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Night deliveries are increasingly used by motor carriers to cut costs, but many carriers struggle to secure night truck drivers because many drivers dislike night driving. Monetary incentives are offered to drivers to accept night loads, but the incentive system currently used in practice is inefficient, as it not only pays more incentives than necessary but also does not incentivize the right drivers, both of which increase costs. This paper develops a new incentive system that allows carriers to secure night drivers at lower costs. Based on the interviews conducted with seven motor carriers, as well as the analysis of large night delivery data, we propose a bi-level programming approach that (1) generates a unique incentive for each driver based on his/her night driving performance, and (2) implicitly aligns the incentive paid to each driver with his/her reservation price. Simulation experiments conducted with real-world data showed that by using our approach carriers can not only avoid over-incentivizing drivers, but also (1) incentive only the right set of drivers who can bring cost savings and (2) encourage other drivers to improve night driving. Results also showed that our approach may give cost savings of up to 2.5% over the current system. [ABSTRACT FROM AUTHOR]

As urban areas grow and city populations expand, traffic congestion has become a significant problem, particularly in regions with substantial truck traffic. This study presents a coordinated freight routing system designed to optimize network utility and reduce congestion through personalized routing guidance and incentive mechanisms. The system customizes incentives and payments for individual drivers based on current traffic conditions and their specific routing preferences. Using a mixed logit model with a linear utility specification, the system captures drivers' route choice behaviors and decisions accurately. Participation is voluntary, ensuring most drivers receive a combined expected utility, including incentives, exceeding their anticipated utility under User Equilibrium (UE). This structure encourages drivers to follow suggested routes. Data collection on drivers' routing choices allows the system to update utility parameter estimates using a hierarchical Bayes estimator, ensuring routing suggestions remain relevant and effective. The system operates over defined intervals, where truck drivers submit their intended Origin-Destination (OD) pairs to a central coordinator. The coordinator assigns routes and payments, optimizing overall system costs and offering tailored incentives to maximize compliance. Experimental results on the Sioux Falls network validate the system's effectiveness, showing significant improvements in the objective function. This study highlights the potential of a coordinated routing system to enhance urban traffic efficiency by dynamically adjusting incentives based on drivers’ choice data and driver behavior. View the NCST Project Webpage

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This article explores the formation of migrant agency by scrutinizing the decision-making processes of owner-operator truckers. Drawing on qualitative data collected among male migrants from Turkey in the US, the main finding is that migrant truckers, by making various decisions at the turning points of their career, choose one of three trucking segments and decide the number of trucks that they own. To understand the differentiated ways agency is formed, the article utilizes forms-of-capital analysis, which reveals how truckers mobilize combinations of social, economic and cultural forms of capital. The study conceptually contributes to the scholarship on migrant entrepreneurship by developing the concept of ongoing agency that combines the temporal aspects of migrant agency with the forms-of-capital analysis. This concept is used to understand how migrant truckers reconfigure their agency as they move between segments and ownership statutes over time. [ABSTRACT FROM AUTHOR]

Objective: This study aimed to evaluate the quality of completion among both drivers and medical examiners in filling out Commercial Driver's (CD) Medical Examination Report Forms. Methods: This was a cross‐sectional retrospective study of abstracted data from the year 2019. CD Medical Examination Report Forms, collected from a single nationally‐based employer and initially reviewed by corporate medical directors, were evaluated by the study team for completeness of documentation provided by both drivers and medical examiners (MEs). Relevant findings included unanswered questions, inconsistency between responses, and lack of necessary elaboration for positive responses. Results: Among 1603 examinations, MEs completed the Medical Examination Report Form incompletely or incorrectly in 30% of examinations (n = 484). Drivers inconsistently filled out their health history with elaborations 38.7% of the time. Most commonly, they failed to elaborate on positive health history responses in 28.7% of examinations, but other types of errors were noted as well. Conclusions: A considerable proportion of drivers or examiners (n = 890, 55%) failed to adequately or correctly complete CD Medical Examination Report forms. [ABSTRACT FROM AUTHOR]

Enhancing hazmat truck safety through advanced driving assistance systems (ADAS) relies on both system efficacy and driver reactions. This study investigates the driving behaviors of hazmat truck drivers in response to forward collision warnings (FCWs). Traditional warning triggering methods struggle to capture diverse and immediate driver responses; therefore, our research employs a vision‐based framework for driving data extraction and utilizes the K‐means++ clustering method for response‐based classification. Moreover, we propose an enhanced version of the intelligent driver model (IDM) based on the concept of a virtual vehicle to reproduce hazmat truck drivers' differential behaviors during risky car‐following periods, achieving results that depict improved driving simulations. This model is compared with classic benchmarks, including the IDM, optimal velocity model (OVM), and full velocity difference (FVD) model, demonstrating superior performance in terms of traffic stability and safety in extreme scenarios. Our findings highlight that preaction drivers tend to accelerate before receiving warnings, opting to overtake rather than maintain safe distances. In contrast, calm drivers decelerate in anticipation of the warning, showcasing their awareness of maintaining safety. The analysis reveals that aggressive drivers are predominantly in the 41–45 age group, indicating a higher skill level, while calm drivers are more commonly older, reflecting a trend in cautious driving behaviors. Overall, our research contributes to the development of effective ADAS by considering real‐time driver responses and emphasizes the potential of our model to revolutionize commercial ADAS adoption and enhance road safety for hazmat operations. [ABSTRACT FROM AUTHOR]

