Heavy Vehicle Drivers Research Articles

Assessment of occupational musculoskeletal disorders (MSDs) among heavy vehicle drivers.

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

Prevalence of Upper Cross Syndrome Among Heavy Vehicle Drivers (HVD): A Cross-Sectional Study

Background: Heavy vehicle drivers (HVDs) are at risk for musculoskeletal disorders, including Upper Cross Syndrome (UCS), due to prolonged sitting and repetitive tasks. UCS is characterized by muscle imbalances in the neck, shoulders, and upper back, leading to pain and functional impairment.Objective: This study aimed to assess the prevalence of UCS among HVDs and identify contributing factors such as working hours and pain intensity.Methods: A cross-sectional study was conducted from March 2024 to August 2024, involving 212 male HVDs aged 25-55 years, working for over a year, and experiencing neck and back pain for more than three months. Exclusion criteria included recent spinal trauma or deformities. Data were collected using the Numeric Pain Rating Scale (NPRS) and Neck Disability Index (NDI) and analyzed using SPSS version 25. Chi-square tests and linear regression were applied.Results: UCS was prevalent in 26.4% of drivers. A significant relationship was found between pain intensity and disability (χ² = 170.126, p = 0.000). Most drivers (66.9%) were aged 25-35 years, and 30.2% reported very mild pain.Conclusion: UCS is significantly prevalent among HVDs, with long working hours and poor ergonomics contributing to its onset.

Stress and Sleep Deprivation Experienced by Bus Drivers of Government Bus Depots in an Urban Area of Chengalpattu District in South India.

Background Driving a bus is far more stressful than other professions. Bus drivers also take more sick leave than other workers because of the physical and mental demands of their employment. Bus drivers are especially vulnerable because of their intense work environment, poor sleep, and poor food pattern. The aim of the study was to assess stress and sleep deprivation of the government bus drivers in Chengalpattu District in Tamil Nadu and to identify the sociodemographic factors influencing them. Methodology An analytical cross-sectional study was conducted among 429 government bus drivers working under Tamil Nadu State Transport Corporation (TNSTC )in selected government bus depots in the urban area of Chengalpattu District in Tamil Nadu, India. The principal investigator collected the data by using a semi-structured questionnaire which comprises six parts, i.e., sociodemographic variables, job-related factors, comorbidity, habits, stress assessed by using the Perceived Stress Scale (PSS-10), and sleep deprivation assessed by using the Epworth Sleepiness Scale (ESS). Results In the present study, the mean age of participants was 47.683 years. Hindus comprise 95.3% of the sample, and 96.7% are married. Approximately 47.6% of the drivers have achieved a higher secondary education. The average time spent working as a heavy vehicle driver was 20.4 years. About 37.1% (159 individuals) have hypertension, making it the most prevalent comorbidity. In this study, 47.3% (203 participants) reported very poor sleep, 35.7% (153 participants) reported average sleep, and 17.0% (73 participants) reported good sleep. Inferential statistics revealed that those drivers who were degree holderstook less than three breaks in duty time, slept over six hours at night, had the habit of smoking and drinking alcohol, and took more than 60 minutes to fall asleep experienced very poor sleep according to the ESS. In this study, 57.1% (245 participants) reported moderate stress levels, 24.2% (104 participants) showed high levels of perceived stress, and 18.6% (80 participants) reported low stress levels. Inferential statistics revealed that those drivers who were Muslims, degree holders, those with primary education, smokers, alcohol consumers, drivers with very poor sleep, and those who took more than six days of casual leave in the past six months experienced high stress according to the PSS-10. Conclusion Implementing effective health management strategies and minimizing work-related stress will lead to a decrease in work-related disorders among drivers. TNSTC should ensure enough lodging facilities for drivers at depots, bus terminals, and outstations. They require a minimum of eight hours of sleep every day to maintain optimal physical well-being.

