physical landscape model that was also completely 3D printed. The sediment mound structure was specifically printed into parallel roadways forming channels to increase preferential infiltration of run-off water into the otherwise clayey sediment. Nutrients were measured before and after pumped runoff water passed through either amended (biochar, commercial resins) or unamended dredged sediment structures and demonstrated that the unamended marine-sourced dredged sediment had sufficient capacity to almost immediately reduce concentrations of phosphate by 79 to 98% and nitrate by 93 to 99% from runoff discharge. This concept may be scaled using hoppers, excavators and trucks to move sediment from nearby CDFs to a runoff site for field demonstration and application.
Slopes often emerge as traffic bottlenecks, yet not all slopes lead to congestion. The relationship between slope capacity and factors like grade and length is complex and non-linear. Accurately estimating road slope capacity and mitigating traffic congestion remain challenges in traffic management. Drivers instinctively adjust vehicle acceleration or braking to counteract gravity, influencing vehicle speed and road capacity. However, traditional models often overlook these compensatory adjustments, leading to inaccurate predictions. This study introduces a novel approach using vehicle trajectory data and the Expectation-Maximization (EM) algorithm to estimate driver compensations on slopes. The algorithm separates observed acceleration into baseline (flat road) and compensatory components. Data from field experiments with cars, trucks, and buses reveal that compensatory acceleration decreases with speed and remains predictable across different slopes. These findings enhance our understanding of slope impacts on traffic flow and provide valuable insights for traffic management and infrastructure design.
This study presents an integrated controller and estimator technique, using a stationary reference frame (αβ) system, for a surface permanent magnet synchronous motor (PMSM) drive system. This paper aims to develop an improved sensorless vector control where the Field Oriented Control (FOC) method and the speed estimation are performed together, as opposed to being separate functions in the conventional method for a PMSM drive. In the proposed technique, current control and motor speed - rotor position estimations are performed based on current sensor feedback only. The speed is estimated from αβ-currents of the motor, thereby eliminating the need for speed sensor. The current control loop of the vector control is implemented using αβ-currents instead of dq-currents. The main advantages of this proposed approach are reduced circuitry and simple control configuration. The key benefits include reduction of sensors, controller complexity, computing complexity, and cost. The proposed integrated approach is theoretically investigated and simulated for the PMSM drive model. Using the Opal-RT configuration, the performance is also assessed in real-time. The proposed control strategy shows promise in meeting the closed-loop drive system's speed tracking requirements. A comparison of the simulation results between the conventional and proposed models validates the effectiveness of the proposed technique for speed control applications. When compared to the conventional control method, the proposed model's performance improved in terms of different transient levels, speed responses and evaluation metrics.
The number of motor vehicles have experienced significant growth over the past few decades as the economy continues to grow and urbanization accelerates. This phenomenon creates an urgent need for smart transportation technology. This paper aims to explore a prediction method suitable for predicting traffic flow in public transportation systems and the challenge of coping with the impact of abnormal traffic events on prediction. To this end, we propose a deep learning approach using dynamic graph networks and multi-head attention mechanisms to develop a traffic flow prediction method based on spatio-temporal multi-source information fusion. In addition, this paper also proposes an urban road traffic prediction method suitable for normal and abnormal traffic conditions. This method employs a deep learning framework to process traffic and accident data through dynamic graph networks and multi-task learning. The experimental results on three real traffic datasets, namely PEMS04, PEMS08 and Highways England, show that the proposed model has significantly better MAE than the optimal baseline AGCRN on PEMS04. On the England dataset, RMSE was 18.7% lower than DCRNN. In terms of long-term prediction, the MAE predicted by the model at 60 min is still lower than the baseline. Extensive experiments on a real-world dataset validate the effectiveness of our model. This study provides a novel perspective and solution for smart city traffic prediction, while developing a predictive tool for abnormal events, thereby enhancing the intelligence level of traffic management.