Large numbers of vulnerable road users were killed in truck crashes. In this study, ensemble machine learning models are constructed to predict the injury severity of the vulnerable road user (VRU) to truck (VRU-T) crashes. The study is based on the five years (2017–2021) of VRU-T crash data in the Shandong Province from the Center for Accident Research in Zibo. The injury severity of VRUs is estimated using machine learning ensemble models- Stacking, Voting, Random Forest, and eXtreme Gradient Boosting (XGBoost). Compared to the other three models, the Stacking has excellent predictive performance on the pedestrian and non-motorized datasets. Then, SHapley Additive exPlanations and Partial Dependence Plot box are introduced to analyze risk factors qualitatively and quantitatively. The innovative findings of this study are as follows: (1) as VRUs age, they are more likely to be seriously injured in truck crashes; (2) middle-aged truck drivers and truck drivers with medium driving experience increase the probability of VRUs being severe and fatally injured in truck crashes; (3) crashes involving heavy trucks, under signalized crossing, or on the national and provincial road and urban road have a positive effect on the crash severity for cyclists, and E-Bike riders. [ABSTRACT FROM AUTHOR]

BACKGROUND: Professional driving requires long hours of work, uncomfortable seats, negotiating rough terrain and highways, and possibly minor repairs and other auxiliary transportation duties. Heavy vehicle drivers driving vehicles such as trucks, bulldozers, etc. due to such working structures are more prone to various musculoskeletal disorders (MSDs) and pain, which is of great concern. OBJECTIVES: In the present study, it is planned to investigate possible ergonomic risk factors such as age, weight, driving exposure, seat suspension systems, lifting heavy weights causing MSDs in drivers of various heavy vehicles. The results of the study are expected to help drivers reduce the risk of MSDs. METHODS: For the present study, the Nordic questionnaire on musculoskeletal disorders was modified and standardized and was administered to the 48 heavy vehicle drivers randomly selected to collect the data. RESULTS: The analysis divulged that over the past 12 months, lower back pain (LBP) emerged as the most dominant pain experienced by 56% of drivers, followed by knee pain (KP) (43%) and neck pain (NP) (39%) respectively. The prevalence of shoulder pain (SP) was observed to be much lower than in previous literature. The logistic regression model further revealed that increasing age, poor suspension system and poor body posture were significantly associated with lower back pain. Additionally, a poor suspension system and lifting heavy weights had significant effect on the drivers' knee pain. CONCLUSION: The results demonstrated the evident necessity for ergonomic consideration in vehicle designing and ergonomic training for heavy vehicle drivers. [ABSTRACT FROM AUTHOR]

BACKGROUND: The effects of traffic sign information volume (TSIV) on the visual characteristics and workload of novice and experienced drivers were investigated in this study. TSIV plays a crucial role in road traffic safety, and understanding its impact on drivers is essential for designing effective traffic sign systems. OBJECTIVE: This research aimed to compare the visual characteristics and workload of novice and experienced drivers under varying TSIV doses through simulated driving tests. The objective was to provide insights for optimizing the design of road TSIV. METHODS: Six TSIV levels were considered: S0, S1, S2, S3, S4, and S5, representing different workload levels. Participants, including both novice and experienced drivers, were involved in simulated driving scenarios with varying TSIV levels. Eye movement data was collected using an eye tracker device. The study was conducted in China, and appropriate driving simulators and equipment were utilized. RESULTS: The findings revealed several valuable results. Experienced drivers exhibited a higher proportion of saccade behavior in the 30–90 ms time period and did not show rapid saccade behavior during the 0–30 ms period, indicating superior visual search strategies. Both novice and experienced drivers demonstrated improved visual cognitive abilities at the S3 level of TSIV, which corresponds to normal and safe driving conditions. Furthermore, a majority of both groups had saccade amplitudes in the range of 0°–4°, with experienced drivers showing a slightly higher proportion. About 82% of experienced drivers had saccade behavior within the range of 0°–2°, compared to 75% of novice drivers. CONCLUSIONS: The study concludes that the S3 level of TSIV, corresponding to 30 bits/km, is optimal for both novice and experienced drivers. This level promotes better visual performance and reduces visual workload, indicating that drivers' information acquisition capacity and visual search strategies are maximized while keeping the workload associated with driving at a minimum. These findings have significant implications for enhancing driving safety. [ABSTRACT FROM AUTHOR]