Reviewing the prevalence of refractive errors and color blindness among commercial drivers

Road traffic crashes pose a significant global public health concern, claiming the lives of approximately 1.19 million people each year. A worrisome trend surfaces as a considerable majority of these fatalities involve vulnerable road users, encompassing not only drivers of heavy and light vehicles but also pedestrians, cyclists, and motorcyclists. Visual impairments play a significant role in these tragic events, further exacerbating the risks faced by road users. Whether due to poor eyesight or other vision-related challenges, impaired visual perception contributes to the vulnerability of individuals on the road. This review aimed to systematically analyze the currently available research and articles to determine the prevalence of color vision defects and refractive errors among commercial drivers. The primary objective was to investigate whether refractive errors and color vision defects pose a risk factor for road safety among commercial drivers. A primary search of databases including PubMed, Google Scholar, and others was conducted using relevant keywords. Articles published between 2010 and 2023 were considered, resulting in 16 cross-sectional studies being included for analysis. Prevalence rates were calculated using weighted averages based on sample sizes. Among commercial drivers, the prevalence of color vision impairment was lower (4.35%) compared to refractive errors (23.42%). Individuals with protanopia were at higher risk of road traffic accidents (RTAs). Complex issues such as alcoholism and visual impairments contributed to higher RTA frequencies in certain populations, emphasizing the multifaceted nature of road safety challenges.

Exploring the impact of right-turn safety measures on E-bike-heavy vehicle conflicts at signalized intersections

A major safety hazard for e-bike riders crossing an intersection is encountering heavy vehicles turning right in the same direction, which often results in severe casualties. Recently, some cities in China have implemented right-turn safety improvement treatments (i.e., right-turn yielding rules and right-turn warning facilities) at intersections to reduce the occurrence of such accidents. However, the risk perception and behavior of e-bike riders and heavy vehicle drivers dynamically change during the right-turn interaction process, and the safety effects of different right-turn safety measures remain unclear. This study aims to investigate the safety effect of right-turn safety measures on E-Bike-Heavy Vehicle (EB-HV) right-turn conflicts at signalized intersections. The right-turn conflicts and potential influencing factors are extracted from aerial video data, including characteristics of right-turn warning facilities, characteristics and behavior of e-bike riders and heavy vehicle drivers, environmental factors, and traffic-related factors. Moreover, traffic conflict indicators such as the Time to Collision (TTC), Post Encroachment Time (PET), and Jerk are selected and calculated. Multinomial and binary logit models are used to estimate and analyze the EB-HV right-turn conflict severity and drivers yielding behavior. The results reveal that: (a) right-turn warning facilities can decrease the probability of slight and severe EB-HV right-turn conflicts, while the presence of law enforcement cameras could prompt heavy vehicle drivers to comply with the yielding rules and adopt more cautious behavior; (b) increased heavy vehicle speed and acceleration before turning right have strong correlation to illegitimate yielding behavior of the driver and higher EB-HV right-turn conflict severity; and (c) aggressive behavior of e-bike rider increases the severe conflict probability, especially at intersections without right-turn warning facilities. Based on the study findings, several practical implications are suggested to reduce the risk of EB-HV right-turn conflicts, enhance the effectiveness of right-turn safety measures, and improve crossing safety for e-bike riders.

Heavy vehicle driver fatigue: Observing work and rest behaviours of truck drivers in Australia

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.

Woozy Driving Detection

Abstract: Abstract-wooziness has been identified as one of the primary causes of injury or death in car accidents. As a result, it is proposed in this research work to develop an adaptive heavy vehicle driver fatigue and alertness modelbased on spectral bands by combining signal processing algorithms with soft computing techniques such as the neuro-fuzzy algorithm to estimate the driver cognitive state while driving a vehicle in a virtual reality (vr)-based dynamic simulator under monotonous driving conditions.As a result, it is proposed in this study to use soft computing approaches to reduce the amount of features and nonlinear supervised classification algorithms to classify them. The suggested adaptive model distinguishes between the driver's level of exhaustion by analysing brain responses to determine whether the driver is fatigued owing to task- induced variables or attitude/behaviour, and then the amount of fatigue is associated to sleepiness (i.e. Level of alertness towards driving). The adaptive model can also be used to warn drivers and regulators about impending catastrophes by optimizing the features of interface systems. The suggested system notifies the driver when he or she is fatigued or drowsy based on the identification of cognitive state and calculates the fatigue index and alertness level. The proposed method also assists the driverin being more alert and intuitive in order to avoid deadly road accidents.

