Drivers Of Large Trucks Research Articles

Unravelling route choices of large trucks using trajectory clustering and conditional Logit models

The mobility of sizable trucks is often limited by their large size. They thus may have additional requirements on road types, road widths, and the turning radius at the intersection when travelling. Therefore, this study explores the unique needs and preferences of large truck drivers’ route choice with a focus on trip and road network characteristics. GPS trajectory data from the central Kansai area of Japan with numerous ports and freight terminals are used. Trajectories are considered having the same origin (destination) if their starting (ending) coordinates are in the same 500 m × 500 m mesh. For the trajectories of the same pair of origin–destination (OD) meshes, several route clusters are obtained based on geographical configuration using a QuickBundles algorithm. Sampling techniques are employed to equalize the number of input points for each vehicle trajectory and the optimal number of clusters is determined automatically by our algorithm based on the Silhouette Coefficient. By taking the clusters as route choice options for an OD pair, a Conditional Logit Model is used to identify the factors that influence the route choice considering both vehicle- and trip-specific attributes. The results quantify the preference of trucks for wider roads and toll routes, as well as aversion to long distances and turns. Heterogeneity in route choice based on vehicle type, trip time (date), and trip purpose is also evident. The findings of this study can provide insights for freight road network design and optimization.

Severity analysis of crashes involving in-state and out-of-state large truck drivers in Alabama: A random parameter multinomial logit model with heterogeneity in means and variances

The trucking sector contributes significantly to the economic vitality of the United States. Large trucks are primarily used for transporting goods within and across states. Despite its economic importance, large truck crashes constitute public safety concerns. To minimize the consequences, there is a need to understand the factors that contribute to the severity outcomes of truck-involved crashes. Since many large truck drivers transport goods across several states, the driver-centered crash factors are expected to differ between in-state and out-of-state drivers. For this reason, this study developed two random parameters multinomial logit models with heterogeneity in means and variances to examine the factors contributing to the severity of crashes involving in-state and out-of-state large truck drivers in Alabama. The study was based on the 2016–2020 large truck crashes in Alabama. After data cleaning and preparation, it was observed that approximately 20% of in-state and 23% of out-of-state large truck crashes were fatigue-related. There were more speeding related crashes (12.4%) among in-state large truck drivers, but the contribution of speeding to crash severity outcomes was only significant in the out-of-state model. More crashes related to red light running violation (14.2%) were observed among out-of-state drivers, pointing to the fundamental issues of fatigue and unfamiliarity with the operations of signalized intersections in Alabama. The study contributes to the literature on large truck crashes by uncovering the nuances in crashes involving in-state and out-of-state large truck drivers. Despite the seeming similarity in factors that influence crash outcomes, this study provides the basis for truck drivers’ training and communication campaigns on the differences that may exist in roadway characteristics from state to state. Also, policy formulations and strategies that prioritizes the well-being of the large truck drivers and creates a better working condition for them should be explored.

An empirical analysis of driver injury severities in work-zone and non-work-zone crashes involving single-vehicle large trucks

Objectives Florida ranks among the states with the highest rates of work-zone crashes involving large trucks. With significant emphasis in Florida’s strategic highway safety plan, understanding work-zone crashes involving large trucks and resulting injury severities is critically important. This study investigated the contributing factors influencing the driver injury severity of single-large-truck crashes in work zones, benchmarked against non-work zones in Florida. Methods Using work-zone and non-work-zone crash data from 2011 to 2019 (inclusive), driver-injury severities in single-large trucks crashes were studied using random parameters logit models that allow for possible heterogeneity in the means and variances of parameter estimates. The available data included a wide variety of factors known to influence driver injury severity, including spatial and temporal; vehicle and traffic; roadway, harmful events, and driver characteristics. Results The model estimates produced fundamental shift in unobserved heterogeneity for work-zone and non-work-zone crashes involving single large trucks. More importantly, the likelihood of large truck drivers’ injury severity is about fourteen-times higher on rural and six-times higher on urban interstate highways and 1.3 times lower with 10 miles per hour below the posted speed limit for large trucks inside work zones relative to non-work zones. The model results also indicate that the likelihood of severe driver injury is higher for heavy truck (more than 26000 pounds), a lane-shift work-zone configuration, and careless driving in work-zone crashes involving single large trucks. Conclusions The model findings add valuable insights to have profound effects in the safety performance of large trucks and in-vehicle safety technologies, such as, Advanced Driver Assistance Systems, for careful driving along the work-zone segments with lower speed, leading to Automated Driving Systems. These measures include various policy-related safety countermeasures including revisiting traffic control plan for lane-shift on highways specifically for large trucks and developing training modules for Florida registered truck drivers.