With the development of the Shanghai International Shipping Center, the diversity of vehicle types on the highways and arterial roads near Yangshan port is continually increasing. Within such a container port corridor, large container trucks are primarily utilized for mainline transportation. Their larger size and significant inertia would increase psychological pressure on sedan drivers, and elevate their behavior risk. To investigate the effects of container trucks on drivers' visual characteristics and driving behavior as well as to predict driving risk, firstly, this research conducted field tests in four scenarios surrounding the port. Visual characteristics and behavior data of sedan drivers were collected. Secondly, a "Visual‐behavior" chain model was established. The relationship between drivers' visual characteristics, driving behavior, and driving risk was illustrated from the perspective of time‐series behavior patterns. Thirdly, three driving risk prediction models were built with Autoregressive Integrated Moving Average (ARIMA), Long Short‐Term Memory (LSTM), and ARIMA‐LSTM. The results indicate that the ARIMA‐LSTM model shows the most effective prediction performance. This research provides a field‐data comparative analysis of the driving risks influenced by a high proportion of container trucks. The findings contribute to understanding the unique mixed traffic visual environment around large‐scale container ports. [ABSTRACT FROM AUTHOR]

Abstract Background The job of truck driving exposes the drivers to various risk factors of musculoskeletal disorders (MSDs) due to unfavorable working conditions. Hence, this research was conducted to investigate the frequency of MSDs among individuals working as truck drivers. Materials and methods The present research followed the preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines, and its protocol was registered in international prospective register of systematic review (PROSPERO) under the code CRD42024507985. Databases such as PubMed, Scopus, Web of Science, Science Direct, SID, ISC, and Google Scholar were searched without time restrictions until February 7th, 2024 to identify relevant articles. Meta-analysis was conducted using a random effects model, and heterogeneity between studies was evaluated using the I2 index. Additionally, STATA (version 14) was utilized for conducting data analysis. Results In the initial search, 678 articles were identified. After removing duplicates and articles unrelated to the research objective and inclusion criteria, 15 articles were included in the meta-analysis, involving 2,662 truck drivers. The findings from the research show that 61.75% of truck drivers have musculoskeletal disorders (MSDs) (95% CI: 46.98–76.52, I2 = 96%, P

Karen Levy's book, "Data Driven: Truckers, Technology, and the New Workplace Surveillance," provides a detailed examination of the occupational culture of long-haul truck drivers in the US. The book explores how truckers navigate and negotiate with technology that is designed to displace, control, and surveil them. Levy challenges the idea that automation should aim to replace workers, instead emphasizing the importance of integrating new technology into sociotechnical environments. The book specifically focuses on the introduction of electronic log devices (ELDs) in trucks, which were intended to reduce accidents caused by driver fatigue but have not been successful in doing so. Levy argues that the failure of ELDs can be attributed to the absence of efforts to address the underlying conditions that lead to overwork among truck drivers. The book also highlights the symbolic meaning and value-laden nature of truck driving as a profession, drawing on ethnographic research and cultural representations of trucking. Overall, "Data Driven" sheds light on the complex relationship between technology, work, and worker agency, and raises important questions about the role of rules and discretion in the workplace. [Extracted from the article]

Background: Truck drivers are a vital workforce, but have higher rates of obesity and other chronic diseases than the general population. The occupation's sedentary nature, limited physical activity opportunities and access to healthy food, and irregular sleeping patterns contribute to poor health. This systematic review and meta-analysis aimed to evaluate the effectiveness of interventions on health behaviours and cardiometabolic biomarkers of health in truck drivers. Methods: A systematic search was conducted in February 2024, and reported according to PRISMA 2020 guidelines. Experimental studies targeting physical activity, sedentary behaviour, sleep, diet, weight loss, drug/alcohol use, and/or smoking were eligible. Two reviewers independently screened and completed data extraction and risk of bias assessment. Data were combined at the study level. Pooled statistics were calculated using mean differences (MD) or standardised mean differences (SMD) for outcomes that were reported in ≥2 studies. Pre- and post-intervention means and standard deviations (SD) for the intervention and control groups were used to compute effect sizes. Results: Nineteen studies (n=2137 participants) were included. Meta-analyses found a small-to-moderate increase in fruit and vegetable consumption (SMD 0.32, p=0.03) with no other significant effects on other outcome variables. Conclusions: Interventions are moderately effective in increasing truck drivers' fruit and vegetable consumption, but not other outcomes. There is a dearth of research in the driver population compared to other occupational groups. Future interventions should consider workplace and environmental factors to promote the health and wellbeing of truck drivers. Trial registration: The study protocol was registered on PROSPERO (CRD42021283423). [ABSTRACT FROM AUTHOR]

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The transportation of goods by road is crucial in Tanzania's Central and North-South Corridors. However, challenges such as truck congestion and road accidents are affecting the efficiency of these routes. Road crashes are prevalent in low- and middle-income countries, with Africa experiencing an exceptionally high rate. This study examines the opinions of Tanzanian truck drivers on the effectiveness of Advanced Driver Assistance Systems in reducing road safety risks. A discriminant analysis was conducted to assess the awareness and use of these systems among different driver experienced groups. The results highlight the importance of improving infrastructure, ensuring vehicle safety standards, providing comprehensive driver training, and integrating innovative Intelligent Transport Systems to address road safety issues. In conclusion, the study provides valuable insights for policymakers and stakeholders to strengthen road safety measures in Tanzania, facilitating smoother road freight transport operations and promoting economic growth. [ABSTRACT FROM AUTHOR]

