Truck Drivers Research Articles

Lane numbers and their impact on commercial motor vehicle crash safety: An econometric perspective

This research investigates the intricate relationship between the number of lanes on highways and injury severities sustained by commercial motor vehicle (CMV) drivers. Many studies have addressed crash determinants, but the safety implications of differing numbers of lanes remain insufficiently examined, especially during the highway planning stages. Our study fills this knowledge gap by analyzing injury severity crash factors for a varied number of lane scenarios. Employing a random parameters logit modeling framework, we differentiated injury levels for 2–4 lanes and 6–10 lanes. Key factors were identified for each number of lanes, with older, loss of vehicle control, non-collision crashes, and crashes on locations where grade or hill existed being more perilous and increasing the risk of sustaining severe injuries on 2-lane highways. For 4-lane highways, factors such as non-Oregonian drivers, older drivers, crashes that occurred during the spring season, and crashes that occurred beyond shoulders were associated with an elevated probability of being involved in severe injury crashes. Regarding highways with 6 lanes and higher, driving too fast for conditions and driver error (drowsy, fatigued, inattentive, or reckless) increases the odds of being involved in higher levels of injury crashes. To enhance truck driver safety, we recommend the implementation of electronic stability control in CMVs, moderated speeds on graded sections, improved curve markers, and robust public safety campaigns.

Promoção da saúde de motoristas de caminhão de longa distância e suas famílias

ABSTRACT Objectives: to describe the development of a Health Information and Communication Technology for the health care of long-haul truck drivers and their families. Methods: this is a description of the development of an Information and Communication Technology, developed from March to September 2023, following the systematization of the experience in five steps: 1) starting point, 2) initial questions, 3) recovery of the lived process, 4) background reflection, and 5) arrival points. Results: the technology called “Work-Family Balance,” electronically available, presents resources for the health care of long-haul truck drivers. It is anchored in studies on the health of long-haul truck drivers, notes from the International Labor Organization, the Strategic Action Plan for Confronting Chronic Diseases and Non-Communicable Diseases in Brazil, 2021-2030, and the Declaration by the International Association of Family Nursing. Final Considerations: the theoretical improvement of nursing can potentially improve the health care of long-haul truck drivers, prevent Chronic Non-Communicable Diseases, and promote work-life balance to achieve the goals of Agenda 2030.

IT Job Infusion ‘Keeps on Truckin’: Towards a Context-based Framework

As technologies pervade workplaces, job designers look for an integrative framework that considers IT's enabling and constraining impact on worker well-being across diverse job contexts. Using context theorizing guidelines, this paper proposes an integrated Framework of IT Job Infusion that can be used to examine worker outcomes for various IT-infused jobs. The framework holds that (1) IT possesses properties that manifest as resources or demands, and (2) these resourceful and demanding manifestations of IT affect a worker’s perceptions of the task, physical, social, and organizational job contexts and job outcomes. The framework is then applied to the truck driving context experiencing IT infusion. The results of data gathered from 154 truck drivers validate our proposed framework. We conclude with the theoretical and practical implications of the framework.

Investigating the contributory factors influencing speeding behavior among long-haul truck drivers traveling across India: Insights from binary logit and machine learning techniques

Speeding is one of the most common aberrant driving behaviors among the driving population. Although research on speeding behavior among drivers has been increased over the decades, little is known about the motivating factors associated with speeding behavior among Long-Haul Truck Drivers (LHTDs), especially in developing nations like India. This study aims to develop a prediction model for speeding behavior and to identify the contributory factors and their influential patterns underlying speeding behavior among LHTDs in India. A cross-sectional study was conducted among LHTDs in Salem city, Tamil Nadu, India. The data were collected through face-to-face interviews using a questionnaire encompassing socio-demographic, work, vehicle, health-related lifestyle, and speeding-related characteristics. A total of 756 valid samples were collected and utilized for analysis purposes. While conventional statistical method like binary logit technique lacked prediction capabilities, machine learning algorithms including Decision tree (DT), Random Forest (RF), Adaptive Boosting (AdaBoost), and Extreme gradient boosting (XGBoost) were employed to model speeding behavior among LHTDs. The analysis results showed that RF demonstrated superior performance in predicting speeding behavior over other competing algorithms with accuracy (0.80), F1 score (0.77), and AUROC (0.81). From the befitting RF model, the importance of factors contributing to speeding behavior among LHTDs were determined through the variable importance plot. Pressured delivery of goods, sleeping duration per day, age of truck, size of truck, monthly income, driving experience, driving duration per day, and age of the driver were identified as the eight topmost critical factors contributing to speeding behavior among LHTDs. Based on the developed RF model, the hidden relationships behind identified critical factors in relation to the speeding behavior were investigated using partial dependence plots (PDPs). The outcomes of this research will be useful for road safety authorities and Indian trucking industries to frame suitable policies and to introduce effective strategies for mitigating speeding behavior among LHTDs to promote road safety.