Investigating factors affecting severity of large truck-involved crashes: Comparison of the SVM and random parameter logit model

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.

Comparison of contributing factors for injury severity of large truck drivers in run-off-road crashes on rural and urban roadways: Accounting for unobserved heterogeneity

Abstract In spite of numerous efforts to quantitatively identify the factors contributing to the injury severity of different crash types in rural and urban settings, the distinction between rural and urban areas regarding the injury severity of run-off-road (ROR) crashes involving large trucks is still not clearly understood. As such, the objective of this study is to investigate the effect of area type (i.e., urban vs. rural) on injury severity outcomes sustained by drivers in ROR crashes involving large trucks while accounting for unobserved heterogeneity. To do this, the latent class ordered probit models with two classes are developed. The crash data pertaining to ROR crashes involving large trucks in Oregon between 2007 and 2014 were utilized. The estimation results reveal that the developed latent class ordered probit models (for urban and rural areas) are substantially distinct in terms of the contributing factors affect urban and rural ROR crash severities. The results indicate that female drivers and speed limit of 55 mph were associated with moderate injuries (non-incapacitating) in rural roadway ROR crashes while no injury outcome is most likely for crashes occurred in urban roadways with raised medians and on areas with a population density between 10,001 and 25,000. Also, the findings show that some factors increase the risk propensity of sustaining higher injury levels regardless of the land use setting such as crashes on horizontal curves, not wearing seatbelt, and driver fatigue. The findings of this study could benefit trucking industry, transportation agencies, and safety practitioners to prevent or alleviate the injury severity of ROR crashes involving large trucks by developing appropriate and cost-effective countermeasures.

Understanding truck driver behavior with respect to cell phone use and vehicle operation

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.

Analytical observational study of nonfatal motor vehicle collisions and incidents in a light-vehicle sales and service fleet

Motor vehicle crashes (MVCs) are a significant cause of lost-workday injuries, and consistently the leading cause of work-related fatalities in the United States for all industries combined. Prevention research has focused mainly on collisions fatal to the drivers of large trucks. This analytical observational study addresses gaps in the literature by: conducting a descriptive analysis of motor vehicle claim events involving light-vehicle drivers in a large health care industry fleet; identifying risk factors for work-related MVCs and injuries based on vehicle miles traveled; and providing details on circumstances of these events.The study examined 8068 motor vehicle events resulting in vehicle damage, property damage, or injury reported by 6680 U.S.-based drivers in a light-vehicle sales and service fleet operated by a health care company over a 4 ½-year period (January 2010 through June 2014). Thirty-three percent (n = 2660) of the events were collisions. Collisions were segmented as recoverable or non-recoverable according to whether the company could recover costs from another party, and mileage-based collision and injury rates were calculated by gender, age, tenure, and vehicle type. Differences in collision and injury rates between groups of interest (for example, tenure and age categories) were assessed with Poisson regression techniques adjusted using generalized estimating equations (GEE) for repeated observations on the same employee over time.Age, gender, and job tenure were significant collision risk factors, and risk patterns for recoverable and non-recoverable collisions were similar to those for total collisions. Collisions per million miles (CPMM) were significantly higher for drivers 21–24.9 years of age compared to drivers age 25–54.9 years (9.58 CPMM vs 4.96 CPMM, p = .025), drivers employed for less than 2 years compared to those employed 2 or more years (6.22 CPMM vs 4.82 CPMM, p < .001), for female drivers compared to male drivers (6.37 CPMM vs 4.16 CPMM, p < .001), and for drivers of passenger cars compared to all other vehicles (5.27 CPMM vs 4.48 CPMM, p < .001). Among collisions between the employee’s vehicle and another vehicle in transport, those where the front of one vehicle hit another vehicle at an angle were the most likely to result in injury to the employee driver or another party (26%), followed by rear-end collisions (25%). Special attention should be given to preventing collisions among newly-hired employees, and to preventing angle and rear-end collisions, which were the most common types of collisions and also were most likely to result in injury than all other collisions combined.

