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• 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]

Drowsy driving increases the risk of motor vehicle crashes in those with untreated obstructive sleep apnea (OSA). Although previous studies indicated that excessive daytime sleepiness (EDS) might not predict OSA, they were not conclusive due to their small study sizes or restricted participants to sleep clinic patients. The overall objective was to determine whether self-reported EDS can be used for case identification of OSA among commercial truck drivers. Commercial truck drivers (N = 19,699) were screened for OSA-related symptoms. EDS was determined using the Epworth Sleepiness Scale (ESS) ≥ 11 and all participants completed the home sleep apnea test using a type 4 portable monitor to derive the respiratory event index (REI). Regression analyses were used to characterize the association between EDS and REI. EDS was associated with OSA severity (p for trend <0.001). The sensitivity and specificity values of EDS for identifying moderate-to-severe OSA (REI ≥15 events/hour) were 0.10 and 0.93, respectively, and 0.48 and 0.71 if BMI ≥25 kg/m2 was added. Those using BMI ≥25 kg/m2 with OSA-related signs yielded the best sensitivity and specificity of 0.77 and 0.50, which were not improved by the addition of EDS. Despite the associations between EDS and OSA severity and between OSA and lethal crash, case-identification of OSA using the ESS in commercial truck drivers is poor. Thus, OSA screening strategy may need a special approach, including a hierarchical combination of screening tools (Swiss Cheese Model approach), and incorporation of home sleep apnea testing. • A nation-wide, large-sized survey was conducted on commercial truck drivers. • Self-reported sleepiness poorly predicts the presence of sleep disordered breathing. • The screening approach by questioning sleepiness is not perfect. • Preventive strategies include a screening by objective assessment of sleep apnea. • Furthermore, a hierarchical approach may be helpful. [ABSTRACT FROM AUTHOR]

• Field test for short-haul truck drivers was conducted to investigate the interaction effect of occupational fatigue factors on driving behavior and eye-motion metrics. • Less than four hours of sleep between 1 a.m. and 5 a.m. had the greatest impact on driver fatigue and correlated with the worst driving performance. • Sleepiness is more uncovered during the inbound task due to the monotony and low workload of the driving process. • Short-haul truck drivers who were sleep deprived exhibited short periods of protect driving behavior by exerting greater effort during the outbound task. • The interaction of time-on-task and sleep pattern exacerbated driver fatigue more than the effects of any single factor alone. • Applications include better fatigue management of short-haul truck drivers. Driver fatigue is the leading cause of truck-related accidents. The most significant occupational fatigue factors among short-haul truck drivers are sleep patterns, the round-trip driving task, and the time-on-task. However, the underlying mechanisms of these influential factors remain unclear. This study aims to explore the interactive effects of sleep patterns, driving task, and time-on-task on driving behavior and eye-motion metrics among short-haul truck drivers. We obtained test data from eleven professional short-haul truck drivers, with each driver participating in a three-day test under the conditions of two driving tasks and three different sleep patterns. We applied three-way repeated-measure ANOVA and non-parametric tests to analyze the data. The results reveal that: (1) violation of sleep-related legal requirements, insufficient sleep, and unreasonable time-on-task can have negative effects on short-haul truck drivers' vigilance and driving performance; (2) both driving task and sleep pattern contribute to driver fatigue, and the interaction of time-on-task and sleep pattern exacerbates driver fatigue more than the effects of any single factor alone; and (3) short-haul truck drivers who are sleep deprived exhibit short periods of controlled compensatory behavior during the outbound task, and sleepiness is more prevalent during the inbound task compared to the outbound task due to the monotony and low workload of the driving process. These findings provide theoretical and practical guidance for transportation industry managers to strengthen company-wide fatigue-related regulations, ensure adequate sleep for drivers via regulations, and optimize work schedules to improve safety outcomes of short-haul truck drivers. [ABSTRACT FROM AUTHOR]

• The electronic logging device (ELD) system allowed research on a new safety system. • Four themes tied into cultural aspects were identified, indicating a safety culture existed. • The data suggested the ELD influenced both positive and negative safety behaviors. • Influence links connecting safety culture, safety climate, and safety behaviors were documented. Introduction: This paper examines the safety relationships between safety culture, safety influences, safety climate, and safety outcomes for long-haul truck drivers. The relationships focus on the intersection of the electronic logging device (ELD) technology, regulations, and truck drivers that fall into the lone-worker category. Results: Through research questions, links between safety culture and safety climate were established with links between the layers. Conclusions: The implementation of the ELD system was associated with safety outcomes. [ABSTRACT FROM AUTHOR]

Urban freight transport is an important issue in sustainable mobility discussions. It constitutes a significant proportion of urban traffic, and expected negative impacts for urban freight transport can be arguments against implementing restrictive measures targeting passenger traffic. The scarcity of empirical studies might lead to over- or underestimation of consequences for urban freight transport. This might slow shifts towards more sustainable mobility or cause unintended negative consequences. A long-planned 14-month capacity reduction in a main road tunnel in Oslo, Norway, causing significantly increased congestion, offered an excellent opportunity to study urban freight transport adaptations, effects and consequences. With truck drivers and logistics professionals as key informants, the study amplifies voices not often heard in research. Truck drivers adapted by avoiding the tunnel during rush hours only to a limited degree, and less than general traffic did. They reported limited flexibility, as routes and trip timing are strongly defined by customer contracts. The wider consequences for drivers were more stress and less predictable workdays. The findings might improve understandings of how truck drivers can and do adapt, and what consequences they experience. This will help authorities and freight companies plan for changes in urban transport systems aimed at sustainable mobility. • Empirical study of how truck drivers adapted to increased delays in main road tunnel. • Analyses of traffic data, surveys to and interviews with truck drivers. • Truck drivers adapted less than other traffic by avoiding the tunnel. • Consequences for drivers were mainly more stress and frustration. [ABSTRACT FROM AUTHOR]

The driver shortage is a perennial problem in the trucking industry. The review of literature reveals currently there is a lack of holistic approach to address driver shortage problem. This paper presents a systems approach to understand the factors influencing the issue of truck driver shortages in an emerging economy context and their evolution over time. For this study, we define truck driver shortage as: "The shortfall in the market when the quantity of truck drivers supplied is below the number of truck drivers demanded". A system dynamic model has been developed to study a representative truck segment unit. The model is validated and simulated for ten years. The findings from the proposed model indicate that the current system and other environmental factors do not support the truck driver supply increase, drivers and shortages are likely to become acute in the coming years. The study proposes appropriate intervention for the government, and other stakeholders must design proper steps to fill the gap beyond a specific timeframe. The study contributes to the existing literature as a pioneering work on driver shortages in the emerging economy context. Also, the study is one of its kind in using a holistic, quantitative approach to modeling the driver shortage phenomenon. In the process, the study elicits complex relationships that prevail among the factors included in the study. • A system approach to understanding key issues for truck driver shortages. • The current systems and other environmental factors are not associated with the driver supply increase. • A pilot study examining driver shortages in the emerging economy context. [ABSTRACT FROM AUTHOR]

• Using bibliometric methods, the main factors affecting the safety of truck drivers in terms of drivers, vehicles, roads, environment, and management were comprehensively sorted out. • This paper summarizes the method of constructing a user persona and proposes a theoretical framework for a truck driver persona. • Current management interventions are reviewed and intervention processes and methods covering pre-post, in-transit and on-post sessions are proposed. • This paper summarizes the limitations of the existing research base and proposes future directions for research. The trucking industry urgently requires comprehensive methods to evaluate driver safety, given the high incidence of serious traffic accidents involving trucks. The concept of a "truck driver persona" emerges as a crucial tool in enhancing driver safety and enabling precise management of road transportation safety. Currently, the road transport sector is only beginning to adopt the user persona approach, and thus the development of such personas for road transport remains an exploratory endeavor. This paper delves into three key aspects: identifying safety risk characteristic parameters, exploring methods for constructing personas and designing safety management interventions. Initially, bibliometric methods are employed to analyze safety risk factors across five domains: truck drivers, vehicles, roads, the environment, and management. This analysis provides the variables necessary to develop personas for road transportation drivers. Existing methods for constructing user personas are then reviewed, with a particular focus on their application in the context of road transportation. Integrating contemporary ideas in persona creation, we propose a framework for developing safety risk personas specific to road transportation drivers. These personas are intended to inform and guide safety management interventions. Moreover, the four stages of driver post-evaluation are integrated into the persona development process, outlining tailored safety management interventions for each stage: pre-post, pre-transit, in-transit, and on-post. These interventions are designed to be orderly and finely tuned. Lastly, we offer optimization recommendations and suggest future research directions based on safety risk factors, persona construction, and safety management interventions. Overall, this paper presents a safety management-oriented research technology system for constructing safety risk personas for truck drivers. We argue that improving the design of the persona index system, driven by big data, and encompassing the entire driver duty cycle—from pre-post to on-post—will significantly enhance truck driver safety. This represents a vital direction for future development in the field. [ABSTRACT FROM AUTHOR]