Although rollovers account for 3% of vehicle crashes, they result in approximately one-third of all occupant deaths. This study investigates the effects of human, vehicle, and environmental factors on the occurrence and severity of rollovers in single-vehicle crashes. We emphasize the importance of a safe system approach that incorporates safer speeds, safer vehicles, and equitable design to prevent and mitigate rollover crashes while addressing the diverse needs and challenges of all road users. Using data from NHTSA's Crash Report Sampling System and the New Car Assessment Program's Safety Ratings, we applied logit regression and XGBoost models to identify the significant predictors of rollover likelihood and injury severity. We estimated the Levels of Automation in vehicles and their impact on the occurrence and severity of rollovers. We also used SHAP analysis to interpret the XGBoost model predictions. Our findings reveal that younger drivers, impairments, and device-related distractions on high-speed limit roadways increase rollover risks, while higher vehicle automation levels and seatbelt usage reduce them. We also find that environmental factors, such as road alignments and surface conditions, have complex impacts on rollover occurrence and severity. Notably, NHTSA's rollover possibility value, with a significant positive coefficient of 1.54, indicates that rollovers are more likely to occur as the values rise. We highlight the potential of emerging vehicle technologies to reduce rollover vulnerability. Additionally, we emphasize the need for inclusive road safety measures that cater to the needs of all road users. These insights provide valuable guidance for future transportation safety strategies.
Insect-Inspired Flapping-wing Micro Aerial Vehicles (FWMAVs) have attracted significant attention due to their unique advantages in agility, manoeuvrability, low noise, and adaptability to cluttered environments. Over the past two decades, research in this field has progressed from early conceptual demonstrations to more advanced platforms capable of hovering, rapid manoeuvres and limited autonomous flight. This review summarizes the historical development of FWMAVs, highlights key unsteady aerodynamic mechanisms such as the leading-edge vortex, wake capture, clap-and-fling, rotational lift and added-mass effects, and analyses their roles in enabling lift enhancement under low Reynolds number conditions. Actuation approaches including motor-driven, piezoelectric, electromagnetic and emerging soft-material-based systems are examined, together with structural innovations in wing configurations such as two-wing, four-wing, X-wing, and multi-wing architectures. Control strategies for tailless vehicles, including wing-kinematics modulation, attitude feedback control and onboard sensing, are systematically reviewed. Despite significant progress, current FWMAVs still face major challenges in energy efficiency, endurance, lack of adaptability to different environments, environmental robustness and material limitations. Future development will require integration across disciplines such as smart materials, high-efficiency power systems, micro-fabrication and advanced control algorithms to achieve truly autonomous, robust, and long-range bio-inspired flight.
The aim of this scoping review was to provide an overview and classification of the studies on workplace health interventions targeted at commercial heavy vehicle drivers. Of the 36 publications included, 28 concerned solely truck drivers, five solely bus drivers, and the remaining three included professionals also from other related fields. The participants were mostly male. Most of the studies (20 publications) examined individual-focused interventions, seven studies focused on organizational interventions, and nine studies addressed both. None of the interventions focused on improving drivers' access to healthcare. More than half of the studies included some form of technological tools. The duration of the interventions was predominantly several months; however, only eight studies reported results after a follow-up period. For future studies, four areas for development were identified. First, collecting and reporting gender-disaggregated data is required to address the unique health needs among female drivers. Second, when using technology, consideration should be given to its usability across the entire workforce and the support needed from human professionals. Third, including systematic follow-up periods would help to understand how long-lasting a behavior change is and what impact it has on health outcomes. Finally, further studies focusing on organizational interventions are especially needed.