Identifying Fatality Risk Factors for the Commercial Vehicle Driver Population

Commercial/large-truck fatal crash involvement by drivers of different age groups is a critical issue for the trucking industry. Escalating safety concerns related to these heavy vehicles serving the freight economy in the U.S. have an impact national freight reliability and economic growth. This study identifies major contributing factors leading to large-truck fatal crashes for four age groups of driver: &lt;30, 30–49, 50–65, and 65+. The analysis in this study is based on five years (2012–2016) of Fatality Analysis Reporting System data and provides an overall picture of risk factors in large-truck fatal crashes. In total, 30 variables were found to be significant in the logit models, indicating varying risks associated with large-truck drivers of these four age groups. Model results indicate different risk factors associated with driver characteristics, spatial and temporal characteristics, vehicle and vehicle maneuvering characteristics, and environmental conditions at the time of the crashes. Identifying the risk factors for different age groups of drivers is important so proper countermeasures can be implemented from the perspective of human factors (e.g., safe speed choice, fatigue), roadway engineering (e.g., design of roadside barriers, radius of ramps), enforcement (e.g., presence of law enforcement personnel at critical locations), and emergency medical attention in remote areas. Considering the aging of the truck driver population in the U.S. and around the world, the findings of this study are vital to understand better the importance of safety in relation to large-truck fatal crashes.

Risky business: Investigating influences on large truck drivers' safety attitudes and intentions

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.

Classification of driving workload affected by highway alignment conditions based on classification and regression tree algorithm

ABSTRACTObjective: Guaranteeing a safe and comfortable driving workload can contribute to reducing traffic injuries. In order to provide safe and comfortable threshold values, this study attempted to classify driving workload from the aspects of human factors mainly affected by highway geometric conditions and to determine the thresholds of different workload classifications. This article stated a hypothesis that the values of driver workload change within a certain range.Methods: Driving workload scales were stated based on a comprehensive literature review. Through comparative analysis of different psychophysiological measures, heart rate variability (HRV) was chosen as the representative measure for quantifying driving workload by field experiments. Seventy-two participants (36 car drivers and 36 large truck drivers) and 6 highways with different geometric designs were selected to conduct field experiments. A wearable wireless dynamic multiparameter physiological detector (KF-2) was employed to detect physiological data that were simultaneously correlated to the speed changes recorded by a Global Positioning System (GPS) (testing time, driving speeds, running track, and distance). Through performing statistical analyses, including the distribution of HRV during the flat, straight segments and P-P plots of modified HRV, a driving workload calculation model was proposed. Integrating driving workload scales with values, the threshold of each scale of driving workload was determined by classification and regression tree (CART) algorithms.Results: The driving workload calculation model was suitable for driving speeds in the range of 40 to 120 km/h. The experimental data of 72 participants revealed that driving workload had a significant effect on modified HRV, revealing a change in driving speed. When the driving speed was between 100 and 120 km/h, drivers showed an apparent increase in the corresponding modified HRV. The threshold value of the normal driving workload K was between −0.0011 and 0.056 for a car driver and between −0.00086 and 0.067 for a truck driver.Conclusion: Heart rate variability was a direct and effective index for measuring driving workload despite being affected by multiple highway alignment elements. The driving workload model and the thresholds of driving workload classifications can be used to evaluate the quality of highway geometric design. A higher quality of highway geometric design could keep driving workload within a safer and more comfortable range. This study provided insight into reducing traffic injuries from the perspective of disciplinary integration of highway engineering and human factor engineering.

Ticketing aggressive cars and trucks (TACT): How does it work on city streets?