• Light/medium truck drivers involved had higher FROI rates than heavy truck drivers involved in crashes. • Younger light/medium truck drivers were represented at a higher proportion in crash FROIs compared to younger heavy truck drivers. • The retail trade industry made up the largest percentage of light/medium truck local crash FROIs (47%); the transportation and warehousing industry was most frequently cited in heavy truck FROIs. • Light/medium truck crashes most frequently involved being rear-ended, running red lights, and turning in front of other vehicles. Objective: Multiple heavy truck driver injury studies exist, but there is a paucity of research on light and medium truck driver injuries. The objective of this study was to use first report of injury (FROI) data to: (a) compare demographic and injury characteristics; (b) assess workers' compensation (WC) claim disposition and lost work time status; and (c) describe injury scenarios by vehicle type for heavy truck and light/medium truck driver local crashes. Method: Kentucky Department of Workers' Claims FROI quantitative and free text data were analyzed for years 2010–2019. Of 800 total FROIs, 451 involved heavy trucks and 349 involved light or medium trucks. Results: There was a higher light/medium truck driver crash FROI rate compared to the heavy truck driver crash FROI rate. There was a higher proportion of younger light/medium truck driver crash FROIs compared to younger heavy truck driver crash FROIs. The retail trade industry made up the largest percentage of light/medium truck local crash FROIs (47%); the transportation and warehousing industry was most frequently cited in heavy truck FROIs (46%). The heavy truck types most frequently identified in FROIs were semi-trucks (13%) and dump trucks (11%). The most common light/medium truck type identified was delivery trucks (30%). Most commonly, heavy truck crash FROIs involved rollovers, driving off/overcorrecting on narrow roadways, and driving downhill/unable to downshift. Light/medium truck crash FROIs most frequently involved being rear-ended, running red lights, and turning in front of other vehicles. Conclusions: The utilization of WC FROI data highlighted top injury scenarios and specific vehicle types for targeting driver safety training among truck drivers, particularly light/medium truck drivers. Road safety policies regarding driver training, crash reviews, and in-vehicle monitoring systems are needed for truck drivers with previous crash injuries, especially for light and medium truck drivers. Practical applications: Enhanced safety training on speeding on narrow roadways, on nearing intersections, and on downshifting on hills is needed for semi-truck, dump truck, and coal truck drivers with previous crash injuries. Rear-end crash prevention training (e.g., gradual stopping and checking mirrors) is needed for drivers of furniture, automotive parts and accessories, and groceries and soft drink delivery trucks with previous crash injuries. [ABSTRACT FROM AUTHOR]

To avoid traffic accidents due to driver fatigue in long-haul trucking, there are laws and provisions all over the world that regulate breaks and rest periods of truck drivers. When aiming to follow the existing hours of service regulations, however, parking space for trucks in service areas is often scarce, especially along crowded mainlines. In this context, the paper on hand investigates whether a central platform coordinating the usage of limited parking capacity on the supply side and truck drivers' individual drive and rest schedules on the demand side can be advantageous. Specifically, we formulate a holistic optimization problem, which jointly determines the drive and rest schedules for a given set of trucks with fixed routes and coordinates their assignment to a limited number of available parking spaces over time. To solve this problem, we introduce a heuristic solution procedure, which is benchmarked with the status quo, where each driver seeks parking space individually, and a booking platform that allows to reserve parking space ahead of arrival. Our computational results reveal that a booking platform that produces much less organizational overhead is almost as effective as a central matching platform based on perfect knowledge of all truck schedules. • We optimize the coordinated parking of trucks taking rests and breaks. • A decomposition heuristic is presented. • We benchmark coordinated parking with the status quo and a booking platform. • We show that a booking platform is almost as efficient as optimized coordination. [ABSTRACT FROM AUTHOR]

• The organization-level safety climate (OSC) scale and the group-level safety climate (GSC) scale for truck drivers were adapted into Chinese. • Agreeableness, neuroticism and openness were confirmed to be predictors of both positive and dangerous driving behaviors of truck drivers. • Trucking safety climate at both the organizational level and the group level could mediate the effect of personality on driving behavior. • Our study reminded trucking companies to pay attention to the construction and promotion of both levels of the safety climate. Introduction: The number of traffic accidents involving truck drivers remains high, and strategies to eliminate the probability of such accidents have been proposed, among which enhancing the safety climate has attracted much interest. The main purpose of the current study was to validate the Chinese version of the safety climate scale for truck drivers and apply it to investigate the mediating effect of safety climate between truck driver personality and driving behavior. Method: A total of 389 male truck drivers completed the Big Five Inventory, the Chinese version of the trucking safety climate scale, the driver behavior questionnaire and the positive driver behavior scale. Results: The reliability and validity of the Chinese version of the organization-level safety climate scale and the group-level safety climate scale were confirmed through factor analysis. More importantly, a path analysis revealed that the organization-level safety climate mediated the effects of agreeableness and neuroticism on aggressive violations, ordinary violations and lapses, while the group-level safety climate mediated the influences of agreeableness, neuroticism and openness on positive behavior and all four kinds of aberrant driving behaviors. Conclusions: This study introduced the trucking safety climate scale into China and stressed the significance of improving both the organizational and the group levels of safety climate to reduce accidents involving professional truck drivers. Practical Applications: First, the adapted safety climate scale for Chinese truck drivers contributes to further investigating the role that safety climate plays in the safety problem of truck drivers in China. Moreover, the critical impacts of both levels of the trucking safety climate serve as reminders for relevant companies to not only pay attention to establishing an organization-level safety climate but also invest more effort into promoting the group-level safety climate. [ABSTRACT FROM AUTHOR]

Objective/background: Insomnia among truck drivers may contribute to traffic accidents. We previously reported that truck drivers had adverse lifestyle habits, including smoking and drinking alcohol, in addition to enforced sedentarism making them more vulnerable to lifestyle-related diseases. This study aimed to investigate the association between insomnia and diabetes, as well as the effect of anxiety related to driving tasks on this association.Patients/methods: A total of 755 male truck drivers under 65 years of age who belonged to Akita prefecture Truck Association in Japan were investigated. Insomnia and State-Trait Anxiety Inventory (STAI) score were assessed using a self-administered questionnaire in 2020. Insomnia was defined in accordance with International classification of sleep disorders-third edition. Diabetes was defined as a fasting blood glucose level of ≥126 mg/dL and/or use of anti-diabetic drugs using data obtained from health checkups in 2018.Results: The proportions of truck drivers with insomnia and diabetes were 13.9% and 9.7%, respectively, which were higher than those in the general working population in Japan. After adjusting for age, smoking, drinking, daily hours of driving and sleep, and hypertension, the STAI score (odds ratio [OR] for the highest quartile vs. the lowest: 3.88, 95% confidence interval [CI]: 1.84-8.18) and diabetes (OR: 2.49, 95% CI: 1.11-5.60) were found to be associated with insomnia. A statistical interaction with STAI scores was not observed between diabetes and insomnia.Conclusions: The present study demonstrated that diabetes and anxiety are independently and significantly associated with insomnia in male Japanese truck drivers. [ABSTRACT FROM AUTHOR]

• Truck drivers face numerous work-related constraints due to delivery deadlines. • These constraints increase perceived stress among truck drivers. • Perceived stress was linked to risky driving behaviors and poor mental health. • Some differences were found between French and non-French truck drivers. Introduction: The growth of the European market for road-freight transport has recently led to important changes. The growing number of foreign pavilion drivers transiting in France, which plays a bridging role among European countries, has influenced the lives of truck drivers by increasing competition, pressure on day-to-day activities, and constraints related to delivery deadlines. Adding this new pressure to those inherent in the road-freight transport sector has raised concerns, especially ones linked to levels of perceived stress by truck drivers. Method: With safety concerns in mind, we devised a questionnaire aimed at understanding how French truck drivers and non-French truck drivers, passing through four highway rest areas in France perceive stress, organizational factors, mental health, and risky driving behaviors. A sample of 515 truck drivers took part in the survey (260 French nationals), 97.9% of whom were male. Results: The results of a structural equation model indicated that perceived stress can increase self-reported risky driving behaviors among truck drivers. Furthermore, organizational factors and mental health were closely linked to perceived stress. Finally, some differences were found between French and non-French truck drivers with respect to mind-wandering and mental health, and to perceive driving difficulties to overcome and driving skills. Practical Applications: Several recommendations based on the findings are provided to policymakers and organizations. [ABSTRACT FROM AUTHOR]