Hydrocarbon pneumonitis is an uncommon condition that can radiologically mimic malignancy, particularly in patients with risk factors for lung cancer. We report the case of a 52-year-old male truck driver with a 15 pack-year smoking history who presented with persistent cough and mild weight loss. Initial imaging revealed a mass-like consolidation in the right middle lobe (RML) and multiple hepatic lesions, raising concern for metastatic lung cancer. Comprehensive history-taking eventually revealed a single episode of gasoline siphonage approximately 10 days prior to symptom onset, involving direct oral suction of fuel for several seconds, with estimated aspiration of a small volume (< 5 mL). This acute, high-intensity exposure preceded symptom onset and was pivotal in redirecting the diagnosis. Further radiological evaluation confirmed hydrocarbon pneumonitis with incidental hepatic hemangiomas, avoiding unnecessary invasive procedures. This case highlights the critical importance of thorough occupational and environmental exposure history in patients presenting with pulmonary opacities, particularly when radiological findings suggest malignancy.
Motor-vehicle crashes (MVCs) are the leading cause of work-related death among U.S. Oil and Gas Extraction (OGE) industry workers. It is possible that seat belt use could have contributed to reducing MVC-related deaths. Although research has identified instances of non-seat belt use in this industry, reasons behind this behavior are not well understood. A convenience sample of OGE employees participated in interviews (n = 18 workers) and three focus groups (n = 9 supervisors). Instruments incorporated theoretical constructs from the Health Belief Model (HBM) to obtain perceptions and experiences that might impact work-related seat belt use. Worker and supervisor data were qualitatively analyzed and then triangulated to cross-validate findings and ensure data saturation. Themes were deductively aligned with HBM constructs. Most participants reported always using their seat belts or observing employee seat belt use. Some participants acknowledged circumstances when they or others might not wear a seat belt. Four primary themes emerged: Situational risk and readiness, Company behavioral norms, Belief-based barriers, and Personal safety standards. Within each theme, several codes provided additional insights that aligned with HBM constructs including perceived susceptibility to experiencing an MVC and severity of the outcome; perceived barriers and benefits to using a seatbelt; and cues to action that prompted seat belt use over time. Conclusions/Practical applications: Our data suggest that worker knowledge of company programs and associated consequences of non-compliance, including supervisory enforcement, support seat belt use. State laws and technology found in company vehicles such as in-vehicle monitoring systems and phone apps also influence seat belt use. Future research may explore how to integrate these aspects into a driving safety program that serves as a comprehensive cue to action hence, enhancing perceived benefits of seat belt use, correcting misconceptions about barriers, and incorporating strategic messaging, training, and education during onboarding and at times of heighted perceived susceptibility and severity.
Due to alarming mortality rates in the 1960-70s, a central decision was made to centralize preterm infant care. Following an Anglo-Saxon model based on regionalization and a network of progressive care, nine perinatal intensive centers were already operating nationwide by 1979. However, a specialized neonatal transport service was only launched in the central region in 1989 with the Peter Cerny Foundation Ambulance Service. Its history can be divided into four periods: I. Transport with general-purpose ambulances and staff (1976-1988): guided by the "scoop and run!" principle (also known as "gas-pedal therapy"). Neonatal transport was performed nationwide by emergency medical (oxyological) ambulances, supplemented by some neonatal equipment. A leading figure in developing ward and transport care and the creative domestic adaptation of Western techniques was Professor János Kiszel. II. Specially designed neonatal ambulance (1989-2008): acting as the "extended arm" of the intensive care center, this era was built on the principle: "Arrive as soon as possible, start or take over resuscitation, stabilize the patient, and make them transportable." Most neonatal transport services nationwide