As automotive technology advances in the realm of digitization, vehicles are becoming smarter and, at the same time, more vulnerable to various threats. This paper focuses on techniques for detecting faults to mitigate the risk of freight transportation. Our observations show that vehicle uptime varies significantly even under similar operating conditions. This variation stems from differences in the wear and tear of moving and stationary parts, the characteristics of transported loads, driving styles, the quality of maintenance, etc. These factors are particularly crucial for abnormal vehicles designed to carry AILs (Abnormal Indivisible Loads). Such vehicles are especially prone to surprising threats, requiring efficient techniques for monitoring separate vehicle components and providing drivers with vital information about their operational status. The presented article proposes an original concept of an integrated three-level monitoring system based on the AOP (All-in-One Platform) principle, using the DBSCAN (Density-Based Spatial Clustering of Applications with Noise) algorithm, which is a tool oriented to distinguish points from three categories: basic, boundary, and external. This is a solution not yet found in the literature. It is based on assessments of LOFs (Local Outlier Factors) and to detect anomalies in the measured values of operational parameters. The purpose of our study was to determine whether providing truck drivers with current information from an active threat warning system could help reduce unplanned downtimes. [ABSTRACT FROM AUTHOR]

This systematic review collected evidence on the burden of non-communicable diseases (NCDs) among professional drivers and reported on the most common factors that increase the risk of NCDs in this specific population in low- and middle-income countries (LMICs). The protocol for this systematic review was registered in the International Prospective Register of Systematic Reviews (PROSPERO). We conducted a thorough search on PubMed/MEDLINE, EMBASE, Scopus, Global Health, Web of Science and Africa-wide information databases on 11 May 2023. We adapted the Joanna Briggs Institute (JBI) tool to assess the quality of the studies. We estimated the prevalence of hypertension, prediabetes, diabetes mellitus (DM), overweight and obesity among professional drivers using a random effect model to compute pooled and subgroup analyses. In addition, we conducted a narrative synthesis of the risk factors and recommendations presented in the included studies. Forty-one studies, including 48 414 study participants, met the criteria for inclusion. The pooled prevalence of hypertension, DM and obesity among professional drivers was 36.7% [95% confidence interval (CI): 31.8–41.6%], 15.2% (95% CI: 7.0–23.4%) and 27.2% (95% CI: 18.7–35.8%), respectively. Unsupportive environment, work stress, sedentary lifestyle, consumption of unhealthy foods and shift work were the most common modifiable risk factors reported. Our findings also show a significant burden of hypertension, DM and obesity among professional drivers in LMICs. The prevalence of DM and obesity was two- and three-fold higher than findings in general populations, respectively. Our findings indicate an urgent need for tailored interventions for different occupation-related risk factors for NCDs among professional drivers in LMICs. [ABSTRACT FROM AUTHOR]

The World Bank's International Finance Corporation has produced three research reports on gender equality and ride-hailing, in collaboration with ride-hailing companies. This article examines these reports in light of wider conversations about the growing corporate co-production of development knowledge. Focusing on research claims about the gender and development potential of 'sharing economy' firms like Uber, it argues that the shortage of women drivers has been successfully framed as a gender and development problem, rather than a labour supply problem to be resolved by ride-hailing companies themselves. Multi-stakeholder solutions are proposed, involving licensing actors, charities, government development agencies, banks, and insurance providers. Gender equality research contributes to a new development common sense involving expanding the numbers of indebted 'independent contractors' in the gig economy; reducing 'regulatory burdens' on platforms; and binding nongovernmental organizations (NGOs) ever tighter to corporate interests. This article shows the centrality of gender research to the Bank's broader reorientation towards business-led development, and highlights some distinctive lessons of work on ride-hailing. [ABSTRACT FROM AUTHOR]

Background: Long-haul truck drivers (LHTDs) face a number of occupational hazards. One such hazard is exposure to diesel engine exhaust (DEE). However, this concept has yet to be analyzed. To address this gap, a concept analysis was conducted to explore the effects of DEE in relation to lung cancer. Methods: Walker and Avant's eight-step concept analysis method was utilized: concept selection, analysis purpose, concept uses, defining attributes, model case, borderline case, antecedents and consequences, and empirical referents. PubMed, Scopus, and CINAHL databases were searched for relevant literature. Findings: Diesel engine exhaust was identified as a mixture of gases and particulates that are considered carcinogenic. Defining attributes of DEE for truckers include respiratory effects such as decreased peak flow and increased airway resistance leading to symptoms such as a phlegm-producing cough, eye and throat irritation, exacerbation of asthma symptoms, and allergic responses. The identified level of DEE exposure associated with these attributes is 75 μg EC/m3 for 1 to 2 hours daily or a long-term exposure of 10 μg EC/m3. The conceptual definition of DEE in truckers was illustrated by the attributes, antecedents, consequences, model case, and empirical referents. Conclusion: Lung cancer was identified as a significant consequence of occupational DEE exposure for LHTDs. This analysis highlights the need for future research to develop interventions that will safeguard truckers from the adverse health effects of DEE exposure. [ABSTRACT FROM AUTHOR]

