Commercial Truck Research Articles

An Exploratory Assessment of Driver Injury Severities in Large-Truck Crashes Involving Fatigued and Non-Fatigued Driving

Fatigue poses a persistent safety challenge for long-haul truck drivers, significantly elevating the risk of highway crashes and compromising daily work performance. This study investigates the factors influencing driver injury severity in single-large truck crashes due to fatigued driving, compared with non-fatigued driving (normal driving). Using Florida crash data from 2011 to 2019 (inclusive), covering both fatigue and non-fatigue-related crashes, this analysis employs random parameters logit models to account for potential unobserved heterogeneity in means and variances. A comprehensive array of factors affecting driver injury severity, including spatial and temporal characteristics, vehicle and traffic attributes, roadway conditions, and driver-specific characteristics, are examined. Strikingly, different parameter estimates emerge for fatigue and non-fatigue-related crashes, indicating fundamental dissimilarities in unobserved heterogeneity in large truck crash data. The estimated model results unveil distinct marginal effects between fatigued and non-fatigued driving, particularly concerning severe injury crashes, suggesting significant variation in driver behavior based on fatigue levels. These findings contribute substantially to the growing literature on the intrinsic disparities between fatigued and non-fatigued driving behaviors. Moreover, this research underscores the potential ramifications of these distinctions on the safety performance of commercial trucks, highway safety technologies, and policy-related safety countermeasures. Understanding the unique characteristics of fatigued driving can inform targeted interventions to bolster safety within the commercial trucking industry and mitigate the impact of severe crashes resulting from fatigued driving.

Estimating Vehicle Turn-In Rate of Expressway Rest Areas via ETC Gantry Data – An ADPC-GMM Approach

Vehicle turn-in rate is a critical and widely adopted input for expressway rest area design and operation. With the implementation of expressway ETC gantries, the ERA turn-in rate can be further estimated by measuring the travel speed distribution via ETC gantry data. This paper proposed an adaptive density peak clustering Gaussian mixture model (ADPC-GMM) for ERA turn-in rate estimation. The ADPC algorithm is applied to generate the GMM’s inputs accommodating to the traffic characteristic of ERA expressway segments and GMM would further provide the turn-in rate estimation results. To validate the model precision, the turn-in rate data of four selected ERAs in Sichuan, China, as well as the ETC gantry data of their corresponding expressway sections are obtained. According to the estimation results, the MAE and RMSE are 0.0228 and 0.0267 for the passenger car scenario and 0.0264 and 0.0356 for the commercial truck scenario, respectively. These results are also at the lowest level compared with the results acquired from ordinary GMM, K-Means and DBSCAN algorithms. The proposed method has good applicability for vehicle turn-in rate estimation and can be deployed at different ERAs, especially those ERAs without traffic monitoring.

Characterizing hydrogen-diesel dual-fuel performance and emissions in a commercial heavy-duty diesel truck

This study investigates hydrogen (H2) as a supplementary fuel in heavy-duty diesel engines using pre-manifold injection. A H2-diesel dual-fuel (H2DF) system was implemented on a commercial class-8 heavy-duty diesel truck without modifying the original diesel injection system and engine control unit (ECU). Tests were conducted on a chassis dynamometer at engine speeds between 1000 and 1400 rpm with driver-demanded torques from 10 to 75%. The hydrogen energy fraction (HEF) was strategically controlled in the range between 10 and 30%. Overall, CO2 reduction (comparable to the HEF level) was achieved with similar brake-specific energy consumption (BSEC) at all loads and speeds. To maintain the same shaft torque, the driver-demanded torque was reduced in H2DF operation, which resulted in a lower boost pressure. At higher loads, engine-out BSNOx slightly decreased, while BSCO and black carbon (BC) increased significantly due to lower oxygen concentration resulting from the lower boost pressure. At lower loads, engine-out BSCO and BSBC decreased moderately, while NO2/NO ratio increased substantially in H2DF operation. Deliberate air path and diesel injection control are expected to enable higher HEF and GHG reductions.