still operate primarily on this principle. III. Multifunctional neonatal ambulances (2009-2017): equipped with dual incubators, this model was based on the principle: "Replace unnecessary transport with on-site deployable telemedicine." Its development in the central region was justified by the high volume of round-trip transports for diagnostic purposes. In this "transport-replacing" activity, the diagnostic equipment travels to the patient rather than the patient to the diagnostic center. IV. Next-generation transport model (2018-2025): this concept ensures definitive care in the central region of Hungary by following the principle: "Don't just stabilize and prepare for transport, but become the most modern mobile intensive unit right on-site." This model is not satisfied with widely used "safe" transport procedures and tools; instead, it strives to bring the full arsenal of the latest diagnostic and therapeutic equipment to the scene. Orv Hetil. 2026; 167(23): 891-904. Az 1960–70-es évek riasztó mortalitási adatai miatt központi döntés született a koraszülött-ellátás centralizációjáról. Az angolszász mintára, a regionalitás és a progresszív ellátás szerinti hálózatban 1979-ben országosan 9 perinatális intenzív központ működött, de speciális neonatológiai transzportszolgálat csak 1989-től indult a Peter Cerny Alapítványi Mentőszolgálattal. A központi régiós transzport fejlődéstörténete négy korszakra bontható: I. Általános felszerelésű mentővel és személyzettel történő transzport (1976–1988), amelynek vezérelve a „kapd fel és rohanj!” („gázpedál-terápia”) volt. Az újszülöttek szállítását országosan oxiológiai mentőegységek végezték, néhány neonatológiai eszközzel kiegészítve. Az osztályos és a transzportellátások fejlesztésében, a nyugati technikák kreatív hazai adaptálásában az egyik vezéregyéniség dr. Kiszel János professzor volt. II. Az intenzív központ kinyújtott karjaként működő (1989–2008), speciálisan kialakított neonatológiai mentőegység a „mielőbb érj ki, kezdd el/vedd át az élesztést, stabilizáld és hozd szállítható állapotba!” elvre épült. Az adott terület speciális igényeire szabott neonatológiai transzportszolgálatok országosan többnyire ma is ezen az elven működnek. III. Multifunkciós neonatológiai rohamkocsi (2009–2017), két inkubátorral felszerelve, a „váltsd ki a felesleges szállításokat, helyszínre telepíthető telemedicinával” elven alapult. Fejlesztését a központi régióban a diagnosztikai célú oda-vissza szállítások nagy száma indokolta. A szállítást kiváltó tevékenység során nem a beteg utazik a vizsgálat helyére, hanem a vizsgálóeszköz. IV. Az újgenerációs transzportmodell (2018–2025) a „ne csak stabilizáld és készítsd fel a transzportra, hanem már a helyszínen légy a legmodernebb mozgó intenzív egység” koncepció szerint biztosítja a központi régióban a definitív ellátást. A modell nem elégszik meg az általánosan elterjedt, biztonságosnak tartott transzporteljárásokkal, -eszközökkel, hanem a legmodernebb diagnosztikus és terápiás felszerelések teljes arzenálját igyekszik a helyszínre juttatni. Orv Hetil. 2026; 167(23): 891–904.
Emergency response for medical incidents is increasingly extended by community first responder (CFR) systems that dispatch nearby trained volunteers. The implementation of CFR systems has led to significant decreases in emergency response times, especially in rural areas where ambulances take longer to arrive. CFR systems that dispatch volunteers to various emergency types can increase their effectiveness by training their volunteers, enabling these volunteers to provide first aid for more emergency types. We study the problem of optimizing a CFR system's training strategy to maximize its effectiveness given a limited budget, where the effectiveness is measured by the probability that at least one volunteer arrives before the ambulance for any given incident. We introduce an optimization model that explicitly accounts for the heterogeneous nature of volunteers' availability and locations, as well as a solution approach that efficiently obtains optimal solutions for realistically-sized instances. We apply the optimization approach to a CFR system operating in Lincolnshire, United Kingdom. The results show that the optimization approach yields substantially larger improvements in the CFR system's effectiveness compared to several intuitive greedy training strategies. Additionally, dispatch restrictions to limit the workload of volunteers are shown to have important implications for the optimal training strategy.