• Empirical data about driving anger of truck drivers were provided. • Psychometric properties of short driving anger scale were analysed. • Factor structure of driving anger of truck drivers was examined. • One factor of driving anger of truck drivers was confirmed. • Driving anger significantly predicted truck drivers' aberrant driving behaviour. Truck-related accidents in China have substantially increased. Truck driver safety issues have elicited considerable attention from transportation safety researchers. Driving anger is one of the most important factors that influence driving behaviour. However, little knowledge about the driving anger of truck drivers was available in the relevant literature. This study aimed to (1) provide empirical data about the driving anger of truck drivers, (2) examine the factor structure and psychometric properties of the driving anger scale (DAS) with a sample of truck drivers and (3) explore how the driving anger of truck drivers predicts their aberrant driving behaviour. The three-part survey data from 475 randomly selected truck drivers were collected in this study. The self-reported aberrant driving behaviour that was measured with the driving behaviour questionnaire was predicted by the drivers' DAS ratings. One-factor structure and three-factor structure of the DAS were tested for Chinese truck drivers. Results indicated that the one-factor structure of the six-item DAS was more suitable for truck drivers than the three-factor structure. The factor structure of the DAS for Chinese truck drivers was consistent with the structure of the original version by Deffenbacher, Oetting, and Lynch (1994). The internal consistency, convergent validity and criterion-related validity of the six-item DAS were acceptable. Driving anger amongst truck drivers can be used to predict their aberrant driving behaviour. This study identified several practical implications to reduce the driving anger of truck drivers and consequently improve their driving behaviour. [ABSTRACT FROM AUTHOR]

Extensive studies have examined the effect of variables such as demographic characteristics, insomnia, and working conditions of drivers individually on inattention-related error as well as expression of anger in driving. Nevertheless, so far no study has tested the concurrent effect of these factors on crashes. This study has dealt with indirect investigation of the effect of variables including demographic characteristics, insomnia, and working conditions of drivers on inattention-related error and expression of anger in driving (as mediation model). Next, the effect of these two variables on the probability of incidence of road crashes has been assessed among truck drivers. For this purpose, 780 Iranian truck drivers were interviewed by validated questionnaires including insomnia severity index (ISI), attention related driving errors scale (ARDES), and driving anger expression (DAX). To confirm the validity of these questionnaires, confirmatory factor analysis (CFA) was used. Next, structural equation modeling (SEM) was employed for investigating the relationships between these latent variables and truck drivers' demographic characteristics, working conditions and their crash involvement. SEM results indicated that as the severity of insomnia among drivers increased, they committed more inattention-related errors, and also expressed more anger during driving. Generally, the results of this study indicated that with increase in the extent of experience and safety knowledge of drivers and improvement in their working conditions and their sleep status, it is possible to reduce inattention-related error and expression of anger during driving among heavy vehicle drivers. • Demographics characteristics, insomnia, inattention-related error, and expression of anger of truck drivers are analyzed. • The inattention-related error and expression of anger among the truck drivers increase their probability of road accidents. • Older drivers with more experience and higher levels of education express less anger during driving. • The severity of truck drivers' insomnia has a direct relationship with inattention-related errors, and expressed anger. [ABSTRACT FROM AUTHOR]

In this paper, a label-setting algorithm is proposed in order to efficiently solve the weekly Truck Driver Scheduling Problem (TDSP), which can be seen as a single-machine problem that minimizes the completion time of a fixed sequence of jobs with release and due times and break periods. A focus is made on the night working rule that has only partially been considered in the literature. In the wide literature dedicated to Vehicle Routing Problems, some published heuristics and exact algorithms include constraints related to break/rest periods. But the night working rule defined by the European Community Social Legislation is only considered in some simplified and sub-optimal versions. This constraint is of interest, since it has a direct impact on the practical applicability of routes. Moreover, from a scheduling point of view, the night working rule enforces minimum and maximum time lag constraints, which makes challenging the development of an efficient solution algorithm. The method is benchmarked with a mixed integer linear programming model. Computational experiments show the efficiency of the proposed methods and the effect of the night working constraint on the feasibility of the schedules. [ABSTRACT FROM AUTHOR]

Truck drivers are essential to modern supply chains. However, the companies that employ them are having trouble keeping them. At many trucking firms, annual turnover rates approach 100 percent or higher. This paper develops a method for predicting individual truck driver turnover events before they happen by applying supervised machine learning classifiers to a new source of operational truck driver data. Our paper uses Electronic Logging Device (ELD) data, which comprise newly federally-mandated, time-stamped work logs collected from approximately 1200 American long-haul truck drivers over 3 years. We train three supervised machine learning classifiers (logistic regression, random forests, and support vector machines) on this data and achieve 60 to 70 percent prediction accuracy and 50 to 60 percent recall across two 5-fold cross-validated experiments. We observe that the quantity and consistency of week-day driving assignments explain most of our models' predictive power. We offer these results as both a new technical tool, as well as novel managerial insights for improving global truck driver retention problems for the benefit of supply chains worldwide. [Display omitted] • Newly available operational data from long haul truck drivers is analyzed. • Three machine learning classifiers are trained on the operational data. • Predictive models achieve 60–70 percent accuracy, 50–60 percent recall. • Models show potential to enable interventions to retain truck drivers. [ABSTRACT FROM AUTHOR]

• An optimization method considering truck response behaviors of FCW algorithm was. • Driver response behavior can be divided into three behaviors. • Linear SVM and LSTM were conducted to predict the reaction distance and braking distance. Forward collision warning (FCW) systems have been widely used in trucks to alert drivers of potential road situations so they can reduce the risk of crashes. Research on FCW use shows, however, that there are differences in drivers' responses to FCW alerts under different scenarios. Existing FCW algorithms do not take differences in driver response behavior into account, with the consequence that the algorithms' minimum safe distance assessments that trigger the warnings are not always appropriate for every driver or situation. To reduce false alarms, this study analyzed truck driver behavior in response to FCW warnings, and k-means clustering was adopted to classify driver response behavior into three categories: Response Before Warning (RBW), Response After Warning (RAW), and No Response (NR). Results showed that RBW clusters tend to occur at long following distances (>19 m), and drivers applied braking before the warning. In RAW clusters, deceleration after warning is significantly more forceful than before warning. NR clusters occur at short distances, and deceleration fluctuates only slightly. To optimize the FCW algorithm, the warning distance was divided into reaction distance and braking distance. The linear support vector machine was used to fit the driver reaction distance. The long short-term memory method was used to predict braking distance based on each of the three response scenarios: R2 was 0.896 for RAW scenarios, 0.927 for RBW scenarios, and 0.980 for NR scenarios. Verification results show that the optimized truck FCW algorithm improved safety by 1 % to 5.1 %; accuracy reached 97.92 %, and the false alarm rate was 1.73 %. [ABSTRACT FROM AUTHOR]

• The role of at-fault party in large truck-involved crashes has been investigated. • Support Vector Machine and Random Parameter Logit Model were utilized for analysis. • Fatigue and deviation to left in the at-fault truck-driver crashes led to fatality. Introduction: Reducing the severity of crashes is a top priority for safety researchers due to its impact on saving human lives. Because of safety concerns posed by large trucks and the high rate of fatal large truck-involved crashes, an exploration into large truck-involved crashes could help determine factors that are influential in crash severity. The current study focuses on large truck-involved crashes to predict influencing factors on crash injury severity. Method: Two techniques have been utilized: Random Parameter Binary Logit (RPBL) and Support Vector Machine (SVM). Models have been developed to estimate: (1) multivehicle (MV) truck-involved crashes, in which large truck drivers are at fault, (2) MV track-involved crashes, in which large truck drivers are not at fault and (3) and single-vehicle (SV) large truck crashes. Results: Fatigue and deviation to the left were found as the most important contributing factors that lead to fatal crashes when the large truck-driver is at fault. Outcomes show that there are differences among significant factors between RPBL and SVM. For instance, unsafe lane-changing was significant in all three categories in RPBL, but only SV large truck crashes in SVM. Conclusions: The outcomes showed the importance of the complementary approaches to incorporate both parametric RPBL and non-parametric SVM to identify the main contributing factors affecting the severity of large truck-involved crashes. Also, the results highlighted the importance of categorization based on the at-fault party. Practical Applications: Unrealistic schedules and expectations of trucking companies can cause excessive stress for the large truck drivers, which could leads to further neglect of their fatigue. Enacting and enforcing comprehensive regulations regarding large truck drivers' working schedules and direct and constant surveillance by authorities would significantly decrease large truck-involved crashes. [ABSTRACT FROM AUTHOR]