Electrified fleet and infrastructure aware energy efficient routing

This paper presents an optimization framework to improve the energy efficiency and cost-effectiveness of fleets of commercial trucks operating pickups and deliveries in urban areas. As the electrification of transportation is moving from passenger cars to medium- and heavy-duty vehicles, the proposed analysis considers a fleet of pickup and delivery trucks that includes conventional internal combustion engine vehicles (ICEV), as well as battery electric vehicles (BEV), and plug-in hybrid electric vehicles (PHEV). Given a set of pickups and deliveries, and a fleet including different types of vehicles, the goal is twofold: assign the best vehicle to each task, and solve the vehicle routing problem, i.e., find the optimal route to navigate the vehicle from the origin to the destination(s). To estimate the energy consumption of the different vehicles, vehicle dynamics are considered, together with actual charging infrastructure and road data, including speed limits, road grade, and stop signs. Moreover, the total cost of ownership (TCO) is evaluated to estimate the cost-effectiveness of different fleet compositions and operations. To solve this problem, a hybrid simulated annealing (HSA) heuristic algorithm is proposed. The algorithm is validated against a benchmark exact solver based on mixed integer linear programming (MILP). The proposed methodology achieves optimal results with a 1.2% optimality gap compared to the benchmark, surpassing MILP in computational efficiency. The research findings highlight how fleet composition and operational strategies can vary significantly based on whether the focus is on energy efficiency, total cost of ownership, or a combination of the two, also depending on the number of years of operation. Simulation case studies in the Columbus, OH area demonstrate that integrating fleet and recharging infrastructure information alongside energy savings in vehicle routing problem solutions can achieve 20% to 50% savings in fleet operation costs compared to solely optimizing for minimum energy consumption.

Investigation into Human Factors in Rear-End Crashes of Commercial Trucks Based on Human Factors Analysis and Classification System with Bayesian Networks Fusion Method

Investigation into Human Factors in Rear-End Crashes of Commercial Trucks Based on Human Factors Analysis and Classification System with Bayesian Networks Fusion Method

Advancing Vehicle Technology Exploration with an Open-Source Simulink Model Featuring Commercial Truck Solutions

Advancing Vehicle Technology Exploration with an Open-Source Simulink Model Featuring Commercial Truck Solutions

Commercial Truck Risk Assessment and Factor Analysis Based on Vehicle Trajectory and In-Vehicle Monitoring Data

Commercial Truck Risk Assessment and Factor Analysis Based on Vehicle Trajectory and In-Vehicle Monitoring Data

Research on Load Estimation for Commercial Trucks Based on Air Suspension State Parameters

For commercial trucks, accurate vehicle load information is crucial to preventing overloading and uneven load distribution, thereby improving transportation efficiency and reducing transportation costs. Existing research on vehicle load estimation primarily focuses on tires, axles, frames, and leaf springs, with limited studies on load estimation for vehicles equipped with air suspension. This paper presents the design of a vehicle onboard weighing system for civil commercial trucks based on the state parameters of air springs and proposes a matching method for vehicle load estimation. The system utilizes laser distance- and gas pressure sensors to collect the state parameters of the air springs. It estimates the vehicle load and displays the load information in real time on the onboard display. The vehicle load estimation method was explored through prototype testing and theoretical derivation. A fitting model was constructed using surface fitting techniques to establish the relationship between the effective contact area and the height variation and capsule pressure of the air spring. Subsequently, a load prediction model was built based on the force equilibrium equation of the air springs. Finally, the construction and accuracy of the load prediction model for a specific type of diaphragm air spring were verified using finite element analysis. The results demonstrated that the average relative error of the load prediction model could be controlled within 1%, meeting the high accuracy requirements of the vehicle weighing system.

Hybrid simulation-optimization approach for planning relief-aid distribution with a real-world case study

PurposeAuthorities have set up numerous security checkpoints during times of armed conflict to control the flow of commercial and humanitarian trucks into and out of areas of conflict. These security checkpoints have become highly utilized because of the complex security procedures and increased truck traffic, which significantly slow the delivery of relief aid. This paper aims to improve the process at security checkpoints by redesigning the current process to reduce processing time and relieve congestion at checkpoint entrance gates.Design/methodology/approachA decision-support tool (clearing function distribution model [CFDM]) is used to minimize the effects of security checkpoint congestion on the entire humanitarian supply network using a hybrid simulation-optimization approach. By using a business process simulation, the current and reengineered processes are both simulated, and the simulation output was used to estimate the clearing function (capacity as a function of the workload). For both the AS-IS and TO-BE models, key performance indicators such as distribution costs, backordering and process cycle time were used to compare the results of the CFDM tool. For this, the Kerem Abu Salem security checkpoint south of Gaza was used as a case study.FindingsThe comparison results demonstrate that the CFDM tool performs better when the output of the TO-BE clearing function is used.Originality/valueThe efforts will contribute to improving the planning of any humanitarian network experiencing congestion at security checkpoints by minimizing the impact of congestion on the delivery lead time of relief aid to the final destination.