ABSTRACTObjective: The purpose of this study was to determine the feasibility of modifying the Ticking Aggressive Cars and Trucks (TACT) program, originally designed to work on state highways, within a metropolitan area to reduce unsafe interactions and their related crashes between drivers of large trucks and passenger vehicles.Methods: Using crash data, the driving behaviors most commonly associated with large truck and passenger vehicle crashes were identified. A public awareness campaign using media messaging and increased law enforcement was created targeting these associated behaviors. The frequency of these behaviors both before and after the public awareness campaign was determined through observation of traffic at 3 specific locations within the city. Each location had a sufficient volume of large truck and passenger traffic to observe frequent interactions. Pre- and postintervention data were compared using negative binomial regression with generalized estimating equations to evaluate whether the campaign was associated with a reduction in the identified driving behaviors.Results: A comparison between crash data from before, during, and after the campaign and crashes during the same time periods in previous years did not show a significant difference (P =.081). The number of large trucks observed in traffic remained the same during pre- and postintervention periods (P =.625). The rates of negative interactions per 100 large trucks decreased for both large trucks and passenger vehicles after the intervention, with calculated rate ratios of 0.58 (95% confidence interval [CI], 0.48, 0.70) and 0.31 (95% CI, 0.20, 0.47). The greatest reduction was seen in passenger vehicles following too close, with a rate ratio of 0.21 (95% CI, 0.15, 0.30).Conclusions: Although designed for reducing crashes on highways, the TACT program can be an effective approach for improving driver behaviors on city streets.

An empirical analysis of run-off-road injury severity crashes involving large trucks

In recent years, there has been an increasing interest in understanding the contributory factors to run-off-road (ROR) crashes in the US, especially those where large trucks are involved. Although there have been several efforts to understand large-truck crashes, the relationship between crash factors, crash severity, and ROR crashes is not clearly understood. The intent of this research is to develop statistical models that provide additional insight into the effects that various contributory factors related to the person (driver), vehicle, crash, roadway, and environment have on ROR injury severity. An ordered random parameter probit was estimated to predict the likelihood of three injury severity categories using Oregon crash data: severe, minor, and no injury. The modeling approach accounts for unobserved heterogeneity (i.e., unobserved factors). The results showed that five parameter estimates were found to be random and normally distributed, and varied across ROR crash observations. These were factors related to crashes that occurred between January and April, raised median type, loss of control of a vehicle, the indicator variable of speed not involved, and the indicator variable of two vehicles or more involved in the crashes. In contrast, eight variables were found to be fixed across ROR observations. Looking more closely at the fixed parameter results, large-truck drivers who are not licensed in Oregon have a higher probability of experiencing no injury ROR crash outcomes, and human related factor, fatigue, increases the probability of minor injury. The modeling framework presented in this work offers a flexible methodology to analyze ROR crashes involving large trucks while accounting for unobserved heterogeneity. This information can aid safety planners and the trucking industry in identifying appropriate countermeasures to help mitigate the number and severity of large-truck ROR crashes.

Driving Conditions and Occupational Accident Management in Large Truck Collisions

Objective: Objective of this study is to provide characteristics of injury frequency and severity by driving condition in large truck-related traffic collisions. Background: Traffic accidents involving large trucks draw a lot of attention in accident prevention and management policies since they bring about severe human and financial damages. Method: In order to identify the major risk factors of accidents by driving condition, 255 recognized traffic accidents by large truck drivers were analyzed in terms of time of the day, road type, and shape of the road. Results: The driving conditions in the results are represented by the following form of combination, Road Type (Non-expressway or Express) – Shape of Roads (Straight, Curved, Downhill, or Intersection) – Time of Accidents (Day or Night). In the analysis of injury frequency, Non-expressway-Straight-Day condition was the most frequent one. Meanwhile, Expressway-Curved-Day, Non-expressway-Curved-Night and Nonexpressway-Intersection-Night were evaluated as high level in view of injury severity. Also, Expressway-Straight-Night is the driving condition that is the highest in risk among the conditions that have to be managed as grade High. Non-expressway-Straight-Night, Non-expressway-Downhill-Day, and Non-expressway-Curved-Day are also categorized as grade High. Conclusion and Application: Safety managers in the fields require basic information on accident prevention that can be easily understood. The research findings will serve as a practical guideline for establishing preventive measures for traffic accidents.