Construct validity and factor structure of Sinhalese version of Epworth Sleepiness Scale

Objective: This study is the first to validate the Epworth Sleepiness Scale (ESS) in Sri Lanka population. ESS is a tool used to evaluate patients’ habitual sleepiness during the day and is used worldwide. This work examined the psychometric properties of the Sinhalese version of eight items ESS among heavy vehicle drivers in Sri Lanka.Methods: A sample of 403 participants was randomly split into two to evaluate dimensionality by exploratory factor analyses (EFA) and confirmatory factor analyses(CFA).Results: Study findings suggest that the single version of ESS is a reliable and valid instrument for sleep evaluation. The analysis identified two-factor structures for the Sinhalese version of ESS. Factor one included sitting and talking, watching TV, car in traffic, sitting in public and sitting and reading, summarized as sitting and doing something. Factor two included lying down, being a car passenger, and sitting quietly, indicating sitting without engaging in any particular activity.Conclusions: Sinhalese version of the ESS is a bi-dimensional instrument that is reliable and valid for assessing daytime Sleepiness in Sri Lankan heavy vehicle drivers. However, the Sinhalese version of the ESS needs further evaluation among the general population and those with sleep disturbances.

Understanding overdriving: Human factors considerations in heavy vehicle headlight visibility and stopping distance

Nighttime road fatalities are a significant safety concern in the U.S., especially those involving commercial vehicles. Often an explanation offered involves overdriving headlights, or driving at a speed that does not allow for safe response within the range of the headlights’ visibility. The scientific literature was reviewed to assess the real-world applicability of guidance commonly provided in Commercial Driver’s License (CDL) manuals that advises heavy vehicle drivers to drive at a speed that allows them to be able to stop within the range of the vehicle’s headlights. We conclude that this guidance provided by CDL does not account for the variety of roadway types, driver expectations, and travel speeds, nor a scientific understanding about visual perception and hazard avoidance. It is recommended that further work be conducted to examine real-world application of commercial vehicle driver capabilities and variable roadway types to better inform guidance and recommendations.

A preliminary study on the barriers and facilitators to improving the health, safety, and well-being of aging heavy vehicle drivers

Introduction. Managers from road freight transportation organizations were interviewed on barriers and facilitators to implementation of occupational health, safety, and well-being interventions for aging heavy vehicle drivers. As aging drivers are more likely to be seriously injured or die in a work-related incident than younger drivers, it is important to recognize strengths and weaknesses throughout the system to identify intervention that addresses their specific needs. Method: A Systems Theoretic Accident Model and Processes (STAMP) control structure was constructed to chart the controllers, controls, and feedback channels in the system to identify gaps in health, safety, and well-being intervention in the system. The STAMP control structure also charted the barriers and facilitators within levels across the system. Eleven managers were recruited into the study representing a range of road freight transportation organizations throughout Australia. Results: Interview data revealed that barriers and facilitators existed at most levels of the system. Facilitators included advice from external agencies, support from upper management, modern technology, and regular social communication with drivers. Barriers were a lack of guidance on aging issues, operational conflicts with health and safety objectives, and the drivers’ fear of disclosing health information associated with their driving role. In regards to formalized intervention in place to support aging heavy vehicle drivers, the system is reliant on fitness to drive medical assessments based on age and jurisdiction. Conclusions: As there was generally a lack of senior direction cited from the upper levels of the system on aging issues, there was much variation across the study on how aging risks are managed in the workplace for heavy vehicle drivers. Practical Applications: This study recommends that managers across the road freight transportation industry receive formalized aging-awareness health and safety training in how to manage work-related driving hazards for aging heavy vehicle drivers.

Fastag Fraud Detection: A Literature Survey

Abstract: National Payments Corporation of India (NPCI) has developed the National Electronic Toll Collection (NETC) program to meet the electronic tolling requirements of the Indian market.The use of FASTag is prevalent in modern society. However, it is clear that the number of FASTag frauds in the global integration and existing protection system is constantly increasing. This is why the issue of FASTag fraud detection is very important. Various FASTag frauds occur and there is no means to detect or prevent them. Among the many frauds, we will try to reduce the fraud where drivers of heavy vehicles issue and use FASTags that are intended for light vehicles. In this way, drivers of heavy vehicles are exempted from paying the higher tolls that are set for heavy vehicles. FASTag fraud detection is used to solve this problem. The system will be able to identify the type of vehicle and the FASTag amount paid. Using this system, FASTag fraud detects if any anomaly has occurred and alerts the system accordingly

Ergonomic Analysis of Factors Causing Musculoskeletal Disorders (MSD) in Heavy Vehicle Drivers