To quantify factors associated with paramedic transport to hospital for older people in residential aged care facilities (RACFs) and supported accommodation, and to identify modifiable drivers of non-transport. Retrospective cohort study using routinely collected electronic patient care records, analysed with gradient boosting models and multivariable logistic regression. Ambulance Tasmania attendances to RACFs and supported accommodation across Tasmania, 1 January 2018 to 31 December 2024. All eligible ambulance attendances for people aged 65 years or older at these facilities. The primary outcome was transport to hospital. Scene time and clinical status at first assessment, summarised using the National Early Warning Score 2 (NEWS2) and Shock Index, were descriptive variables and candidate predictors. Of 23,317 attendances, 19,386 (83.1%) resulted in transport and 3931 (16.9%) did not. Most attendances were low risk. Crew skill set, calendar month, initial pain score, respiratory rate and NEWS2 category were the strongest predictors of transport. In adjusted logistic regression, extended care paramedic attendance was associated with markedly lower odds of transport than attendance by standard paramedic crews (adjusted odds ratio, 0.09 [95% CI, 0.07-0.12]), corresponding to an adjusted transport probability of 0.50 compared with 0.85 for intensive care paramedic crews, 0.86 for standard paramedic crews and 0.69 for other crews. Paramedic transport decisions for RACF residents were strongly associated with acute illness severity, but crew skill set was also independently associated with transport. Attendances managed by extended care paramedics had lower adjusted probabilities of hospital transport. These findings suggest that extended-scope paramedic models warrant prospective evaluation in this setting. The Known: Older people in residential aged care are frequently transferred to hospital after ambulance attendance, but some transfers may be avoidable and predictors of transport are poorly understood. The New: In 23,317 Tasmanian ambulance attendances, 83.1% resulted in transport. Most attendances had low illness severity; of which 80% were transported; transport increased with illness severity. Crew skill set was also important; extended care paramedic attendance was associated with lower adjusted transport probability. The Implications: Extended‐scope paramedic models may support care in place for selected residents, but need prospective evaluation with resident outcomes, case selection and follow‐up pathways.
Remote sensing equipment (RSE) plays an essential role in monitoring vehicle emissions but requires comprehensive evaluation to verify reliability. In this study, the performance of seven different RSE models in Beijing was assessed by comparing their measurements with those of portable emission measurement systems (PEMS) and steady-state condition testing (SCT). Large variations were observed among the RSE models for CO (16 %-363 %), NO (2 %-354 %), and HC (10 %-725 %) emissions. Comparisons between RSE and PEMS revealed even more significant discrepancies for CO (20 %-6773 %), NO (15 %-2818 %), and HC (13 %-5477 %), with maximum deviations occurring at vehicle speeds between 20 and 60 km/h, reflecting the impacts of vehicle operation on emission measurements. The correlation between RSE and SCT was satisfactory for CO and NO (R² values of 0.64-0.76) but poor for HC (R2 = 0.16-0.22), primarily due to differences in measurement principles. Strengthening management, enhancing certification and accreditation, and regular consistency checks of RSE with SCT and PEMS are recommended for improved reliability in law enforcement applications.
Tow truck drivers (TTD) are often the first to present at vehicle crashes, in addition to police, ambulance and at times fire services. Existing research suggests that TTD do not always receive similar supports as other first responders. A gap highlighted the need to understand the physical and psychological experiences of TTD, how they manage challenges and what supports they receive. The aim of the study is to understand why TTD should be considered first responders and to explore experiences related to the challenges encountered in their work. A scoping review was conducted to address the aims of the study using the PRISMA for scoping reviews guidelines to ensure accurate and rigorous reporting of findings. A broad literature search was conducted between February and April 2024, using the databases PubMed, Scopus, CINAHL and EBSCO (Newspaper Source Plus), while also searching Google Scholar search engine to identify sources related to TTD. Studies of TTD were included if they were original research, written in English, peer-reviewed or news reports. Studies were excluded if truck drivers were not related to the tow truck industry. A total of 14 studies were included in the review. No specific key attributes for TTD were reported; however, they were found to be the first on the scene following an accident due to their well-developed network. TTD experience physical and psychological trauma. Support services are limited, and poor coping strategies, such as excessive alcohol intake, are reported. Lack of safety training was considered a contributor of occupational hazards in the industry. There is a palpable inequity of supports available to this group of first responders when compared to other common disciplines. The findings support a deeper exploration of TTD's lived experiences on the job, including how best to address their physical and psychological well-being.