Truck Drivers Research Articles

Salmonella Biomapping of a Commercial Broiler Hatchery

Poultry-associated salmonellosis results in significant costs to poultry producers and consumers. Given the vertically integrated nature of the United States poultry industry, a better understanding of Salmonella ecology throughout all levels of poultry production is essential. One nexus point is the hatchery, where eggs from multiple broiler breeder farms are incubated and hatched, with the chicks being sent to numerous farms; therefore, the hatchery represents an ideal area to understand preharvest Salmonella ecology and flow. To achieve this, a commercial broiler hatchery was biomapped, focusing on Salmonella prevalence and serotype diversity among four major sample type categories (Air, Egg, Water, Facility) across five different places in the prehatch, hatch, and posthatch areas. Following two sets of eggs from broiler breeder farms over two production days, the overall Salmonella prevalence was 26% (48/184). Of the positive samples, the highest prevalence was observed in swabs taken from the floor drains in the facility and transport truck (56%), as well as in the hatch and posthatch hatchery areas (50%). Kentucky (n = 17), Gaminara (n = 12), and Alachua (n = 11) were the dominant Salmonella serotypes, with serotypes of greatest outbreak concern from chickens (Enteritidis) representing only 6.25% (3/48) of all recovered Salmonella isolates. The posthatch transport area, including the underfloor reservoirs of the transport trucks, not only harbored Enteritidis but also the enrichment broths from these Salmonella-positive samples also possessed sequences matching the commercial live-attenuated vaccine Typhimurium strain according to CRISPR SeroSeq analyses. These findings highlight the complex diversity of commercial hatchery Salmonella populations, including identifying facility floor drains and transport trucks as potentially important critical control points for hatchery managers to focus their Salmonella mitigation efforts to reduce loads and serotypes entering live production farms.

Smartbin Track

This project presents a sophisticated waste management system that combines IoT technology with an Android application to optimize garbage collection processes. The core infrastructure includes IoT-enabled smart trash cans equipped with sensors to detect fill levels autonomously. Complementing this hardware, the Android app serves as a comprehensive platform for managing various aspects of waste collection operations. Divided into modules for administration, employees, and truck drivers, the app offers functionalities such as real-time monitoring of trash levels, route optimization for collection trucks, and automated alerts for full bins. Through seamless integration of IoT capabilities and mobile technology, the system facilitates efficient and environmentally responsible waste management practices, contributing to a cleaner and more sustainable urban environment. Furthermore, the system enhances coordination and transparency within the municipal waste management framework. Municipal administrators gain valuable insights into waste collection efficiency through the app's dashboard. Employees can input data and perform tasks seamlessly, while truck drivers receive real-time updates on bin fill levels and optimized routes, improving overall operational efficiency. By harnessing IoT technology and mobile platforms, this innovative solution not only streamlines waste collection processes.

Daily Goal-Disruptive Events Increase Truck Drivers’ Fuel Consumption via Anger and Happiness

Daily Goal-Disruptive Events Increase Truck Drivers’ Fuel Consumption via Anger and Happiness

Investigating the impact of fatigue level on visual attention of heavy-duty truck driver: An eye tracking study

Fatigue driving is considered one of the main risk factors leading to truck-involved crashes. A significant correlation between fatigue driving and visual behavior has been established in previous studies. However, the relationship between visual behavior and fatigue levels, especially among truck drivers, has been seldom explored. Therefore, this study conducted a field driving test on heavy-duty truck drivers to assess their visual attention distribution when affected by fatigue. Fatigue levels were measured based on the Percentage of Eye Closure (PERCLOS), and the impact on eye movement was evaluated. The study found that an increase in PERCLOS led to a decrease in the percentage of glances toward the left-wing mirror and the right-hand side of the windshield. Similarly, the number of glances toward the left-hand side of the windshield and left-wing mirror also decreased with the increase in PERCLOS. These findings suggest that a higher level of fatigue results in reduced attention toward the left-wing mirror and windshield, with truck drivers’ focus remaining at the front for longer periods, indicating a reduced alertness to road conditions. These insights could be valuable in monitoring fatigued drivers by examining the visual attention of heavy-duty truck drivers.

Near miss road traffic accidents and associated factors among truck drivers in Gamo zone, southern Ethiopia by using a contributory factors interaction model.