Cooperative truck platooning trial on Canadian public highway under commercial operation in winter driving conditions

Cooperative truck platooning, a convoy of trucks driving together while communicating and coordinating with each other, represents a technology-driven approach to improve energy conversion efficiency, lower greenhouse gas emissions, and enhance road safety. Despite numerous studies have explored these potentials, there is a scarcity of empirical investigations into on-road cooperative truck platooning during commercial operations, particularly in winter driving conditions. This paper presents the findings of an experimental study on the first commercially focused truck platooning implementation on a Canadian public highway in the winter season, using two SAE level 2 class 8 trucks. The on-road trials took place on the Queen Elizabeth II Highway, between Calgary and Edmonton, with ambient temperatures ranging from −27°C to 12°C, and truck weights spanning 16–39 tons. Nine well-trained and experienced drivers conducted 41 incident-free (platooning and baseline) test trips, covering a distance of 22,855 km. The experimental results confirmed the feasibility of operating commercial truck platooning with 3–5 s time gaps on public roads during the Canadian winter season including various road surface conditions. The results also show that the platooning engagement ratio reached up to 88.9%, with an average of 61.6% across 25 platooning trips. Furthermore, the follower truck achieved a 1.6% fuel savings on flat road sections during platooning, but its freight transportation specific fuel consumption was higher than that of the lead truck on hilly terrain. Test results indicate the lighter truck exhibited higher specific nitrogen oxides (NOx) emissions. Moreover, the frequent engagement and disengagement of the cooperative truck platooning system had adverse effects on the powertrain system of the truck, leading to increased fuel consumption and engine-out NOx emissions. This study provides real-world data to identify limitations and needed areas for improvement in adapting cooperative truck platooning technology to commercial operations on public roads.

A Cloud-Based Ambulance Detection System Using YOLOv8 for Minimizing Ambulance Response Time

Ambulance vehicles face a challenging issue in minimizing the response time for an emergency call due to the high volume of traffic and traffic signal delays. Several research works have proposed ambulance vehicle detection approaches and techniques to prioritize ambulance vehicles by turning the traffic light to green for saving patients’ lives. However, the detection of ambulance vehicles is a challenging issue due to the similarities between ambulance vehicles and other commercial trucks. In this paper, we chose a machine learning (ML) technique, namely, YOLOv8 (You Only Look Once), for ambulance vehicle detection by synchronizing it with the traffic camera and sending an open signal to the traffic system for clearing the way on the road. This will reduce the amount of time it takes the ambulance to arrive at the traffic light. In particular, we managed to gather our own dataset from 10 different countries. Each country has 300 images of its own ambulance vehicles (i.e., 3000 images in total). Then, we trained our YOLOv8 model on these datasets with various techniques, including pre-trained vs. non-pre-trained, and compared them. Moreover, we introduced a layered system consisting of a data acquisition layer, an ambulance detection layer, a monitoring layer, and a cloud layer to support our cloud-based ambulance detection system. Last but not least, we conducted several experiments to validate our proposed system. Furthermore, we compared the performance of our YOLOv8 model with other models presented in the literature including YOLOv5 and YOLOv7. The results of the experiments are quite promising where the universal model of YOLOv8 scored an average of 0.982, 0.976, 0.958, and 0.967 for the accuracy, precision, recall, and F1-score, respectively.

Influence of commercial drivers’ risky behavior on accident involvement: moderating effect of positive driving behavior

The influence of risky driving behavior on road traffic accidents (RTAs) is a relationship that requires draconian measures to curtail the rising surge of road traffic accidents among commercial drivers. Any attempt to ignore this will result in continuous loss of lives and properties, thus weakening the global economy, especially in developing countries. The risky driving behaviors of commercial drivers (truck and taxi drivers) in Nigeria require a panacea due to their contribution to RTAs. The study examines the moderating effect of positive driving behavior on commercial truck and taxi drivers’ risky driving behavior and accident involvement relationship. A total of 1823 commercial vehicle drivers (943 taxi drivers and 880 truck drivers) completed the driver behavior questionnaire (DBQ), while the structural equation modeling (SEM) method was used for the analysis. The results indicated a significant moderating effect of positive driving behavior on the risky driving behavior and accident involvement relationship for both commercial truck and taxi drivers in Nigeria. Specifically, the truck drivers had a positive moderating effect, resulting in a decrease in RTAs with an increase in positive driving behavior. In contrast, the taxi drivers had a negative moderating effect. The results suggest that increasing positive driving behavior among truck drivers will enhance their safety, while taxi drivers will need more assessment to identify other risky behaviors that could expose them to more RTAs despite the positive driving behavior. This study will aid decision makers, transport trainers, and driver employers in knowing the importance of enforcing and promoting positive driving behaviors among drivers and include it in driving policy and driver training curricula towards RTA reduction.