• Slippery horizontal curves are contributed to the high risk of curve-related crashes. • Drivers' behavior is an influential factor contributing to 93% of crashes. • Connected Vehicles (CV) notifications enhance driver's situational awareness. • Drivers adapt their driving behavior based on CV notifications on horizontal curves. • A less likelihood of Run-off-Road crashes can be obtained in the CV environment. Although drivers can adequately adjust their operating speed according to the road curvature, they show a lack of recognition regarding the pavement friction conditions. In this regard, inappropriate speed selection on Horizontal Curves (HCs) with reduced surface friction can lead to a remarkable rate of run-off-road, sideswipe, head-on, and rollover crashes, especially on rural highways. Aligned with the Connected Vehicle (CV) Pilot Program on Interstate-80 in Wyoming, this study scrutinizes how CV advisory/warning messages can enhance traffic safety on slippery HCs. To this aim, a roadway consists of two HCs with regular and slippery pavement conditions was designed in a high-fidelity driving simulator experiment. A total of 24 professional truck drivers were recruited to drive the simulated roadway under CV and non-CV environments. In the CV scenario, drivers were informed about the pavement conditions and the advisory speeds before entering HCs. In contrast, no messages were given to non-CV drivers. Truck drivers' behaviors in both scenarios were quantified using four Kinematic-based Surrogate Measures of Safety (K-SMoS), including deviation from the pathway, instantaneous acceleration, lateral speed, and steering angle. CVs' trajectories were statistically compared to non-CVs in terms of the central tendency and dispersion using the Wilcoxon Signed-Rank Test (WSRT) and Median Absolute Deviation (MAD), respectively. The results of WSRT depicted, under the effect of CV advisory/warning messages and throughout the slippery HC, the central tendency of four K-SMoS could be shifted toward zero by 23% up to 99%. This shifting is associated with a significant safety enhancement that potentially can reduce the likelihood of curve-related crashes on slippery HCs. It was revealed that the variation in drivers' behavior on the slippery HC could be minimized in the CV environment, where 54% up to 95% reduction in the dispersions of four K-SMoS were observed, leading to more certainty in drivers' behavior. [ABSTRACT FROM AUTHOR]

Introduction: The prevalence of obstructive sleep apnea (OSA) among professional truck drivers has varied from 28 to 78% in previous studies. In this study we wanted to estimate the prevalence of OSA and OSA with both subjectively measured sleepiness and objectively measured ability to stay awake (ie obstructive sleep apnea syndrome, OSAS) among professional truck drivers in Finland.Subjects and Methods: Altogether 2066 professional truck drivers received a structured questionnaire. 175 drivers had a clinical examination and sleep laboratory studies, which included respiratory polygraphy (RP) and maintenance of wakefulness test (MWT). Three groups were formed: 75 subjects with suspected sleep apnea, 75 healthy controls and a random sample of 25 subjects.Results: 1095 drivers answered the questionnaire. RP was performed on 172 drivers and 167 drivers participated in MWT. The mean age was 40.7 years and the mean BMI was 27.7 kgm-2. The prevalence of sleep apnea in professional truck drivers using various criteria were: AHI ≥5: 40.1%, AHI≥ 15: 16.2% and, AHI≥ 30: 7.2%. The prevalence depended on clinical history. Prevalence of AHI≥5 varied between 20 and 56.9% and prevalence of AHI≥15 was 4.3-25%. Altogether 4.8% of subjects with AHI ≥15 had abnormally short sleep latency in MWT (<19.4 min).Conclusions: Moderate sleep apnea is common among professional truck drivers but significant inability to stay awake, defined as MWT <19.4 min, is found in about one of twenty professional drivers. [ABSTRACT FROM AUTHOR]

• S hift-working drivers frequently attribute on-duty sleepiness to sleep loss. • Tram drivers additionally report insufficient rest breaks as causing sleepiness. • Long-haul truckers working night shifts specify time of day as causing sleepiness. • Other causes (e.g., traffic, visibility) are not widely linked to severe sleepiness. • Future studies should examine task-related sleepiness in occupational driving. Identifying the causes of sleepiness in various safety-critical work environments is necessary for implementing more efficient fatigue management strategies. In transportation, little is known about drivers' own perceptions of these causes. Therefore, we instructed shift-working tram (n = 23) and long-haul truck drivers (n = 52) to report at the end of their shifts what made them sleepy if they felt so. These self-reports, measured on-duty sleepiness, and sleep amounts were recorded on every shift over a period of 2–3 weeks per driver. The causes of sleepiness were queried with smartphone applications and sleep logs. Sleepiness was measured with the Karolinska Sleepiness Scale (KSS) and sleep with wrist-worn actigraphs. Data were analyzed using generalized estimating equations. Sleep loss and insufficient rest breaks were commonly reported as causing sleepiness among the tram drivers, whereas time of day and sleep loss were the leading causes among the truck drivers. Other causes, such as traffic or cabin conditions, were not frequently mentioned. During morning, day, and evening shifts, the truck drivers were less likely to report insufficient rest breaks as causing sleepiness than the tram drivers. Similarly, during morning shifts, the truck drivers were less likely to attribute their sleepiness to sleep loss. In shifts with drives reporting severe sleepiness (KSS ≥ 7 at least once, 18–21% of shifts), sleep loss was significantly reported as causing sleepiness among both groups. Reporting insufficient rest breaks was associated with severe sleepiness among the tram drivers, whereas time of day showed the same among the truck drivers. The results highlight the need for addressing sleep-related fatigue in transportation and provide directions for future research with regard to secondary causes of sleepiness. [ABSTRACT FROM AUTHOR]

Truck parking shortages are a critical concern for both the trucking industry and truck drivers in the USA. To tackle growing shortages, parking capacity expansions are needed. This paper presents a hybrid agent-based simulation and optimization approach to model truck movements and driver behaviors to determine feasible locations for truck parking facility capacity expansions across a state. The simulation model considers the driving limit and rest requirements of hours-of-service regulations set forth by the federal regulations. By leveraging observed historical truck GPS data, agent profiles are created, capturing truck trip origins, necessary stops, trip destinations, and parking behaviors. The simulation model estimates parking facility utilization over time by identifying where, when, and how long truck drivers rest. The estimated parking usage data is then fed into a maximal coverage capacitated multiple facility location optimization with multiple service model to deduce capacity expansion locations given budgetary restrictions. Ultimately the model recommends where and how many parking spaces to add to accomplish a certain level of parking overcrowding. The simulation and optimization approaches are finally integrated into a map-based, user adaptable decision support tool. [ABSTRACT FROM AUTHOR]

• Truck drivers are classified into three groups by using latent class clustering. • Some risk factors affecting crash severity have different impacts on distinct groups. • More driver-related risk factors are associated with the high-risk driver group. Introduction: Fatal crashes that include at least one fatality of an occupant within 30 days of the crash cause large numbers of injured persons and property losses, especially when a truck is involved. Method: To better understand the underlying effects of truck-driver-related characteristics in fatal crashes, a five-year (from 2012 to 2016) dataset from the Fatality Analysis Reporting System (FARS) was used for analysis. Based on demographic attributes, driving violation behavior, crash histories, and conviction records of truck drivers, a latent class clustering analysis was applied to classify truck drivers into three groups, namely, "middle-aged and elderly drivers with low risk of driving violations and high historical crash records," "drivers with high risk of driving violations and high historical crash records," and "middle-aged drivers with no driving violations and conviction records." Next, equivalent fatalities were used to scale fatal crash severities into three levels. Subsequently, a partial proportional odds (PPO) model for each driver group was developed to identify the risk factors associated with the crash severity. Results' Conclusions: The model estimation results showed that the risk factors, as well as their impacts on different driver groups, were different. Adverse weather conditions, rural areas, curved alignments, tractor-trailer units, heavier weights and various collision manners were significantly associated with the crash severities in all driver groups, whereas driving violation behaviors such as driving under the influence of alcohol or drugs, fatigue, or carelessness were significantly associated with the high-risk group only, and fewer risk factors and minor marginal effects were identified for the low-risk groups. Practical Applications: Corresponding countermeasures for specific truck driver groups are proposed. And drivers with high risk of driving violations and high historical crash records should be more concerned. [ABSTRACT FROM AUTHOR]