Road traffic accidents (RTAs) are among the leading causes of injuries, fatalities, and the resulting increase in financial burdens worldwide. Every year, RTAs cause numerous serious injuries and fatalities in Ethiopia. it is important to understand how prevalent near-miss crash accidents are, and which by definition could have injured the victim but did not result in an actual accident. The determinants of these near-misses are essential in road crash accident reduction strategies. In spite of the fact that near-miss accidents are much more common than actual losses or injuries, very little research has been conducted on them. Thus, this study was intended to assess the near-miss accidents and associated factors among truckers in Gamo zone, southern Ethiopia. The community-based cross-sectional study was employed from May 12 to July 10,2022, using a structured interviewer-administered questionnaire. A simple random sampling technique was used to select participants. The data were analyzed using the statistical package for social sciences. A binary and multivariate logistic regression model was used to identify the determinants of near-miss accidents. A statistical significance level was set at p < 0.05. About 72.5% of truckers had experienced near-miss road traffic accidents. The majority of the near-miss accidents were caused by speeding, followed by driving on the wrong side of the road and skidding, 65 (22.6%), 39 (13.5%), and 38 (13.2%), respectively. Driving frequency per week, location of accidents, condition of the road, sleeping status, and weather conditions were significantly associated with near-miss accidents. The prevalence of near-miss accidents is high in the Gamo zone. Being a younger and less educated driver, high driving frequency per week, driving on major roads and junctions, foggy weather, and inadequate sleep all contribute to the occurrence of accidents. Road safety measures that could address these identified factors are required to mitigate potential RTAs.

Enhancement of Truck Transportation Efficiency Through Remote Monitoring of Route and Mineral Load

Enhancement of Truck Transportation Efficiency Through Remote Monitoring of Route and Mineral Load

Deep reinforcement learning with deep-Q-network based energy management for fuel cell hybrid electric truck

To better utilize hydrogen energy from fuel cell electric vehicles (FCEVs), an investigation is conducted into how a proton exchange membrane fuel cell (PEMFC) hybrid vehicle's 100-km hydrogen consumption rate is affected by a dynamic programming-based energy management method. The Dueling Deep Q-Network (Dueling DQN) algorithm's energy management approach is proposed. The DQN algorithm is optimized to increase the method's stability and quicken its pace of convergence. The simulation results show that the fuel economy of DQN and Dueling DQN algorithm are 94.28 % and 95.7 % respectively, both of which are improved, while the Dueling DQN algorithm converges at 480 steps higher than the DQN algorithm converges at 800 steps, and the algorithm comparison verifies the two algorithms' The validity of both algorithms was verified by algorithm comparison. In China Truck Driving Conditions (CHTC-LT), the hardware-in-the-loop simulation of CAN communication protocol using NI-PXI hardware-in-the-loop test system achieves the target vehicle speed following and verifies the real-time performance of the strategy.

Integrated optimization of truck appointment quotas and container relocations for multiple blocks considering transshipment containers handling

The container terminal is a crucial transshipment node in the sea and land cargo transport supply chain, and the efficiency of container collection and distribution has always been a research hotspot. In this paper, we consider the impact of transshipment containers resulting from various uncertainties and integrate the operations of external truck pickups with the handling of transshipment containers in the import yard of the container terminal. In light of the varying number of transshipment containers and transit times for different blocks, coupled with the unpredictable arrival order of external trucks leading to the unknown number of relocations, it is proposed to dynamically adjust the upper limit of the appointed quota for each block. We establish a two stage mixed integer programming model for the truck appointment quota and the block relocation problem. The objective functions aim to minimize the number of relocations and minimize the number of yard crane moves under the maximal appointment quota, respectively. This model is utilized for dynamically regulating the upper limit of the appointed quota for each block and determining the time intervals for picking up the appointed and transshipment containers, along with the yard crane scheduling scheme. The problem is solved using CPLEX and the improved variable neighborhood search algorithm (IVNS). The IVNS incorporates the correction strategy, decoding strategy, and scheduling rules designed to align closely with the characteristics of the problem. Through scheme comparison and sensitivity analysis, five management insights are proposed for terminals. Experimental results demonstrate that the approach presented in this paper effectively enhances the utilization efficiency of yard cranes, reduces yard congestion, and yields benefits for both terminals and trucking companies.

Risk Factors for Chronic Non-Communicable Diseases of Long-Haul Truck Drivers during the COVID-19 Pandemic: An Integrative Review.