This article examines how employees use gossip as a resource to cope with social isolation. Building on a qualitative study with 32 truck drivers in a Western European company, our research identified gossip in close relationships and gossip in distant relationships as distinct patterns playing a different role in coping with social isolation, and a third pattern in which gossip was not beneficial. First, gossiping with close friends at work helped drivers engage in emotion-focused coping by reducing stress and loneliness. Second, gossiping with distant colleagues helped drivers engage in problem-focused coping by exchanging knowledge involving people in the organization. Third, gossip avoidance occurred in distant relationships, where drivers limited gossip exchanges going beyond instrumentally useful information. Overall, these findings show that drivers relied on different layers of their social network to cope with social isolation. Enriching previous research, this study shows that gossip represents an essential resource for emotion-focused and problem-focused coping. [ABSTRACT FROM AUTHOR]

Accurate fatigue driving recognition is vital for road safety, preventing accidents caused by driver exhaustion, and protecting lives. To address the challenges of inadequate accuracy in driver feature extraction, limited precision in fatigue recognition, and prolonged processing time in conventional truck driver fatigue detection systems, the paper propose a novel fatigue driving recognition of truck drivers based on multi-feature fusion. Firstly, this scheme utilizes specialized acquisition equipment to accurately capture images of truck drivers, and corrects lighting inconsistencies through two-dimensional gamma functions; Then, the image is denoised using a dual neighborhood median filtering algorithm; Subsequently, the features extracted from the driver's eyes, mouth, and head were effectively integrated into an extreme learning machine framework to achieve accurate driver fatigue classification. Experimental results demonstrate the effectiveness of new approach, achieving a peak feature extraction accuracy of 98.74%, a maximum recognition accuracy of 98.56%, and a time efficiency ranging from 0 to 0.53 seconds. [ABSTRACT FROM AUTHOR]

Highways, the lifeline of the Brazilian economy, transport approximately 75% of the country's economic activity, highlighting its importance. However, professional drivers, accustomed to long daily journeys, make use of tablets widely available in Gas Station, which are known as "Rebites," which could contain a mixture of legal and illegal compounds. Thus, this study aims at the chemical characterization of these through different analytical methods. Initially, we performed a comprehensive screening of compounds present in seven samples collected across the country using high‐resolution mass spectrometry (HRMS). The findings revealed caffeine as the main compound, alongside theophylline, lidocaine, and clobenzorex, among others. In the next step, we employ quantitative nuclear magnetic resonance (qNMR) to quantify the caffeine content in the tablets. The results indicated a caffeine concentration ranging between 14% and 31% (m/m), which may imply a daily overdose of this compound from around four tablets. In summary, this investigation provides a chemical characterization of real samples of "Rebites" freely obtained along Brazilian highways. Caffeine emerged as the predominant active compound, with its concentration determined by qNMR analysis. The notable presence of caffeine, combined with other stimulants, depressants, and hallucinogens, underscores the need for strict quality control measures regarding "Rebites" to safeguard public health. [ABSTRACT FROM AUTHOR]

Aim: To explore the experiences of recreational‐vehicle‐dwelling American nomads when seeking healthcare in the United States. Design: This exploratory, qualitative study employed interpretive description design to maintain a disciplinary focus in nursing. Methods: Twenty‐five participants were recruited from campgrounds in eight states. Semi‐structured interviews were conducted face‐to‐face. Thematic analysis was conducted using a phronetic iterative approach. Results: Three overarching themes described participants' experiences seeking healthcare: overwhelming logistics, don't need healthcare, and orchestrating a web of care. Conclusion: Recreational‐vehicle‐dwelling nomads in the United States face additional complexity when accessing healthcare related to seeking care in unfamiliar locations and policies that prevent the portability of healthcare across state lines. Implications: More research is needed to evaluate solutions to improve healthcare access for recreational‐vehicle‐dwelling nomads. Nurses and policymakers should consider healthcare practices through the lens of nomads' lifestyle mobility. The impact of policy decisions on people who are geographically mobile should be considered, and barriers to healthcare portability should be addressed. Impact: This was the first study focused on the healthcare experiences of recreational‐vehicle‐dwelling nomads in the United States.Nomads face additional complexity and barriers to healthcare access. Some nomads developed strategies to overcome barriers, while others disengaged from healthcare.The findings will impact researchers and nurses who study and care for nomads. Reporting Method: The SRQR reporting guidelines were followed. Patient or Public Contribution: Recreational‐vehicle‐dwelling nomads reviewed the recruitment flyer and interview guide and provided input on recruitment methods and sites. [ABSTRACT FROM AUTHOR]