Driving comfort is important for any class of vehicle drivers. Seating comfort is very important for drivers who are undergoing extended period of driving and its associated side effects include higher risk of back problems, discomfort in neck, shoulder and thighs. A good sitting position and correct posture are important for drivers to reduce various work related injuries. There are several occupational risk factors that may affect the work performance of heavy vehicle drivers that may lead to health issues. One of these issues is the driver’s exposure to Musculoskeletal Disorders (MSD). MSDs are injuries and disorders that affect the human body's movement or musculoskeletal system. This is due to the fact that heavy vehicle drivers are often exposed to vibration, prolonged sitting and other postural factors. Some are related to the actual driving task, traffic situation, individual variability and cabin layout. The musculoskeletal disorders are mostly associated with work postures and movements, repetitiveness and pace of work, force of movement, vibration, temperature, workplace layout and monotonous tasks. And these mentioned risk factors may affect not only the health of the heavy vehicle drivers but may also cause related road accidents. In order to determine the occupational risk factors, a study is required to identify the current condition of heavy vehicle drivers in relation to major risk factors. Furthermore, this study aims to determine the significant factors affecting the occurrence of musculoskeletal disorders using statistical analyses.

How Do Vehicles Make Decisions during Implementation Period of Discretionary Lane Change? A Data-Driven Research

To investigate and compare the lane changing behavior of passenger cars and heavy vehicles during the implementation period (defined as the interval from the start time to the end time of a lane change maneuver), this study applies the gradient boosting decision tree (GBDT) method to model the lane changing behavior of heavy vehicles and passenger cars, respectively. Results show that the lane change models vary with the vehicle types and lane change directions. Different factors are considered by the drivers of passenger cars and heavy vehicles when implementing lane changes to different directions. Partial dependence plots of GBDT models reveal that the influence of independent variables on lane changing behavior is nonlinear and complicated, which means that the same variable leads to various effects on the lane change decision across different vehicle types and lane change directions. In contrast with other state-of-the-art methods, the proposed method can obtain more accurate results. The findings indicate that it is necessary to build specific lane change models based on vehicle types and lane change directions for microscopic traffic simulators and autonomous vehicles.

Safety Culture among Transport Companies in Ethiopia: Are They Ready for Emerging Fleet Technologies?

The safety culture and safety climate of transport companies have a significant impact on fleet safety outcomes. Ample research shows that transport companies with a strong safety culture also show lower crash statistics. In spite of modern technologies that help with having a safer fleet, it is difficult to achieve a safer fleet without a proactive safety culture and climate. In Ethiopia, it is assumed that most transport companies have failed to create a distinguishable safety climate in their fleet safety administration and that their heavy vehicle drivers have a poor safety culture. These could be important factors contributing to a higher rate of road traffic crashes involving heavy vehicles. This study aims to assess the existing safety culture among a sample of transport companies in Ethiopia and identify suitable intervention methods to improve the safety culture. Moreover, the study sought to identify the readiness of the transport companies to apply modern technology in their fleets by examining their safety culture and safety climate. In total, 10 fleet managers and 174 heavy vehicle drivers participated in the fleet safety audit survey. A descriptive analysis and a detailed fleet safety audit score were calculated. Based on the scale scoring, ten companies score below best practices, one scores well below best practices, and only one company meets the criteria to be considered achieving best practices. The results from this study show that the safety culture and safety climate in most transport companies are quite limited. In addition, most transport companies implement similar safety measures, including inconsistent driver training and annual maintenance.

An Approach to Detect Driver Drowsiness in Real Time using Facial Landmarks

According to studies, 21% i.e., one-fourth of serious roadway accidents are caused due to the drowsiness of the drivers. Some of the most significant reasons that cause fatigueness in drivers are lack of sleep, heavy exertion and drunken driving. Heavy vehicle drivers who travel long distances despite their lack of sleep are more prone to road accidents. This research proposes a system that detects the drowsiness of the drivers like duration of eye closure and yawning. This system is built using Python and its libraries, such as OpenCV, dlib, pygame, etc. which would be integrated into a web application made with the help of flask in Python. This system in real-time determines if a person driving a car is drowsy or not, if the driver is drowsy, then the system alarms him by sound alerts.