Long-haul truck drivers are responsible for transporting goods valued at millions of dollars of the world economy, and may have their health affected by living and working conditions. This study analyzed and synthesized scientific findings about risk factors for the development of chronic non-communicable diseases in long-haul truck drivers. An integrative literature review was conducted. We identified 23 studies that met the inclusion criteria and evaluated the health of 7363 drivers. The biological risk factors identified were age, gender, race/ethnicity, genetics, and comorbidities, and were considered to be non-modifiable for chronic diseases. The behavioral risks considered to be modifiable were sedentary lifestyle, smoking, alcohol consumption, overweight, diet, stress, anxiety, and unfavorable socioeconomic conditions. Environmental risks involved working conditions such as the following: number of working hours per day, week, and month; time away from home; risk of musculoskeletal injury; and opportunities for rest, hours of sleep, and access to health services. The results were presented in two categories: (1) biological, behavioral, and environmental risks, and (2) general recommendations to promote physical, cognitive, and emotional health. Macro-structural changes are needed to reorganize work and rest, improve access to health services to control modifiable risk factors, and to support behavioral and environmental changes to reduce chronic non-communicable diseases and deaths.

Occupational Stress Management of Truck Drivers and its Effect on Road Safety: Evidence from Petroleum Tanker Drivers in Lagos State, Nigeria

The feeling of apathy among truck drivers regarding managing their health and work stress is endangering lives and properties on Lagos roads. The study examined occupational stress management of truck drivers and on road safety. The study adopted the descriptive survey research designusing a complete enumeration method (Census).Copies of the questionnaire were distributed to fifty truck drivers who are executive members of the union in the state. The questionnaire was designed on a four-point Likert scale and the study used Pearson Product Moment Correlation and Multiple regression in analyzing the collected data. The findings revealed that there is astrong relationship between fatigue and road safety and a weak relationship between inattention and road safety. The findings further revealed that fatigue and inattention account for 47.8% of road safety issues experienced by truck drivers in Lagos State. The study concluded that the indicators identified for the independent variable are good measures of the occupational stress construct. However, the study recommended among others that employers of truck drivers and their unions should enlighten them and create more awareness of the need for them to pay more attention to their health to ensure the safety of lives and properties on Lagos roads.

An investigation of relationships between aberrant driving behavior and crash risk among long-haul truck drivers traveling across India: A Structural Equation Modeling approach

IntroductionRoad Traffic Crashes (RTC) are showing an increasing trend around the world. Empirical evidence suggests that the majority of the crashes are attributed to unsafe or dangerous driving behaviors among drivers. However, little is known about the crash risk among professional drivers such as Long-Haul Truck Drivers (LHTDs) considering aberrant driving behaviors in developing countries like India. Objectives: This study aims to investigate the direct effects of characteristics of LHTDs including socio-demographic, work and vehicle, health-related lifestyle on crash risk. This study also attempts to investigate the indirect impacts of socio-demographic characteristics on crash involvement among LHTDs mediated through aberrant driving behaviors. MethodsUsing cross sectional study design, face-to-face interviews were conducted among LHTDs in Salem city, Tamil Nadu, India. A Driver Behavior Questionnaire (DBQ) consisting of 23 items was utilized to measure aberrant driving behaviors among LHTDs and information related to background variables was also collected. A total of 756 valid samples were used for analysis purpose. Exploratory factor analysis confirmed a four-factor solution including errors, lapses, ordinary, and aggressive violations and the identified structure was validated using confirmatory factor analysis. The deployment of Structural Equation Model (SEM) enabled identification of interrelationships among identified latent and observed variables. Results: The developed SEM model showed that all the identified four latent constructs were positively associated with crash involvement among LHTDs. The results further revealed that age and marital status were found to exhibit an indirect impact on crash involvement in addition to direct effects. ConclusionThe findings from this study could serve road safety authorities and Indian trucking industries to target risky driving behaviors in order to develop engineering and interventional countermeasures.

Characterizing last-mile freight transportation using mobile phone data: The case of Santiago, Chile

Understanding the mobility of surface freight transportation is relevant in urban planning and for developing public policies. Literature shows that most previous efforts on this topic rely on surveys and limited data. In contrast to other works, in this paper, we present an innovative methodology for characterizing last-mile freight transportation that uses a novel and copious data source: mobile phone data, which provides a broader scope. Our methodology involves calibrating supervised machine-learning models that allow us to link cell phones with truck drivers. In this endeavor, we construct several input variables that track mobile phone’s daily movement patterns, including traveled distances, interactions with highway networks, and land use variables. We test our approach by conducting a case study in Santiago, Chile, for which we analyze mobility patterns and logistics indicators disaggregated by day, hour, and zoning. For this case, we show that all supervised models performed well regarding AUC, which can be attributed to the high granularity and handling of the data. However, we chose to use NGBoost in all subsequent experiments, as it provided slightly better results on our validation data. Our work has several implications for practice. For instance, our results can support decision-makers and policymakers in identifying critical areas where urban logistics centers and transportation interventions are needed. Finally, several research lines stem from our work, which include assessing the impact of incorporating geospatial information and the measurement of logistics sprawl over time.