Effects of Behavior-Based Driver Feedback Systems on the Speeding Violations of Commercial Long-Haul Truck Drivers

Effects of Behavior-Based Driver Feedback Systems on the Speeding Violations of Commercial Long-Haul Truck Drivers

Research on the Multi-Mode Composite Braking Control Strategy of Electric Wheel-Drive Multi-Axle Heavy-Duty Vehicles

Electric wheel-drive multi-axle heavy-duty vehicles have the characteristics of strong maneuverability and good passability, thereby they are widely used in heavy equipment transportation. However, current research on the composite braking of multi-axle heavy-duty vehicles is rare, which is not conducive to improving braking performance and braking energy utilization efficiency. This work proposes a multi-mode composite braking control strategy for the five-axle distributed electric wheel-drive heavy-duty vehicle. Firstly, given the differences in braking dynamics between two-axle vehicles and multi-axle vehicles, the brake dynamics characteristics of multi-axle vehicles are analyzed, and the vehicle dynamics model of multi-axle vehicles is constructed. Next, a multi-mode composite braking control strategy including a fully electric braking state and hybrid electro–hydraulic braking state is proposed in order to improve the braking energy recovery and braking stability. Finally, a hardware-in-the-loop simulation system is established, and the single-braking conditions and China heavy-duty commercial vehicle test cycle-heavy truck (abbreviated as CHTC-HT) are conducted to verify the performance of the braking control strategy. The results indicate that the recaptured braking energy and braking stability are significantly increased by applying the control strategy proposed in this work.

Self-reported sleep tendency poorly predicts the presence of obstructive sleep apnea in commercial truck drivers

Background and objectivesDrowsy 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. MethodsCommercial 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. ResultsEDS 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. ConclusionsDespite 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.

Emergency collision avoidance maneuvers of multi-trailer articulated heavy vehicles

A simulation investigation of emergency evasive maneuvers of the type experienced during obstacle avoidance is conducted for long combination vehicles at highway speeds. The dynamic model includes a commercial truck with a configuration that is typical of what is used in the U.S., with two 28-ft trailers connected to the tractor through a fifth wheel, and to each other by an A-dolly. The model is validated against test data and used for evaluating distance and time needed to safely perform a steering maneuver to avoid an obstacle on the road. The simulation results indicate a significant difference in both the required time and distance for safely exercising an emergency obstacle avoidance, ranging from approximately 151–276 ft in distance and 1.7–2.4 s from the start of the driver action. The reaction time of the driver will add to both measures by an amount corresponding to the lapse in time between the moment the driver recognises the obstacle and when a steering action is initiated. To the extent that the lighting and road condition add to the reaction time, a significantly larger distance and longer time are needed during nighttime driving as compared with daytime, under similar road and loading conditions.

Does sedation with AQUI-S® mitigate transport stress and post transport mortality in ballan wrasse (Labrus bergyltae)?

Ballan wrasse (Labrus bergylta) are commonly used as cleaner fish in salmon farms as a biological treatment to mitigate sea lice infestation. Improved welfare for cleaner fish both during production of these fish and when in sea-cages with salmon is crucial for the industry's development. A common operational procedure in ballan wrasse production is transporting juveniles from one land-based farm to another for further on-growing. Episodes of increased mortality have been reported after such transportations. In this study, the relationship between transport stress and post-transport mortality at the on-growing facility was examined. It was also investigated if light sedation with AQUI-S® can mitigate stress during transport. Stress was quantified by measuring cortisol release rate to the tank water during transport. This was investigated in 10 commercial live carrier truck transports (6 without AQUI-S® sedation and 4 with sedation during loading and transport). The total time of transport varied between 12 and 21 h. In general, mortality was significantly higher (1.0 ± 0.6% day-1) the first five days post-transport compared to 15-20 days post transport (0.5% day-1). There was also a strong relationship between fish weight at transport and post-transport mortality, where higher mean weight at transport reduced mortality. In contrast to what was expected, AQUI-S® treatment during transport procedures increased cortisol excretion rate, suggesting a stimulating effect of AQUI-S® on the stress axis in ballan wrasse. Considering these results, the value of using AQUI-S® to reduce stress during transport of juvenile ballan wrasse might be questioned. However, there was no relationship between cortisol release rate during transport and post-transport mortality. Furthermore, this study emphasizes that water cortisol measurements can be used as a none-invasive tool for monitoring stress and can be integrated into the welfare evaluation during commercial fish transports.