Trucker Shortage as Government Failure

The United States is experiencing a shortage of large-truck drivers. Such a shortage might normally be attributed to a failure in the market for driver services. Instead, this article argues that the trucker shortage is an example of government failure, wherein government regulations fail to achieve any gains and, instead, worsen existing problems in the regulated industry.

A comprehensive analysis of factors leading to speeding offenses among large-truck drivers

Much research has demonstrated that speeding is the most common offense among car drivers. However, few studies have focused on this offense among drivers of large trucks. This paper investigates the factors that lead to speeding offenses for drivers of large trucks in Taiwan. The study sample consisted of information for 2101 male large-truck drivers from a national survey in 2012. The results revealed that drivers’ daily working hours ranged from 2 to 15h with a mean of 9.67h, and that they worked for approximately 25.23days – and rested only 4.77days – per month. Among these observed drivers, 11.6% reported having at least one speeding offense over a one-year period. The results of a logistic regression model presented that the factors that influenced speeding offense were not related to job experience. Rather, the driver’s demographics (age and education), mental condition (sleep quality), and driving status (yearly distance driven and driving late at night) were significantly linked to speeding offenses.

Gap Acceptance During Lane Changes by Large-Truck Drivers-An Image-Based Analysis.

This paper presents an analysis of rearward gap acceptance characteristics of drivers of large trucks in highway lane change scenarios. The range between the vehicles was inferred from camera images using the estimated lane width obtained from the lane tracking camera as the reference. Six-hundred lane change events were acquired from a large-scale naturalistic driving data set. The kinematic variables from the image-based gap analysis were filtered by the weighted linear least squares in order to extrapolate them at the lane change time. In addition, the time-to-collision and required deceleration were computed, and potential safety threshold values are provided. The resulting range and range rate distributions showed directional discrepancies, i.e., in left lane changes, large trucks are often slower than other vehicles in the target lane, whereas they are usually faster in right lane changes. Video observations have confirmed that major motivations for changing lanes are different depending on the direction of move, i.e., moving to the left (faster) lane occurs due to a slower vehicle ahead or a merging vehicle on the right-hand side, whereas right lane changes are frequently made to return to the original lane after passing.

Machine Learning for Recognizing Driving Patterns of Drivers of Large Commercial Trucks

Commercial large truck crashes are more likely to involve fatalities and significant costs than passenger vehicle crashes are. To reduce fatigue-related crashes of large trucks caused by drivers' irregular work schedules, FMCSA has enforced hours-of-service rules to regulate the activities of drivers of commercial large trucks. The complex influence of drivers' multiday driving activity patterns on crash risk was examined with data collected from two national truckload carriers. A machine learning approach, k-means clustering, was used to classify large truck drivers into 10 clusters according to their 15-min driving activities over multiple days. Then, the crash risk and driving activity pattern were identified for each cluster. Discrete-time logistic regression models were used to quantify the relationships between driving activity patterns and crash risk. Results indicated that the driving pattern with the lowest crash risk could be daytime driving between 4:00 a.m. and noon, with rest breaks in the late afternoon (4:00 to 6:00 p.m.). Drivers with high proportions of afternoon on-duty time after a long off-duty period experienced significantly higher crash risk. A representative day concept is proposed as a complementary method to identify relationships between driving patterns and crash risk. Moreover, on-duty hours can be a useful indicator of crash risk for drivers of large trucks. High proportions of on-duty hours in the early morning and late afternoon often are associated with high crash risk.

Examining the impact of opioid analgesics on crash responsibility in truck drivers involved in fatal crashes