Comprehensive IoT Approach to Enhance Truck Driver Safety

Comprehensive IoT Approach to Enhance Truck Driver Safety

A Virtual Vehicle–Based Car‐Following Model to Reproduce Hazmat Truck Drivers’ Differential Behaviors

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

Optimizing Truck Transportation by Introducing Discount for Shipping Fees

As increase of delivery items, the overtime of truck drivers becomes an important issue, and so the effective operation of trucks is necessary. In this research, the effect of the discount fee on leveling the amount of necessary is discussed. By introducing the discount fee, some of demand corresponds to the late delivery, and leveling loads of trucks is realized. The mathematical model is formulated as a Markov reward model. In numerical examples, the optimal discount is derived and the effectiveness of introducing discount is discussed. Sensitivity analysis, on the number of normal trucks, the capacity for each truck and the demand function for the discount, is also developed.

Seroprevalence Survey of HIV and Hepatitis B Virus and Behavioral Characteristics among Heavy Truck Drivers along Port Sudan-Khartoum Highways

Seroprevalence Survey of HIV and Hepatitis B Virus and Behavioral Characteristics among Heavy Truck Drivers along Port Sudan-Khartoum Highways

National Beef Quality Audit-2022: Transportation, mobility, live cattle, and hide assessments to determine producer-related defects that affect animal welfare and the value of market cows and bulls at processing facilities.

The National Beef Quality Audit (NBQA)-2022 serves as a benchmark of the current market cow and bull sectors of the U.S. beef industry and allows comparison to previous audits as a method of monitoring industry progress. From September 2021 through May 2022, livestock trailers (n = 125), live animals (n = 5,430), and post-slaughter hide-on animals (n = 6,674) were surveyed at 20 commercial beef processing facilities across the U.S. Cattle were transported in a variety of trailer types for an average distance of 490.6 km and a mean transport time of 6.3h. During transit, cattle averaged 2.3 m2 of trailer space per animal indicating sufficient space was provided according to industry guidelines. Of all trailers surveyed, 55.3% transported cattle from an auction barn to a processing facility. When surveyed, 63.6% of all truck drivers reported to be Beef Quality Assurance certified. The majority (77.0%) of cattle were sound when evaluated for mobility. Mean body condition scores (9-point scale) for beef cows and bulls were 3.8 and 4.4, respectively, whereas mean body condition scores (5-point scale) for dairy cows and bulls were 2.3 and 2.6, respectively. Of the cattle surveyed, 45.1% had no visible live animal defects, and 37.9% had only a single defect. Of defects present in cows, 64.6% were attributed to an udder problem. Full udders were observed in 47.5% of all cows. Nearly all cattle were free of visible abscesses and knots (97.9% and 98.2%, respectively). No horns were observed in 89.4% of all cattle surveyed. Beef cattle were predominantly black-hided (68.9% and 67.4% of cows and bulls, respectively). Holstein was the predominant dairy animal observed and accounted for 85.7% of the cows and 98.0% of the bulls. Only 3.1% of all animals had no form of identification. Findings from the NBQA-2022 show improvements within the industry and identify areas that require continued education and research to improve market cow and bull welfare and beef quality.

Practice of COVID-19 prevention measures and its factors in long-distance truck drivers of Tigray.

long-distance truck drivers are a population group that moves in and out of a country and can meet with different individuals and can easily expose to COVID-19. Therefore, this study aimed to assess the level of practice and associated factors towards COVID-19 prevention measures in long-distance truck drivers of Tigray. this is a cross-sectional study conducted on 350 long-distance truck drivers recruited by systematic random sampling in the Mekelle entry point from July 5th to July 20th 2020. Variables with a p-value ≤0.30 in the bivariate regression analysis were entered into the final model of logistic regression to recognize factors. All associations with the practice of COVID-19 prevention were tested for statistical significance with alpha set at the 0.05 level. around 293 (83.7%) with a 95% CI of (79.7-87.6%) of the long-distance truck drivers who participated in this study had good practice towards COVID-19 prevention measures. Having a previous test for COVID-19 is positively significantly associated with good practice towards COVID-19 prevention methods. However, individuals who had a history of COVID-19-like symptoms in the last four months and who have non-communicable diseases are 78% and 84% of the time less likely to practice COVID-19 prevention measures respectively. based on this study, the practice of COVID-19 prevention measures among the long-distance truck drivers of Tigray was very good. The results of this study suggest that more emphasis should be placed on drivers who have non-communicable diseases and providing COVID-19-like symptoms to the drivers.