Association of Hearing Health with Traffic Accidents among Heavy Vehicle Drivers

Aim: Excessive exposure of drivers to traffic noise and the resulting hearing loss could affect safe driving. This study aimed to investigate the hearing status of heavy vehicle drivers and its correlation with traffic accidents. Materials and Methods: This cross-sectional study was conducted on 187 drivers of heavy vehicles (trucks and buses) in Tehran province during 2018. The hearing status of drivers was investigated by the audiometric test method, ISO 6189 (1983). A safety questionnaire and checklist of drivers' demographic variables were used to gather information on accident status and personal characteristics of drivers. SPSS version 24 software was used for the statistical analysis of data. The statistical tests used are Chi-square, Spearman's correlation, paired t-test, and odds ratio. Results: Examining the hearing status of drivers showed that 51.5% were normal and 48.5% of drivers had hearing loss. The highest mean hearing threshold was at a frequency of 4000 Hz. The mean (standard deviation) for the right and left ears was 27.82 ± 15.92 and 18 ± 27.32 dB, respectively. The results of the analysis on drivers' hearing status and accidents showed a significant correlation between the frequency of accidents and the classification of drivers' hearing loss. Thus, a significant relationship with the correlation coefficient of 0.123 and 0.307, respectively, was obtained between the average hearing loss of drivers and having one or five accidents. Conclusion: The findings of the study showed hearing loss in drivers in the left ear, especially at high frequencies. There is also a significant relationship between the number of accidents and the rate of hearing loss of drivers. Hearing loss caused by noise interferes with activities such as driving and negatively affects the safety of drivers and emphasizes the importance of preventing accidents by controlling traffic noise.

Oral Precancer and Cancer Awareness Among Indian Long Distance Heavy Vehicle Drivers – A Marginalized Population

Oral Precancer and Cancer Awareness Among Indian Long Distance Heavy Vehicle Drivers – A Marginalized Population

Investigating the Relation between Drivers Working and Resting Hours with the Occurrence of Road Accidents (Case Study: Heavy Vehicle Drivers)

Investigating the Relation between Drivers Working and Resting Hours with the Occurrence of Road Accidents (Case Study: Heavy Vehicle Drivers)

Examining Coroners’ Recommendations for Health and Safety Management of Ageing Heavy Vehicle Drivers: A STAMP Analysis

Recommendations were analysed from coronial cases involving ageing heavy vehicle drivers (≥55 years) and mapped onto a Systems Theoretic Accident Model and Processes (STAMP) control structure to identify the controllers and control actions influential in the heavy vehicle industry with regard to health and safety. A National Coronial Information System (NCIS) database search revealed 38 coroners' recommendations arising from 14 unique cases of ageing driver involvement. There were no ageing themes identified in the analysis of coroners' findings and recommendations. An examination of the STAMP control structure identified that the highest concentration of recommendations was in the level of regulation, the second most senior level of control, although safety constraints were advised for all five levels of the system. In regard to identifying themes of control flaws in the recommendations, the study found that "unidentified hazards" were the most common type of safety failure in the analysis of cases of ageing drivers, concentrated at the regulatory level, which indicates that additional risk identification methods by upper levels of control are needed. Therefore, a recommendation arising from the current study is that additional controls in safety intervention are necessitated in the upper and middle levels of the road freight transportation system; in particular, formalising health and safety education for organisational managers, with a focus on identifying ageing issues, would fill a gap in the system for managing ageing heavy vehicle drivers. In conclusion, this study has found that improving the health and safety of ageing heavy vehicle drivers necessitates additional safety constraints with a focus on formalised safety education for organisational managers, in addition to a means to detect emerging and unforeseen hazards in the road freight transportation industry.

Identifying Risk Factors for Autos and Trucks on Highway-Railroad Grade Crossings Based on Mixed Logit Model.

This study aimed to determine different influencing factors associated with the injury outcomes of heavy vehicle and automobile drivers at highway-rail grade crossings (HRGCs). A mixed logit model was adopted using the Federal Railroad Administration (FRA) dataset (n = 194,385 for 2011-2020). The results show that drivers' injury severities at HRGCs are enormously different between automobile and truck/truck-trailer drivers. It was found that vehicle speed and train speed significantly affect the injury severity in automobile and truck drivers. Driver characteristics such as gender and driver actions significantly impact the injury severity in automobile drivers, while HRGC attributes such as open space, rural areas, and type of warning device become significant factors in truck models. This study gives us a better understanding of the differences in the types of determinants between automobiles and trucks and their implications on differentiated policies for car and truck drivers.