IntroductionCommercial motor vehicle (CMV) drivers, particularly drivers of large trucks continue to be a population of concern regarding traffic safety despite the reduction in large truck crash rates over the past decade. Medication and drug use while driving is one important risk factor for large truck crashes. Work-related exposures, such as vibration, manual handling and poor ergonomics contribute to an increased risk for injuries and chronic conditions and are common reasons for opioid analgesic (OA) use by CMV truck drivers. The objectives of this study were to examine the role of OA use in CMV truck drivers involved in fatal crashes by: (a) generating prevalence estimates of OA use; (b) documenting the relationship between OA use and crash responsibility. MethodsCase-control study using logistic regression to compare Fatality Analysis Reporting System (1993–2008) record of one or more crash-related unsafe driver actions (UDAs – a proxy measure of responsibility) between drivers with a positive drug test and drivers with a negative drug test for OA, controlling for age, other drug use, and driving history. ResultsThe annual prevalence of OA use among all CMV drivers of large trucks involved in fatal crashes did not exceed 0.46% for any year in the study period and mostly ranged between 0.1 and 0.2%. Male truck drivers using OA had greater odds of committing an UDA (OR: 2.80; 95% CI: 1.64; 4.81). Middle-aged users had greater odds than younger or older users. ConclusionThe results of our study indicate that the presence of OAs is associated with greater odds of committing an UDA. This association may have implications for the commercial transport industry and traffic safety. However, the limited prevalence of OA use is encouraging and further research is needed to address the limitations of the study.

Amphetamine, cocaine and cannabinoids use among truck drivers on the roads in the State of Sao Paulo, Brazil

Drugs are important risk factors for traffic accidents. In Brazil, truck drivers report using amphetamines to maintain their extensive work schedule and stay awake. These drugs can be obtained without prescription easily on Brazilian roads. The use of these stimulants can result in health problems and can be associated with traffic accidents. There are Brazilian studies that show that drivers use drugs. However, these studies are questionnaire-based and do not always reflect real-life situations. The purpose of this study was to demonstrate the prevalence of drug use by truck drivers on the roads of Sao Paulo State, Brazil, during 2009. Drivers of large trucks were randomly stopped by police officers on the interstate roads during morning hours. After being informed of the goals of the study, the drivers gave written informed consent before providing a urine sample. In addition, a questionnaire concerning sociodemographic characteristics and health information was administered. Urine samples were screened for amphetamines, cocaine, and cannabinoids by immunoassay and the confirmation was performed using gas chromatography-mass spectrometry (GC-MS). Of the 488 drivers stopped, 456 (93.4%) provided urine samples, and 9.3% of them (n=42) tested positive for drugs. Amphetamines were the most commonly found (n=26) drug, representing 61.9% of the positive samples. Ten cases tested positive for cocaine (23.8%), and five for cannabinoids (11.9%). All drivers were male with a mean age of 40 ± 10.8 years, and 29.3% of them reported some health problem (diabetes, high blood pressure and/or stress). A high incidence of truck drivers who tested positive for drug use was found, among other reported health problems. Thus, there is an evident need to promote a healthier lifestyle among professional drivers and a need for preventive measures aimed at controlling the use of drugs by truck drivers in Brazil.

Random Alcohol Testing Reduced Alcohol-Involved Fatal Crashes of Drivers of Large Trucks

This study examined the impact of random alcohol testing, implemented on August 1, 1994, on the likelihood that the driver of a large truck involved in a fatal motor vehicle crash was alcohol-involved. Among fatal crashes, the proportion of alcohol-positive large truck drivers (intervention group) was compared with the proportion of alcohol-positive light passenger vehicle drivers (control group). Annual Fatality Analysis Reporting System (FARS) data (1988-2003) were compiled for each of the 50 states and Washington, D.C., for the control and intervention groups. Using these pooled cross-sectional data, logistic regression modeled the likelihood that a driver was alcohol-positive (blood alcohol concentration > 0) before compared with after random alcohol testing. We attributed the difference-in-difference (the difference in likelihoods of being alcohol positive pretesting versus post-testing in large truck versus passenger vehicle drivers) to the impact of random testing. Drivers of large trucks were 18.6% less likely to be alcohol-involved after random testing was implemented than before random testing (odds ratio [OR] = 0.814, 95% confidence interval [CI]: 0.713-0.930). The control group of passenger car drivers was 4.7% less likely to be alcohol-involved after random testing was implemented (OR = 0.953, 95% CI: 0.924-0.983). The net reduction in the odds of alcohol involvement for drivers of large trucks was 14.5% (OR = 0.855, 95% CI: 0.748-0.976). Controlling for the general declining trend in alcohol-involved drivers in fatal crashes, random alcohol testing was correlated with a 14.5% reduction in alcohol involvement among large truck drivers.