• Daily work hours predicted both HOS compliance and safety risk. • Sleep duration, fast pace of work, and miles per week predicted HOS compliance. • Supervisor support, self-reporting fatigue, and work hours predicted safety risk. • Greater HOS compliance was a nonsignificant predictor of safety risk. • Compensation type was a nonsignificant predictor of HOS compliance and safety risk. U.S. long-haul truck drivers (LHTD) experience the most work-related fatalities of any occupation. Hours-of-service (HOS) regulations constitute key public policies aimed at improving safety outcomes; however, little is known about the factors that are associated with HOS compliance, and questions remain about the efficacy of HOS laws in improving safety. This study seeks to identify factors associated with HOS compliance and to determine the significance of HOS compliance in sleep-related safety risk. Using cross-sectional survey data from 260 U.S. LHTD that measured demographic, work organization, sleep health, hours-of-service compliance, and sleep-related safety performance characteristics, we: 1) compiled descriptive statistics to summarize the variables included in this study; 2) performed bivariate correlation analyses between an HOS composite variable called "Hours-of-Service Violations" and the demographic, work organization, and sleep health variables; 3) conducted an ordinal logistic regression analysis, using the HOS composite variable as the outcome variable; and 4) conducted a multinomial logistic regression analysis, using a sleep-related safety performance composite variable called "Sleep-Related Safety Risk" as the outcome variable. Higher scores on the HOS composite variable were significantly associated with more miles driven per week, longer daily work hours, a higher frequency of a fast pace of work, shorter sleep duration, and poorer sleep quality. Statistically significant predictor variables in the Hours-of-Service Violations composite variable model were driving less than 2,500 miles per week (OR = 0.53), working less than 11 h daily (OR = 0.19) or between 11 and 13 h daily (OR = 0.43); a lower frequency of fast pace of work (OR = 0.42); and worknight sleep duration (OR = 0.80). Fewer than 11 h of work daily (OR = 0.37), a higher perception of supervisor support (OR = 0.17), and ever having told supervisor about being too tired to drive (OR = 0.42) were significant predictors in the Sleep-Related Safety Risk composite variable model, while the hours-of-service compliance variables were not. Reducing daily work hours and pace of work, strengthening driver-supervisor relationships and improving supervisor leadership and risk management techniques, making driver compensation fairer, and revisiting HOS policies may represent high-leverage targets for improving regulatory compliance and safety outcomes. [ABSTRACT FROM AUTHOR]

Transport workers like seafarers, truck-, bus-, train- and taxi drivers and fishers have a known great inequity in health at work including high risk of developing type 2 diabetes. Their routine mandatory medical examinations use urine glucose for diabetes check with more than 50% false negatives, which should be replaced by high sensitive tests for diabetes-2, like A1C, Fasting Glucose (FPG) or Oral Glucose Tolerance Test (OGTT). [ABSTRACT FROM AUTHOR]

Giving performance feedback to increase employee productivity has shown ambiguous results: Many laboratory studies have demonstrated a statistically significant effect of feedback on performance, but the effect is less evident in the field. In addition, the effects of digital feedback are not well understood. This feedback differs from other types of feedback as it is personalized, but not generated and provided by another human, e.g., the supervisor, but by a machine. We conducted a field experiment in which we introduced digital feedback to improve truck drivers' performance by enhancing their energy-efficient driving behavior. Our results show that digital feedback led to a statistically significant but small overall improvement in energy efficiency and performance that is driven by the improvement of initially very good- and very bad-performing drivers. This effect is robust, even when considering the differences in the room for improvement relative to the initial performance. [ABSTRACT FROM AUTHOR]

This paper presents a methodology to identify the economic impact of different policies for heavy vehicle routing and driver scheduling in long-haul full-load trips with intermediate stops for refueling and for regulation-driven meal and rest periods. This methodology can be used both as a planning tool to help policy-makers define policies through scenario simulations, and as an evaluation tool to examine ex-post economic impact once policies have been changed as illustrated herein for the Brazilian case. The paper introduces a mathematical formulation, proposes a state-space graph search approach, and develops an algorithmic solution for a variant of the vehicle routing and truck driver scheduling problem with intermediate stops (VRTDSPIS), which is especially important in countries with vast territory where long-range freight trucking trips are common. In addition, it is particularly relevant for the economies of emerging markets that may have evolving regulatory frameworks and suboptimal infrastructure environments. Regulatory maximum driving and minimum driver resting time windows impose significant constraints to trucker routines, especially in sparsely populated regions through which long range vehicle trips with full load are common. Such trips can take days to be concluded and numerous alternative routes are possible, each with very different alternatives for refueling and rest. While the literature to date has focused mostly on truck driver scheduling given pre-determined routes and individual country's laws and regulations, this paper proposes a joint solution for the routing and scheduling problem with time window constraints, determining simultaneously a routing and scheduling plan that meets all maximum driving and minimum resting time interval regulatory requirements at minimum cost. An illustration applying the methodology to evaluate the economic impact of truck driver regulatory changes in Brazil is presented. [ABSTRACT FROM AUTHOR]

• Professional truck drivers' safety performance is captured by a six-factor model. • Compliance and communication factors are important predictors of hard braking. • General safety performance factors generalize to truck driver safety performance. In this investigation involving 227 lone professional truck drivers from a national transportation company in a low and moderate-income country (LMIC), Colombia, a multidimensional model of drivers' safety performance and expectations concerning how safety performance dimensions would predict hard braking were evaluated. The results supported a multidimensional conceptualization of professional truck drivers' safety performance, with factors aligned with confirmed general safety performance factors and occupation-specific factors. Furthermore, results supported the expectation that the dimensions associated with communicating safety information and complying with laws and regulations would predict hard braking over and above less conceptually relevant safety performance dimensions such as using personal protective equipment and preparing to drive, safety climate, and region of operation. Notably, a dimension of safety performance expected to promote workplace safety, the communication of health and safety information, was associated with increased hard braking. We discuss the implications of a multidimensional conceptualization and measure of safety performance for studying workplace safety for professional truck drivers in Colombian organizations and beyond. [ABSTRACT FROM AUTHOR]

• The introduction of a mobile application to improve efficiency of emptying operators' business in Bamako, Mali is discussed. • The application provides useful information for truck drivers and owners, operation managers and local authorities alike. • Application use reduces over time since drivers and owners have interest to keep their business out of official control. • Semi-mechanical operators still use the app contributing to higher income and serving a niche market inaccessible by trucks. In Bamako District, capital of Mali with 2.2 million inhabitants, 98.5 % of the population resorts to autonomous sanitation. This results in a considerable production of fecal sludge, accounting for more than 600,000 m³/year. This article shows how a smartphone application can support and facilitate the management of emptying services. It is based on the experience of Join For Water & PRACTICA who have designed a smartphone application, called 'Kolochilikélan' which in Bambara means 'the controller', to allow all partner emptying operators to monitor and control their activities. [ABSTRACT FROM AUTHOR]

• 51 rear-end conflicts, from a naturalistic driving study in China, were analyzed. • Kinematic and glance behavior variables were extracted from videos of conflicts. • Before conflicts, Chinese drivers directed most of the off-path gazes towards mirrors. • Chinese drivers kept short headways and responded fast when looking back on path. • Differences in driver behavior between China and Western countries were found. Introduction: Rear-end crashes are one of the most frequent crash types in China, leading to significant economic and societal losses. The development of active safety systems – such as Automatic Emergency Braking System (AEBS) – could avoid or mitigate the consequences of these crashes in Chinese traffic situations. However, a clear understanding of the crash causation mechanisms is necessary for the design of these systems. Method: Manually coded variables were extracted from a naturalistic driving study conducted with commercial vehicles in Shanghai. Quantitative analyses of rear-end crashes and near crashes (CNC) were conducted to assess the prevalence, duration, and location of drivers' off-path glances, the influence of lead vehicle brake lights on drivers' last off-path glance, and driver brake onset, and the influence of off-path glances and kinematic criticality on drivers' response to conflicts. Results: The results indicate that the Chinese truck drivers in our study rarely engage in distracting activities involving a phone or other handheld objects while driving. Instead, they direct their off-path glances mainly toward the mirrors, and the duration of off-path glances leading to critical situations are shorter compared to earlier analyses performed in Western countries. The drivers also often keep small margins. Conclusions: Overall, the combination of short time headway with off-path glances directed toward the mirror originates visual mismatches which, associated to a rapid change in the kinematic situation, cause the occurrence of rear-end CNC. When drivers look back toward the road after an off-path glance, a fast response seems to be triggered by lower values of looming compared to previous studies, possibly because of the short time headways. Practical Application: The results have practical implications for the development of driver models, for the design of active safety systems and automated driving, and for the design of campaigns promoting safe driving. [ABSTRACT FROM AUTHOR]