For Better or for Worse How the Use of Digital Technologies Affects Truck Drivers' well-being

For Better or for Worse How the Use of Digital Technologies Affects Truck Drivers' well-being

Assessment of truck driver safety efficiency based on data envelopment analysis

This paper introduces a safety efficiency evaluation framework that integrates Data Envelopment Analysis (DEA) and Super-Efficiency Data Envelopment Analysis (SE-DEA) for quantitatively assessing the safety of truck drivers. The framework utilizes data from onboard equipment to capture drivers’ dangerous behaviors and poor driving states. Subsequently, it evaluates safety efficiency using the DEA combination model, providing safety rankings. Improvement suggestions are offered to underperforming drivers based on slack variable values. The proposed method is compared with EWM-Topsis and Critic-Topsis models using fixed weights, demonstrating better stability. The study’s outcomes enable freight companies to identify high-risk drivers and offer personalized, targeted safety training.

Health promotion of long-haul truck drivers and their families

RESUMO Objetivos: descrever o desenvolvimento de uma Tecnologia da Informação e Comunicação em Saúde para o cuidado da saúde dos motoristas de caminhão de longa distância e de suas famílias. Métodos: trata-se de uma descrição do desenvolvimento de uma Tecnologia da Informação e Comunicação, realizada no período de maio a setembro de 2023, seguindo a Sistematização da Experiência, em cinco etapas 1) ponto de partida, 2) perguntas iniciais, 3) recuperação do processo vivido, 4) reflexão de fundo e 5) pontos de chegada Resultados: a Tecnologia denominada Equilíbrio trabalho-família, disponibilizada eletronicamente, apresenta recursos para o a atenção à saúde de motoristas de caminhão de longa distância, ancorada em estudos sobre a saúde dos motoristas de caminhão de longa distância; Notas Organização Internacional do Trabalho; Plano de Ações Estratégicas para o Enfrentamento das Doenças Crônicas e Agravos Não Transmissíveis no Brasil, 2021-2030, e declaração da Associação Internacional de Enfermagem de famílias. Considerações Finais: o aprimoramento teórico da enfermagem tem potencial para melhorar a atenção à saúde dos motoristas de caminhão de longa distância, prevenir Doenças Crônicas Não Transmissíveis e promover o equilíbrio trabalho-vida para o alcance das metas da Agenda 2030.

Managerial decisions using information platforms for organizing the provision of logistics and transport services

The article examines managerial decision types that can be made with the help of functional capabilities of information platforms for organizing the provision of logistics and transport services. The classification features of managerial decisions in the transport and logistics sphere have been singled out, including methods of justification and making decisions, areas of coverage and application, duration, hierarchy level, methods of regulation and resolution, decisions made by using IT-technologies and information and logistics platforms, tools. The types of managerial decisions have been characterized (including decisions based on subjective conclusions and judgments; rational; economically justified; individual; collective; collegial; general; partial; decisions in the organizational-administrative; organizational-coordinating; informational-analytical; and regulatory spheres; strategic decisions; operational decisions, decisions made by top, middle and lower management managers; contour; structured; algorithmic; typical; non-typical), including those made by using information and logistics platforms, tools and services («DELLA Truck Transport»; «Lardi Trans»; «MaponPro», electronic document management system «M.E.Doc.»; «BAS Accounting Prof» and «BAS Accounting Corp» software; information service of electronic reporting and document management «Liga: REPORT». The existing types of information and logistics platforms («DELLA Truck Transport», «Lardi Trans», «MaponPro») have been studied, and the possibilities and advantages of their use for decision-making in the organization of the provision of logistics and transport services have been determined. It has been established that the use of such information services is an effective tool in the process of making and implementing a wide range of managerial decisions regarding the optimization of logistics and transport processes and routes, improving the quality of services and safety during transportation, and reducing costs when organizing the provision of logistics and transport services.