Objectives To analyze the association between occupational ergonomic risk, personal characteristics, and working conditions with the biomechanical properties of stiffness and muscular tone in the paravertebral muscles of electric pallet jack and forklift operators in the industrial sector. Methods A total of 75 industrial sector machine operators were evaluated in 2021. Personal characteristics and working conditions were assessed through a questionnaire. Ergonomic risk was assessed using the Rapid Entire Body Assessment (REBA) method, and biomechanical properties of stiffness and muscular tone were obtained using the Myoton Pro device. Stiffness in paravertebral muscles was compared based on the operated machine and observed ergonomic risk. A multilevel linear regression model was employed to quantify the relationship, with mean differences and 95% CI calculated. Results Very high ergonomic risk was found in 75% of the electric pallet truck drivers. In this group with the highest ergonomic risk, an association between biomechanical properties and older workers was observed. Additionally, among electric pallet truck drivers, stiffness (mean difference 335.9 N/m, 95% CI: 46.4 (3.4 to 110.0), P < 0.05) and paravertebral muscle tone (mean difference 17.5 Hz, 95% CI: 1.4 (0.1 to 3.4), P < 0.05) showed statistically significant differences in the very high ergonomic risk category compared to the high-risk category. No significant differences were observed in any of the analyzed variables among forklift drivers. Conclusions Workers operating electric pallet trucks with very high ergonomic risk according to the REBA method and aged over 40 yr are associated with increased muscle stiffness and tone. [ABSTRACT FROM AUTHOR]

The aim of this study is to identify the psycho-social determinants of truck overloading from a behavioral perspective. Although there has been sizable efforts to investigate overloading from different perspectives, factors influencing truck drivers' decisions to overload have not been revealed. In this study, sampling 552 truck drivers, we used the theory of planned behavior (TPB), extended with risk perception (RP), facilitating circumstance (FC) and law enforcement knowledge (LEK), as a lens to examine overloading. A survey questionnaire was designed to collect data, including information regarding drivers' demographics and TPB variables. The results showed strong support for the application of the TPB in explaining overloading behavior. Specifically, behavioral intentions (BI) had the greatest direct positive effect on overloading behavior, followed by LEK, while perceived behavioral control had no direct positive effect. Interestingly, subjective norms had a significant positive effect on BI, while attitudes had a positive indirect effect, indicating that drivers' negative attitudes toward overloading could not alone reduce this risky violation. Furthermore, all extended TPB variables were significantly and positively associated with overloading intentions, with FC having the largest positive effect. Finally, these findings were discussed in view of determining how to make truck drivers less inclined to overload. [ABSTRACT FROM AUTHOR]

• Explores the representations of truck platooning acceptance on different stakeholders. • Exemplifies how similarly themed representations capture differences in perspective. • Suggests that on-road experiences may potentiate more positive representations. • Points towards the research gap on the impact of platooning in the future of work. Truck platooning, involving two or more automated trucks virtually linked in a convoy through vehicle automation and communication technologies, has become a core topic in the long-haul freight transport industry. Despite its potential benefits – fuel efficiency, reduced carbon emissions, operation cost savings, improved road safety, and alleviated traffic congestion – further research is required to understand the representations of technology acceptance that will mediate its adoption across different stakeholders. This study presents a systematic review of the representations of decision-makers, truck drivers, and the general public on truck platooning acceptance. A total of 35 papers were included in the review and grouped into (i) studies with no platooning experience, (ii) studies with a simulated platooning experience, and (iii) studies with an on-road platooning experience. Representations were extracted using thematic analysis to synthesize the perspectives of each stakeholder and organized in themes. Even when similar themes emerged, representations highlight each stakeholder singular perspective. Although decision-makers have a more positive outlook on the potentialities of the technology, they are concerned about several obstacles related to its implementation and risks that may undermine the promised efficiency benefits of truck platooning. Regarding general public, peripheral drivers are mainly concerned about the reliability and safety of truck platooning and the potential traffic conflicts. Truck drivers denote the potential advantages in driving comfort and road safety, but highlight their concerns about employment, the reliability of the automation, loss of driving pleasure, trust in the platooning systems and their elements, and additional stress associated with the reconfiguration of their activity by the technology. Meanwhile, considering the role of experience, when the technology was experimented on-road, representations became more positive. Still, although these experiments are closer to real-world context, they only focused on basic driver-truck interactions and did not account for multitask driving scenarios, nor explored truck drivers' employment concerns. [ABSTRACT FROM AUTHOR]