• The effects of stimulant use on cognition of truck drivers is an unpublished issue. • Stimulant use improved attention at the cost of the commitment of more errors. • The confounding effects due to psychiatric and sleep variables were controlled for. • The creation of a safe work environment for truck drivers is needed. Introduction: Brazil has one of the highest traffic accident rates in the world. Cargo vehicles are the second most common type of vehicle involved in these accidents. Driving under the influence of drugs increases the risk of truck drivers being involved in road traffic injuries or fatalities. Psychostimulant drug use among truck drivers is of particular interest, as they use these drugs to maintain their occupational performance. Aim: The aim of this study is to identify the prevalence of psychostimulant drug use among truck drivers through toxicological analyses of urine and saliva samples and to evaluate its effects on the drivers' performance on attention level and executive functioning tests. Methods: A nonprobabilistic sample of 684 truck drivers was recruited at truck stops along three highways in Sao Paulo, Brazil. Participants were requested to answer questions on personal information and work-related matters via a structured research instrument. Afterwards, they were asked to perform four cognitive tests and to complete other questionnaires on confounding variables. Strict exclusion criteria were enforced. Descriptive and inferential analyses were performed in the Minitab 18.1 and R programs, version 3.5.3. Then, the variables from the cognitive performance tests were included as response variables in the generalized linear regression models, separately, correcting for the effects from confounding variables. Results: 504 drivers were excluded from the data analysis due to health conditions that prevented them from safely participating in a cognitive performance evaluation. Therefore, 180 drivers remained in the sample; among them, 44 participants were categorized as users, and 136 were categorized as nonusers. The drug users performed better than the nonusers in almost all the cognitive tests in the univariate analysis. However, after controlling for the effects of confounding variables, we observed that despite drug users being faster at performing some tests, they committed more errors and had lower precision rates than nonusers in other measures. Moreover, users dealt with tasks that involved alternate and divided attention more poorly than nonusers. Conclusion: Psychostimulant drug use may initially seem advantageous to truck drivers since it allows users to maintain their attention for longer periods of time; however, the onus related to that use is much larger and should therefore be avoided by truck drivers and policed by competent authorities. [ABSTRACT FROM AUTHOR]

• Explored factors associated with managing the safety of older truck drivers. • Focus groups were conducted with fifteen safety managers in U.S. trucking companies. • Identified challenges in addressing the needs of older truck drivers. • Intervention must target the needs of this cohort and be realistic for the context. There is convincing evidence to suggest that older truck drivers are a highly valued cohort in the transportation industry. Thus, ensuring the safety, health and wellbeing of this workforce should be a priority so that transportation industry employers can develop strategies to keep them driving for as long as they are safe on the road. As a first step in achieving this goal, the aim of this study was to better understand the key factors associated with managing the safety, health and wellbeing of older drivers in the trucking industry. Two focus groups with safety managers (n = 15) trucking companies in the United States were conducted to gain an understanding of the barriers and facilitators in planning and managing the safety of older truck drivers. Data were analyzed using thematic analysis and involved a broad categorisation of all meaningful data into four key areas (i.e., advantages, concerns, current strategies and challenges). Themes were subsequently identified within each of the four key areas. The results confirmed that older truck drivers are valued in the workforce for their work ethic and safety attitudes; however, there are multiple and interrelated factors influencing the safety management of older truck drivers. It was also found that there are few interventions that are targeted to the specific needs of older truck drivers and aligned to the challenges in managing the safety risks. Overall, the findings of this study support recommendations to address the barriers associated with managing the safety of older truck drivers. [ABSTRACT FROM AUTHOR]

Transmission of pathogens between farms via animal transport vehicles is a potential concern; however, the available information on driver routines and biosecurity measures implemented during transport is limited. Given the above, the aim of this study was to describe and characterize the prevailing practices and biosecurity measures adopted by cattle transport drivers in Spain. Eighty-two drivers were surveyed via face-to-face or remotely. The survey included questions on general characteristics of the drivers (type of journeys and vehicles) together with biosecurity practices implemented during cattle transport and vehicle hygiene practices. Results showed that several risky practices are performed quite frequently such as visiting different premises with different levels of risk (e.g., breeder and fattening farms); entering the farm premises to load/unload animals, passing by several farms to load and unload animals, or not always cleaning and disinfecting the vehicle between travels, among others. To explore similarities among the drivers and identify groups sharing specific practices, hierarchical clustering on principal components (HCPC) was computed on the results of multiple correspondence analysis (MCA). The first three MCA dimensions (out of 13) were retained in the agglomerative clustering and four different clusters were identified. Clusters 1 and Cluster 4 accounted for 39.5% and 29.6% of respondents, respectively. The clusters were mainly differentiated by practices in the loading/unloading of cattle, such as the frequency of contact with animals remaining on the farm, and the frequency of the vehicle's disinfection between farms. Cluster 2 and Cluster 3 were of similar size, about 15% of respondents each. Cluster 2 consisted of drivers who mainly made journeys to slaughterhouse, while drivers in Cluster 3 were characterised by the use of working clothes and boots. Based on these findings, it is advisable to increase awareness on the role that animal transport can have in the spread of pathogens between cattle farms and the importance of biosecurity in preventing such transmission. There is also a need to support animal transport professionals in such task, not only through the development of initiatives to increase awareness, but also through the investment in improving cleaning and disinfection facilities and to consider the economic cost associated with some practices to not compromise the economic viability of the sector. [ABSTRACT FROM AUTHOR]

• Conducted in-depth interviews to understand truck driver wellbeing during COVID-19. • Applied the extended Job Demands-Resources theory as the theoretical framework. • Truck driver wellbeing is impacted by physical, psychological and social demands. • The COVID-19 pandemic exacerbated pre-existing demands and created novel demands. • Practical recommendations for trucking managers and policymakers are provided. Long-haul truck drivers tend to be exposed to myriad health and safety threats – a problem that was exacerbated amid the Covid-19 pandemic. However, we currently have a fragmented understanding of the demands that threaten truck drivers' health and safety and the resources that can protect them. Drawing on a recently extended version of Job Demands-Resources theory, the present study aimed to identify these demands and resources in order to develop a comprehensive understanding of truck driver wellbeing in times of crisis. Maximum variation and snowball sampling strategies were used to recruit nine truck drivers and seven managers to participate in semi-structured interviews. All interviewees were employed full-time by Canadian trucking companies and had worked in their current position for at least one year prior to and six months during the pandemic. A thematic analysis resulted in the identification of 12 occupational and pandemic-related demands that negatively impacted drivers' physical, psychological, and/or social wellbeing, and the identification of 4 individual-level and 4 organizational-level resources that buffered the harmful impact of these demands. In addition, our findings indicated that demands and resources originating from disparate work- and non-work domains interacted with each other to impact drivers' wellbeing. Theoretically, this research advances the safety literature by contributing to a deeper understanding of the factors impacting employee health and safety in times of crisis. Practically, this research offers recommendations for managers of trucking firms and policymakers to protect long-haul truck drivers, which can have important downstream consequences for supply chains and the economy. [ABSTRACT FROM AUTHOR]

• Truck drivers' working conditions: from working schedules to time away from family. • The load represents a main safety concern imposing careful storage on the vehicle. • Truck driver's attitudes and behaviours related to in-vehicle Technologies. • Truck Platooning and the new skills imposing new training needs. The platooning technology allows for two or more trucks running in convoy at a pre-defined distance between each other, being virtually connected using connectivity technology and automated driving support systems. It is recognized that truck platooning systems bring economical and environmental advantages. Thus, it is time for a transition from the existing truck freight activity towards truck platooning systems. This requires an important improvement in terms of in-vehicle technology, together with infrastructure improvement and truck drivers' acquisition of new technology-related skills. A holistic approach is previewed to identify both the requirements for the development of truck platooning services and the requests for their safe deployment in the real world. Then, qualitative data were collected from truck drivers working for two different Portuguese freight companies using Focus Groups (FG). Thus, three FG sessions were organized and carried out with a total of 22 truck drivers. Considering that age and experience on the job are important factors to take into consideration for technological changes on the job, their potential impact on truck drivers' activity was addressed on the focus group discussions. Anyway, the potential users' attitudes regarding any innovation on the job were addressed as a prevention of further negative attitudes or misuse. Having safety in mind as a permanent attitude toward on job innovation is actually the most important factor toward success. [ABSTRACT FROM AUTHOR]