ОСНОВЫ ПРИМЕНЕНИЯ СИТУАЦИОННОГО ПОДХОДА К УПРАВЛЕНИЮ ПЕРЕВОЗОК

The authors of the article provide theoretical data on modern approaches to managing the processes of truck transportation based on a situational approach, which is based on the formation of a situational (transport) model that responds to input (situational) variables and uses internal (situational) variables.

Effect of Transport Road Slope on Fuel Consumption of Coal Mine Transport Truck

Fuel is a major component of mining operational costs. One of the factors that affect fuel consumption is the condition of the mine road conditions. This research was conducted in a coal mine in South Kalimantan to determine the effect of slope and hauling distance on the rate of fuel consumption. The research method used by researchers is a quantitative research method. From the results of the analysis, the total power required for the conveyance to overcome the slope of the road with a distance of 1.47 m is 6.47 MW and for one trip it requires 19.25 liters of fuel. Based on the results of calculations regarding the relationship between road slope and hauling distance to the speed of fuel consumption, it can be concluded that when in free conditions, if there is an addition of 1% road slope, fuel consumption will increase by 4,820 liters. Whereas in unloaded conditions, if there is an addition of 1% road slope, fuel consumption will increase by 2,071 liters.

Matheuristic approach and a mixed-integer linear programming model for biomass supply chain optimization with demand selection

It is crucial to identify alternative energy sources owing to the ever-increasing demand for energy and the other environmental problems associated with using fossil fuels. Biomass as a source of bioenergy is considered a promising alternative to fossil fuels. This study aims to optimize the biomass supply chain by developing an integrated model incorporating typical tactical supply chain decisions based on market or demand selection decisions. To this end, a novel mixed-integer linear programming (MILP) model is proposed to maximize the profit of the corresponding biomass supply chain and to commercialize electricity production by selecting electricity demand and making supply chain decisions regarding power plant operations, biomass feedstock purchase and storage, and biomass transport trucks. Owing to the intricacy of the MILP model, a fix-and-optimize-based solution strategy is developed and validated by applying it to several instances of a real-world case study. The results demonstrate that the proposed strategy can significantly reduce computational time while preserving high solution quality. Additionally, it helps improve planning and decision-making as it reveals the effect of essential biomass logistics characteristics on routing outcomes.

Brake Drum Temperature Rise and Its Influence on Truck Driving Safety in Tunnel Groups Section

Brake Drum Temperature Rise and Its Influence on Truck Driving Safety in Tunnel Groups Section

Optimizing Transportation Safety with Realtime Air Quality Monitoring and Health Alert System

Abstract: The proposed system is a smart air quality monitoring and health alert system designed specifically for truck drivers. The system aims to address the issue of poor air quality within truck cabins, which can lead to a range of health problems for drivers who spend extended periods on the road. The system utilizes advanced sensors to monitor the air quality within the truck cabin, measuring a range of parameters including carbon monoxide, carbon dioxide, and particulate matter. In addition to air quality monitoring, it also includes a health alert system that can notify drivers of potential health risks based on their personal health data and environmental conditions. This feature helps drivers take proactive measures to prevent health problems and maintain their well-being while on the road. Proposed system is a cost-effective and user-friendly solution that can improve the health and safety of truck drivers, ultimately benefiting both the drivers and the trucking industry. It can also be easily integrated into existing fleet management systems, making it a scalable solution for trucking companies of all sizes.

A MODAL SHIFT, BUT HOW?

In this paper, we investigate the conditions that must be met for modal shift (i.e., the transfer of freight transport from road to rail). In addition to describing the competitiveness conditions, the paper makes technical and organizational proposals for rail freight traffic management. One of the most important conditions is that rail-road intermodal freight transport must be competitive in time and price with unimodal road freight transport. The freight transport model presented in this article provides a solution to this problem and an approach to estimating the additional intermodal freight traffic. Another important criterion is the relatively dense network of road-rail links, known as intermodal transshipment points (ITPs) along main railway lines. The proposed model can be compared to the freight transport model described in the term Physical Internet, with the addition that the objective is to minimize road haulage when locating ITPs (or hubs). ITPs are rail-road terminals or, more precisely, transshipment points, which differ substantially from the commonly used continental terminals. The third condition to be met is a horizontal container handling procedure that can be applied efficiently (i.e., at low cost) under the railway catenary and is capable of handling intermodal units used in continental traffic and maritime freight. Finally, an example for the Visegrád countries is presented. The essence of the example is the potential additional freight traffic or modal shift that could be included in the proposed ITP network. We believe that a modal shift could occur for up to 50% of the indicated heavy goods vehicle (HGV) traffic if the offer is competitive.