The extant transport literature suggests that truck driver fatigue is a leading contributor in truck crashes. Various factors are known to affect a truck driver's ability to comply with work and rest requirements, including long working hours, irregular work schedules, sleep deprivation and driving at night. In Australia, while control measures restrict the number of hours of work a truck driver may undertake without rest, they travel vast distances and work with a high level of autonomy. This makes it difficult for transport companies to monitor and manage truck drivers' work and rest compliance; thereby relying on them to self-manage driver fatigue at designated rest-stops. However, there is little Australian research examining rest-stop behaviour of truck drivers in relation to driver fatigue compliance. To better understand how truck drivers manage driver fatigue, an exploratory participant observation study of truck drivers (N = 378) was undertaken at two Australian truck rest stops in rural New South Wales. The findings indicate that while most truck drivers comply with rest requirements regarding driver fatigue, load type, driver changeovers, access to parking, and time spent on work-related activities at the rest areas affects the time truck drivers spend at rest, and these factors are likely to increase the risk of truck drivers not complying with driver fatigue regulation. They are also likely to increase the number of truck drivers involved in fatigue related crashes. [ABSTRACT FROM AUTHOR]

Freight transportation is a crucial part of the global economy, but it encounters several complex challenges, with truck drivers at the centre of these issues. These professionals, responsible for transporting goods over long distances, often work in challenging conditions, exposing them to a range of risks, including physical, psychological, and chemical hazards. These risks make the profession less appealing to younger drivers, leading to an ageing workforce and worsening the driver shortage crisis in the road transport sector. This article aims to identify the various risks faced by truck drivers and examine their negative impacts on several critical aspects, including company image, service quality, financial implications, and road safety. Additionally, the article explores the transformative impact of the Internet of Things (IoT) and autonomous vehicles (AV) on the truck driving profession. [ABSTRACT FROM AUTHOR]

This report is the first in-depth look at the labor market for agricultural truck drivers in California and the first study of this workforce anywhere in the U.S. in almost 30 years. It found that better efforts in recruiting and training drivers would ease turnover and improve job satisfaction, particularly for agricultural trucking, which is critical to California’s economy but can often be seasonal or require specialized equipment.

Traffic crashes represent a global challenge, especially in Brazil, where one-third of incidents on federal highways involve trucks, highlighting significant economic and safety risks for truck drivers and the community at large. This study focuses on understanding the specific causes of crashes involving trucks on Brazilian highways, using a decade of data from the Federal Highway Police to develop a predictive model aimed at accident prevention. It analyzes historical crash trends, selects attributes for prediction models, trains classifiers, evaluates predictions through confusion matrices, and enhances reliability via cross-validation techniques, aiming to develop an accident prevention tool. The analysis revealed a temporal pattern, with a slowdown in fatal incidents from 2013 to 2016, followed by an upward trend from 2017. MG-381 emerged as the deadliest highway, and single-lane roads were identified as more accident-prone, emphasizing the need for targeted preventive measures. Additionally, machine learning models achieved an accuracy of over 70 %, with XGBoost and LightGBM leading at 73 %, providing reliable insights for road safety interventions. In transportation engineering and road safety research, these findings highlight the importance of data-driven approaches to understand accident dynamics and design effective interventions to mitigate risks on highways, thereby contributing to increased road safety and social well-being.

Excess health and safety risks of commercial drivers are largely determined by, embedded in, or operate as complex, dynamic, and randomly determined systems with interacting parts. Yet, prevailing epidemiology is entrenched in narrow, deterministic, and static exposure‐response frameworks along with ensuing inadequate data and limiting methods, thereby perpetuating an incomplete understanding of commercial drivers' health and safety risks. This paper is grounded in our ongoing research that conceptualizes health and safety challenges of working people as multilayered "wholes" of interacting work and nonwork factors, exemplified by complex‐systems epistemologies. Building upon and expanding these assumptions, herein we: (a) discuss how insights from integrative exposome and network‐science‐based frameworks can enhance our understanding of commercial drivers' chronic disease and injury burden; (b) introduce the "working life exposome of commercial driving" (WLE‐CD)—an array of multifactorial and interdependent work and nonwork exposures and associated biological responses that concurrently or sequentially impact commercial drivers' health and safety during and beyond their work tenure; (c) conceptualize commercial drivers' health and safety risks as multilayered networks centered on the WLE‐CD and network relational patterns and topological properties—that is, arrangement, connections, and relationships among network components—that largely govern risk dynamics; and (d) elucidate how integrative exposome and network‐science‐based innovations can contribute to a more comprehensive understanding of commercial drivers' chronic disease and injury risk dynamics. Development, validation, and proliferation of this emerging discourse can move commercial driving epidemiology to the frontier of science with implications for policy, action, other working populations, and population health at large. [ABSTRACT FROM AUTHOR]

Objectives. In developing physical activity (PA) promotion measures in the workplace, individual needs of the target groups must be considered. For this reason, this study aimed to qualitatively identify the individual needs for an increase of PA in truck drivers, representing a neglected working group in terms of PA promotion. Methods. Semi-structured interviews were conducted (N = 19; mean age 50.5 years, SD 11.4 years; 10.5% female). The interview guide was based on the COM-B model to identify needs related to PA behavior in terms of capabilities, opportunities and motivation. The interviews were transcribed and coded by two independent investigators. Results. Altogether, the codes encompassed 395 statements, and the code system contained 14 codes. Three additional codes described further individual needs related to other health behaviors (nutrition, recreation) or external incentives for health interventions. Conclusion. The results reveal a variety of needs that are fundamental to the development of PA-promoting interventions for truck drivers. These needs appear to be closely linked, so a combination of capabilities, opportunities and/or motivation should be considered when developing holistic interventions. An example would be linking automatic motivation and psychological capabilities that encompass motivational techniques combined with information for psychoeducation of truck drivers. [ABSTRACT FROM AUTHOR]