Active traffic management is the ability to dynamically manage congestion based on prevailing and predicted traffic conditions. Several technologies and algorithms have been employed to develop a set of tools utilised by Traffic Control/Traffic Management Centres with the aim to mitigate disruptions and improve traffic flow in the road networks. One such tool, belonging to the Advanced Traveller Information Systems category is Variable Message Signs. Variable Message Signs are located in major roads and provide real-time traffic information to the drivers so as to modify their behaviour considering the prevailing traffic conditions. To this end, the question arises. Is active traffic management in urban road networks – as currently provided by Variable Message Signs – effective for professional drivers? In plain words, do professional drivers modify their route choice, i.e. divert under the provision of real-time traffic information? A stated preference survey was performed to truck and taxi drivers to identify their response to information provided by Variable Message Signs. Results, in the form of ordered probit models indicated that the provided information is not that relevant to the investigated driver categories, as it is for passenger car drivers. [ABSTRACT FROM AUTHOR]

• Road freight transport safety is best understood using a systems approach. • Freight transport is a competitive business, so an economic approach is appropriate. • Truck drivers who are paid for all of their work are safer in part because they work fewer hours. • Truck drivers working for companies that provide health insurance are safer. This study analyzes how truck drivers' compensation affects their safety performance, using moving violations as a proxy for safety. In addition to drivers' pay per mile driven, we employ fringe benefits as independent variables. The result suggests that the rate of pay per mile driven, and employment-based health insurance, significantly decrease the probability of moving violations. The result provides support for the hypothesis that high compensation for drivers improve drivers' safety performance, though other forms of compensation are not significantly related to the incidence of moving violations. [ABSTRACT FROM AUTHOR]

• Long-haul truck drivers encounter a complex work environment on a daily basis. • Occupational tenure is proxy for external factors influencing drivers' perceptions. • Researchers need to examine safety climate as antecedent of accidents and injuries. • Safety climate-behavior relationship somewhat attenuated by occupational tenure. This study aims to advance safety climate research by applying sociotechnical systems theory to investigate the moderating effect of occupational tenure (as an external influence) on the safety climate and driving safety behavior relationship. Safety climate research has largely focused on the internal work environment, but, from a systems perspective, long-haul truck drivers are highly influenced by the external environment of their organization because they are a distributed workforce. Although drivers are exposed to their company's internal message that safety is important, their knowledge of the overall trucking industry and their experiences external to their company may interact with their perception of safety climate. The sample consisted of long-haul truck drivers from seven U.S. trucking companies. Results were consistent with those of prior research in that safety climate had a positive impact on driving safety behavior for long-haul drivers and safety behavior mediated the relationship between safety climate and near misses. Additionally, and unique to this study, driving safety behavior was less dependent on safety climate for drivers with longer occupational tenure compared to those with shorter occupational tenure, albeit with that moderator accounting for a small percentage of variance. Implications regarding examining safety climate and safety outcomes in the trucking industry, and also regarding external and internal organizational influences affecting safety climate, will be discussed. [ABSTRACT FROM AUTHOR]

• Exploratory, qualitative study of truck drivers and transport customer companies. • Drivers cite examples of poor management of queuing and waiting at customers. • Customers and drivers identify similar contributing practices to the problem. • Financial incentives and operational factors shape management practices. • Customer companies often focus on legal compliance more than managing fatigue risk. • Strategies to promote genuine fatigue risk management are needed. Management of queuing and waiting for loading/unloading can influence the risk of fatigue for heavy truck drivers if it extends work hours or hampers drivers' access to rest. Previous studies identified non-driving activities as a major contributor to driver fatigue in this industry despite Chain of Responsibility (CoR) legislation in Australia that establishes a duty on road transport customers to ensure their loading/unloading practices do not result in fatigued driving or breaches of work hours. This study examined how customer companies manage queuing and waiting and the barriers to good management. In phase one, 41 long distance truck drivers identified customer company depots where they had experienced good and poorer management of queuing and waiting. A subgroup (n = 27) provided more information about recent experiences in a survey. In phase two, representatives of 13 depots identified in phase one completed a qualitative interview about their queuing and waiting management. Drivers in phase one identified problems contributing to long queuing and waiting times and limited rest opportunities, with implications for fatigue risk, including communication issues, procedural efficiency, constraints on drivers, equity and fairness, depot capacity and resource management. Company results in phase two corroborated driver concerns. Infrastructure limitations, truck scheduling and the way work is managed were the main types of contributors. Financial and operational considerations were common cited barriers to good management. Companies did not perceive their primary CoR duty included managing hours or work and rest opportunities. Strategies to promote genuine fatigue risk management in loading/unloading processes must be pursued. [ABSTRACT FROM AUTHOR]

The relatively low levels of employee well-being reported among truck drivers directly relate to some of the key challenges faced in the transportation industry, including high turnover of staff and difficulties attracting people to the profession. Drawing on the job demands-resources model, this study addresses this problem by examining how various state-like and trait-like job demands and resources relate to truck drivers' momentary happiness at work. Using an experience sampling study comprising 82 Dutch truck drivers, truck drivers were found to be happier during off-job activities and non-work-related job activities, such as breaks, than during work-related job activities. Furthermore, this study shows that road congestion aggravates the inverse relationship between work-related job activities and momentary happiness. Social support of colleagues and flexible work hours alleviate this relationship. These findings provide valuable information to the industry about the road to happiness for truck drivers. [ABSTRACT FROM AUTHOR]

Introduction : Safety research in the U.S. motor carrier context remains important, as the trucking industry employs approximately 1.7 million large truck drivers. Drivers face many competing pressures in this unique high risk, high regulation, and low direct supervision context. They represent the cornerstone of safe carrier operations. Methods : Using a multi-theoretical approach, this study investigates how drivers' perceptions of carrier safety climate influence their safety-related attitudes and intentions. Results : Responses from nearly 1500 over the road drivers provide evidence that safety climate directly influences drivers' attitudes toward safety, safety norms, and driver risk avoidance, and indirectly influences drivers' intentions to commit unsafe acts. These findings replicate previous findings and also extend the nomological network of theory in this context, adding driver risk avoidance as a central factor to the driver safety theoretical framework. Additionally, carrier managers are encouraged to reflect on the study's evidence and pursue a better understanding of their drivers' risk perceptions and tolerance, while minimizing avoidable risk through prudent safety and operational policies, procedures, and processes. Future research in this area is highly encouraged. • Safety attitudes, norms, control, and risk avoidance influence large truck drivers' intentions to commit unsafe acts. • Safety climate directly influences drivers' safety attitudes, norms, and propensity to avoid risk. • Safety climate indirectly influences drivers' intentions to commit unsafe acts. • The likelihood drivers will commit unsafe acts in the future increases as they progress through their career stages. [ABSTRACT FROM AUTHOR]

• Motion based driving simulators can be a complement to heavy vehicle dynamic testing. • Studied simulator provides satisfactory feedback to the driver for vehicle assessment. • Strong correlation exists between driver evaluation in simulator & objective measures. • Performance measures are better than dimensions for assessing heavy vehicles safety. This article investigates the potential of a motion based driving simulator in assessing and comparing dynamic performance of different heavy vehicles. A driving simulator study with 55 professional truck drivers is performed and the achieved results show a strong correlation between the objective and subjective measures of the different vehicles performance. The motion based driving simulator is used to compare the performance and controllability of high capacity transport vehicles with conventional heavy vehicles. [ABSTRACT FROM AUTHOR]

• Factors affecting truck drivers' decision to report using a cell phone while driving are determined. • Random parameters logit model is applied to account for unobserved heterogeneity. • Factors related to fatigue and truck parking are found to affect the decision to use a cell phone while driving. • Safety training should be modified to aid in reducing cell phone use while driving. • Potential solutions regarding cell phone use while driving are provided. Distracted driving continues to pose threats to transportation safety as it impairs driver performance and increases crash risk. In recent years, cell phone use while driving has become the primary research interest regarding distracted driving. However, the majority of this research has focused on the prevalence and risks of such behavior in passenger car drivers and few have investigated its effect on the performance of drivers of large trucks. Due to the inherent job responsibilities, truck drivers are more susceptible to use a cell phone, or other communication devices (e.g., CB radio), while driving to coordinate delivery logistics. The purpose of this study is to further understand distracted driving in the context of large trucks by identifying the factors that contribute to large truck drivers' decision to report using a cell phone while operating a commercial motor vehicle. Through survey data collected in 2017 from drivers of large trucks who either pick-up or deliver goods in the Pacific Northwest (Oregon, Washington, Idaho, British Columbia), a random parameters binary logit model is used to identify these factors. Of the 515 respondents, 234 (45%) indicated that they use a cell phone while driving. Through the random parameters binary logit model, unobserved heterogeneity is captured, and specific driver behaviors, demographic, work, temporal, and management characteristics are found to affect the likelihood of truck drivers reporting to use their cell phone while driving. Of particular interest, are carrier management characteristics and safety training. Carriers who manage fatigue by imposing schedules to make it easier to take breaks result in a decrease in probability of drivers reporting cell phone use, while carriers who restrict the number of hours worked decreased the probability of reporting cell phone use for the majority of drivers. In addition, having participated in road safety driving resulted in a decrease in probability of reporting cell phone use for the majority of drivers. Such findings have the potential to aid government agencies and commercial motor vehicle carriers in understanding the factors influencing cell phone use while driving among truck drivers. Understanding these motives can aid in the development of programs and policy initiatives that are intended to mitigate distracted driving among truck drivers. [ABSTRACT FROM AUTHOR]