Peritoneal Dialysis in High-Risk Occupations.

Renal Replacement Therapy (RRT), involving two modalities of dialysis, namely haemodialysis (HD) and/or peritoneal dialysis (PD) remain the mainstay of therapy for patients with End Stage Renal Disease (ESRD) worldwide. The goals of renal replacement therapy are multi-pronged. They are not only limited to ensuring patient survival, but also improving patient overall well-being and quality of life. The relationship between employment and quality of life are often closely linked. Peritoneal dialysis provides patients with greater flexibility, independence and control over their treatment. This is particularly advantageous to the working ESKD patients. On the other hand, HD sessions can often interfere with work schedules due to HD facility attendance and the duration of each session. This case report highlights PD and its impact on improved quality of life by specifically supporting the case patient ‘s ability to maintain employment as a commercial truck driver. In addition, it edifies both ESRD patients and clinicians, particularly in developing countries, on the advantages of PD. These advantages extend beyond the physiological preservation of residual renal function and patient survival, but also embody the improvement of the overall well-being and quality of patients’ lives by broadening the employment horizon of patients in even high-risk occupations such as commercial truck driving.

Growth in the Worldwide Stock of E-Mobility Vehicles (by Technology and by Transport Mode) and the Worldwide Stock of Hydrogen Refueling Stations and Electric Charging Points between 2020 and 2022

This study discusses the portion of fuel cell electric vehicles (FCEVs) in the worldwide stock of vehicles on roads, particularly when compared to plug-in electric vehicles (PEVs), which comprise battery electric vehicles (BEVs) and plug-in hybrid electric vehicles (PHEVs). The study considers the overall number of these e-mobility (electric mobility) vehicles, as well as within each of 4 transport modes, namely: (1) passenger light-duty vehicles (PLDVs or simply “cars”), (2) light commercial vehicles (LCVs or simply “vans”), (3) buses, and (4) trucks. The study also investigates the progress in the number of hydrogen refueling stations (HRSs) for FCEVs, and contrasts that with electric charging points (ECPs) for PEVs; during the years 2020, 2021, and 2022. While the number of worldwide FCEVs nearly doubled in 2022 compared to 2020, the ratio of FCEVs to PEVs declined from 0.3348% in 2020 to 0.2738% (less than 0.3%) in 2022. In 2022 also, the number of FCEVs was 0.3914% (less than 0.4%) of the number of BEVs, and 0.9113% (less than 1%) of the number of PHEVs. The worldwide fraction of PEVs with respect to the total vehicles (both electric and non-electric) in 2022 was approximately 1.816% (split into 1.2704% for BEVs and 0.5456% for PHEVs), while the fraction of FCEVs was approximately 0.0050% (only 5 FCEVs per 100,000 vehicles). In terms of the convenience to supply the vehicles with energy, the number of worldwide hydrogen refueling stations nearly doubled in 2022 compared to 2020. Similarly, the worldwide number of electric charging points for use with PEVs nearly doubled in 2022 compared to 2020. However, the ratio of HRSs to ECPs declined from 0.0415% in 2020 to 0.0378% in 2022. The worldwide average FCEVs per HRS in 2022 was 70.69, while the worldwide average PEVs per ECP in 2022 was 9.75. Thus, PEVs are much more attractive than FCEVs for a driver concerned about the network of hydrogen stations. Furthermore, owners of PEVs have an additional option of recharging their vehicles at home (which is not applicable for FCEVs). Between 2020 and 2022, PEVs were dominated by BEVs, with 69.95% of PEVs being BEVs in 2022. This 2022 fraction of BEVs in PEVs reflects a consistent increase from the 2021 fraction (68.34%) and from the 2020 fraction (67.23%). Considering the worldwide increase in these e-mobility vehicles from 2020 to 2022, the number of FCEVs increased by a factor of 2.072, PHEVs increased by a factor of 2.322, and BEVs increased by a factor of 2.636, PEVs increased by a factor of 2.533. Thus, out of the 3 e-mobility vehicle technologies (FCEVs, PHEVs, and BEVs), BEVs had the strongest presence as well as the fastest growth.

CE Module: Program Evaluation of Fit to Pass®-A Remotely Accessible Health Promotion Program for Commercial Motor Vehicle Truck Drivers.

CE Module: Program Evaluation of Fit to Pass®-A Remotely Accessible Health Promotion Program for Commercial Motor Vehicle Truck Drivers.