Long-distance heavy vehicle drivers are marginalized population. Literature search revealed lack of data on this group's awareness, attitude and knowledge about oral cancer. This survey assessed the same among the mentioned who stop in and around Pondicherry. This is a part of the larger research project, funded by ICMR. 2340 heavy vehicle operators responded to a pre-validated questionnaire. The responses were expressed as frequency of means and total knowledge scores were expressed as mean and standard deviations. 67.7% of the participants knew what is oral pre-cancer and cancer. A small 8.9% responded that all smoking, smokeless tobacco, and alcohol can be a cause of oral cancer whilst the majority chose smokeless tobacco as the cause. Only 16% thought that oral cancer is preventable. Responding to questions that could reflect their attitude towards oral cancer awareness, 94.1% were willing to know more about oral cancer and 59% did not want to know about oral cancer. The mean knowledge score of the group is 6.35 ± 4.21. On correlating the age to total knowledge score, a positive correlation was observed (0.65; p = 0.002), but education status and total scores showed a negative correlation (-0.202; p=0.000). Awareness about oral cancer is inadequate among longdistance truck drivers in India. The majority of the population is not aware of the risks, signs, or treatment options of oral pre-cancer and cancer. Results indicate that we need to carry out educational campaigns aiming at the prevention and early diagnosis of oral cancer in this population. [ABSTRACT FROM AUTHOR]

This study provides an approach to identify driving style of truck drivers by using GPS, load condition, and in-vehicle monitoring data and investigates the association of driving styles with risky driving behaviors from macro and micro perspectives. The naturalistic driving data used in this study were collected from 4,357 trucks in Hangzhou, China over three months in 2021. Six driving volatility parameters and six warning parameters were used to characterize the driving styles. Then, three driving styles under the two load conditions were identified using k-means clustering methods and principal component analysis. Finally, one-way MANOVA and ANOVA were used to analyze the relationship between driving styles and driving risk. It was found that truck drivers have different thresholds for aggressive and cautious driving style under different load conditions. Truck drivers who exhibited aggressive driving under both load conditions exhibited high driving risk. Although most truck drivers exhibited safe or normal driving under both conditions, the few who exhibited aggressive driving contribute to a disproportionate driving risk. These results can help distinguish differences in truck drivers' driving styles under different load conditions, thus providing a more comprehensive safety assessment of truck drivers' performance for monitoring purposes. [ABSTRACT FROM AUTHOR]

HIV status awareness is critical for ending the HIV epidemic but remains low in high-HIV-risk and hard-to-reach sub-populations. Targeted, efficient interventions are needed to improve HIV test-uptake. We examined the incremental cost-effectiveness of offering the choice of self-administered oral HIV-testing (HIVST-Choice) compared with provider-administered testing only [standard-of-care (SOC)] among long-distance truck drivers. Effectiveness data came from a randomized-controlled trial conducted at two roadside wellness clinics in Kenya (HIVST-Choice arm, n  = 150; SOC arm, n  = 155). Economic cost data came from the literature, reflected a societal perspective and were reported in 2020 international dollars (I$), a hypothetical currency with equivalent purchasing power as the US dollar. Generalized Poisson and linear gamma regression models were used to estimate effectiveness and incremental costs, respectively; incremental effectiveness was reported as the number of long-distance truck drivers needing to receive HIVST-Choice for an additional HIV test-uptake. We calculated the incremental cost-effectiveness ratio (ICER) of HIVST-Choice compared with SOC and estimated 95% confidence intervals (CIs) using non-parametric bootstrapping. Uncertainty was assessed using deterministic sensitivity analysis and the cost-effectiveness acceptability curve. HIV test-uptake was 23% more likely for HIVST-Choice, with six individuals needing to be offered HIVST-Choice for an additional HIV test-uptake. The mean per-patient cost was nearly 4-fold higher in HIVST-Choice (I$39.28) versus SOC (I$10.80), with an ICER of I$174.51, 95% CI [165.72, 194.59] for each additional test-uptake. HIV self-test kit and cell phone service costs were the main drivers of the ICER, although findings were robust even at highest possible costs. The probability of cost-effectiveness approached 1 at a willingness-to-pay of I$200 for each additional HIV test-uptake. HIVST-Choice improves HIV-test-uptake among truck drivers at low willingness-to-pay thresholds, suggesting that HIV self-testing is an efficient use of resources. Policies supporting HIV self-testing in similar high risk, hard-to-reach sub-populations may expedite achievement of international targets. [ABSTRACT FROM AUTHOR]