• The old trolley problem has received a new dimension in autonomous vehicles. • We examined truck drivers' willingness to save the opposite hypothetical driver. • The willingness to ditch own vehicle was higher in the falling asleep than in the suicide scenario. • The higher the empathy score, the higher the probability of ditching a vehicle. • Implications for autonomous vehicle programming are discussed. Unlike hypothetical trolley problem studies and an ongoing ethical dilemma with autonomous vehicles, road users can face similar ethical dilemmas in real life. Swerving a heavy vehicle towards the road-side in order to avoid a head-on crash with a much lighter passenger car is often the only option available which could save lives. However, running off-road increases the probability of a roll-over and endangers the life of the heavy vehicle driver. We have created an experimental survey study in which heavy vehicle drivers randomly received one of two possible scenarios. We found that respondents were more likely to report they would ditch their vehicle in order to save the hypothetical driver who fell asleep than to save the driver who deliberately diverted their car towards the participant's heavy vehicle. Additionally, the higher the empathy score, the higher the probability of ditching a vehicle. Implications for autonomous vehicle programming are discussed. [ABSTRACT FROM AUTHOR]

• Takeover times of truck drivers very quick (M = 1.35 s) compared to car drivers. • Behavioral differences in gaze behavior has no effect on takeover times. • Predictability of takeover situations influences takeover times. • Learning behavior in takeover reactions is observed. The desire to enable conditional automated driving (CAD) in the near future, entails the challenge to manage drivers' safe transitions from automation back to manual control. Several factors have been considered in recent years in the passenger car context, while the truck has largely been disregarded. For the first time take-over behavior of heavy-duty truck drivers in time critical take-overs is considered in CAD research. This study analyzes the effect of non-driving related tasks, CAD duration, take-over situations and number of take-overs on reaction times of truck drivers. Gaze behavior was tracked with a remote eye-tracker; reaction times and driver interaction during CAD drives was recorded and analyzed. Two different non-driving related tasks were presented in nine unique take-over situations, while also controlling for the duration of CAD. Contrary to assumption, no influence of non-driving related tasks or CAD duration on reaction times is found. Notably, different reaction times are recorded due to the nine unique take-over situations. Finally, it is shown that our take-over times decrease over the course of the experiment and are far lower than other published reaction times (M = 1.35 s) in the passenger car context. The findings are discussed and implications with regard to other published studies are drawn. [ABSTRACT FROM AUTHOR]

• The limit of predictability for the whereabouts of truckers is measured by entropy theory. • We show that the whereabouts of truckers is more difficult to predict than that of an average person. • The characteristics of the profession negatively impact the predictability of mobility patterns. • Predictability of truckers' whereabouts may be insensitive to spatial resolution. This study examines the predictability of a truckload trucker's whereabouts, using a GPS trajectory data set from about 1000 active users of an on-line freight exchange (OFEX) platform. To describe a trucker's whereabouts, two different location mapping schemes are proposed. The first divides the entire study area into rectangular grids using a simple geographic rescaling method and treats each grid as a unique location. In the second scheme, a location type (LT) is first assigned to each GPS point, using Point Of Interest (POI) information. Then, each GPS trajectory is converted to a trajectory of LT data. We then process both types of trajectory data, grid-based and type-based, to develop various entropy and predictability measures. We find that the whereabouts of truckers possess strong regularity, but are substantially more difficult to predict than that of an average person. Specifically, the predictability of truckers' next location on the grid map peaks at 83%, about 10% percentage point lower than that reported in the literature. Surprisingly, predicting the type of the location that a typical trucker may visit the next is even more difficult, with a predictability around 81%. Findings from this study could help guide the development of algorithms for predicting truckers' next visit location and the type of the location. [ABSTRACT FROM AUTHOR]

• Driver attitude explains the risky driving behavior (RDB) of truck drivers. • Prevailing Social norms are positively associated with the RDB. • Work safety climate has a moderating impact on socio-cognitive factors and RDB. • Work safety climate has a moderating impact on social norms and RDB. • Limits placed by supervisors on risky driving have significant impact on RDB of truck drivers. Trucks involvement in fatal road crashes are considerably high and, literature evidenced that the risky driving behavior of truck drivers is the main cause. Therefore, the study investigates the impacts of socio-cognitive factors (attitude towards risky driving, locus of control and social norms related to driving) and work safety climate in forming the risky driving behavior of truck drivers in Pakistan. Data from 396 truck drivers are collected through a self-administered questionnaire. The results show that driver attitude and prevailing social norms both have a positive significant effect on risky driving behavior, while the work safety climate has a negative impact on risky driving behavior. The study distinctly defines the role of "work safety climate" in the context of the culture prevailing among truck drivers in Pakistan, and looks additionally at the moderating effects on socio-cognitive factors and risky driving behavior. Open communication and counseling by supervisors, their role modeling and placing of limits on risky driving behavior in fact have negative effects on the self-reported risky driving behavior of truck drivers. The study reveals that five of the nine factors have significant moderating effects on risky driving behaviors. The results also imply that improvements in the "work safety climate" of the trucking industry would help to control the socio-cognitive factors and risky driving behaviors, and may improve the road safety conditions in Pakistan. [ABSTRACT FROM AUTHOR]

• The study compared risky driving behaviours of professional taxi and truck drivers. • The DBQ segmented into four dimensions both among taxi and truck drivers. • Strong measurement invariance was supported in the DBQ factor structure of samples. • Taxi drivers were more likely to commit errors and violations than truck drivers. • The odds of violations on crash involvement was higher for taxi drivers than truck drivers. Professional drivers are drivers whose profession is to drive a vehicle such as truck and taxi for working purposes. However, these drivers constitute a heterogeneous group and generalizing assumptions about risky driving behaviour across the entire group might be misleading. The current study aimed to investigate similarities and differences of self-reported risky driving behaviour and crash involvement among different groups of professional drivers. Two rather large samples of taxi drivers obtained from 20 taxi stations in two cities (n = 381) and heavy goods vehicles (truck) drivers obtained from a roadside survey in 10 provinces (n = 785) completed the same 27-item Driver Behaviour Questionnaire (DBQ) in Iran. Principal component analysis showed that the DBQ segmented into four dimensions both among taxi and truck drivers. Further, a multi-group confirmatory factor analysis (MGCFA) supported strong measurement invariance in the DBQ factor structure across the two samples. The results showed that taxi drivers were more likely than truck drivers to commit errors as well as ordinary and aggressive violations. A one unit increase in ordinary and aggressive violations increased the probability of having experienced a traffic crash in the last year by 69% and 98% for taxi drivers, respectively, and 37% and 42% among truck drivers, respectively. This highlights that driving violations increased the probability of crash involvement almost twice as much among taxi drivers compared to truck drivers. Policymakers could target ordinary and aggressive violations by establishing better driving training and by improving the licensing procedures among professional drivers. [ABSTRACT FROM AUTHOR]

Cranking the landing gear is a common task performed by truck drivers to raise or lower trailers. This task poses a risk to the shoulder joint due to the required forceful exertion and the posture constrained to the hand-handle interface. As a potential occupational risk, there has been no definitive guideline for best practices among truck drivers. An operator can crank perpendicular (frontal) or parallel (sagittal) to the crank rotation. In this laboratory study, the effects of cranking method and resistance on scapular range of motion and shoulder muscle activity were observed in 12 participants. Scapular posture was measured using an optical motion tracking system. EMG was monitored on 16 muscles contributing to shoulder movement. The results show that during frontal cranking, the scapular range of protraction was 28 ± 11.6°, which was more than the sagittal cranking (23 ± 10.4°), indicating a decreased subacromial space and elevated shoulder impingement risk. Seven muscles (all three deltoid muscles, middle trapezius, supraspinatus, infraspinatus, and teres minor) demonstrated that when the crank resistance was low, the front cranking method resulted in lower activity than the side cranking. When the crank resistance was 20 Nm, the muscle activity on these seven muscles was greater when cranking from the front than from the side. Based on these observations, we suggest that when the resistance is low (lowering the trailer) the driver should stand facing the trailer. On the contrary, it is advantageous to stand parallel to the trailer and crank while raising the trailer to apply the full body strength to reduce the shoulder load. [ABSTRACT FROM AUTHOR]