Optimizing container ports’ transportation and Distribution systems toward a low-carbon future: A Shenzhen port case study

Although the large throughput of China’s container ports signals positive progress on trade and economic growth, large Chinese container ports still heavily rely on high-emitting and heavy-polluting diesel trucks for transportation and distribution. The reliance on diesel HDTs is aggravating air pollution in ports’ surrounding areas, worsening traffic congestion, and preventing port cities from establishing ambitious carbon-emission reduction targets.

Securing Your Eggs in Multiple Baskets — Assuring a Resilient and Secure Supply Chain

AbstractThe global supply chain is a complex system of systems made up of and relying on other complex systems of systems (SoS) to achieve its goals. To take a typical example, Enterprise A is supplied essential parts on a regular basis to manufacture its products. To place the order requires global financial systems, integrated email systems, the internet, multiple telecommunications systems, and supply software provided by large companies. To deliver the parts may require air and maritime transportation systems, the rail network, interstate highway systems, road haulage companies, state and local transportation systems and so forth. When any of these complex systems fail, the impact can be global, and the results catastrophic. Recent examples include the shortage of Personal Protective Equipment (PPE) during the COVID pandemic, computer chip shortages delaying the assembly and sales of cars, and, most recently, the baby formula shortage. These were due to disruptions in the supply chain caused by an overreliance on single sourced suppliers who failed to deliver, transportation disruptions, outsourcing of critical parts, supplies, medicines to distant countries, and/or an overreliance on “Just In Time” for inventory management. This is the case of placing too many eggs in too few baskets, and often just one basket. Counterfeit or substandard parts and products can enter the supply chain via graft, breaks in chain of custody, or carelessness. This has included critical mechanical parts on aircraft, chips containing spyware, and substandard or out of date medicines substituted for the real thing resulting in serious illness and death. This complex SoS needs to be examined, studied, and understood in the same way as a mission critical system; threats, vulnerabilities, and risks need to be identified and mitigated and assurance cases defined to ensure a solid and reliable supply chain. This paper will look at the supply chain of an example factory system to determine how some of these problems can be predicted, prevented, mitigated, and solved using the UAF, RAAML and assurance case techniques.

Driver Drowsiness Detection System

Among the leading causes of fatalities in traffic accidents is being sleepy while driving. Long-haul truck drivers, bus or overnight bus drivers, and drivers who operate their cars after midnight are more prone to encounter this issue. Accidents Senger’s worst Every year, car accidents involving drunk drivers result in several fatalities and serious injuries. Due to their enormous practical significance, identifying driver tiredness and its indication are therefore significant study areas. The acquisition system, processing system, and warning system are the three components or modules that make up the fundamental sleepiness detecting apparatus. The acquisition system takes a frontal facial video of the driver's and transmits it to the processing block for real-time analysis to identify weariness.

Job gain and job loss dynamics in the truck transportation industry

Job gain and job loss dynamics in the truck transportation industry

An Investigation of Double Collisions of Pickup Trucks with Multiple Fatalities in Narathiwat Province, Thailand, October 2023

On 11 Oct 2023, a pickup truck crashed into a roadside tree in Narathiwat Province, southern Thailand after overtaking another vehicle. We conducted a descriptive study by reviewing medical records and interviewing rescuers, officers, survivors, and witnesses. We surveyed the collision site, reviewed video records, and used Haddon’s matrix to assist with the analysis. A pickup truck carrying 11 passengers (including the driver) overtook another pickup truck on a stretch of road. The driver of the first pickup truck lost control due to tire deflation and crashed into a tree resulting in eight deaths (72.7%) and three hospitalizations (27.3%). All deaths were caused by severe head injuries. Multiple factors during pre-crash (vehicle unfamiliarity leading to improper passing sight distance and speeding), the crash (overloaded truck and a roadside tree), and post-crash such as poor communication, contributed to the severity of this event. The driver of the truck had no driving license and was unfamiliar with the vehicle. Six passengers were sitting in the truck’s cab and three were in the cargo bed. Pre-crash and crash speeds were determined to be 80 and 100 kilometers per hour, respectively. The passing sight distance was grossly insufficient. Poor communication in the rescue process and the crowd of people at the scene resulted in improper emergency management. Strengthening law enforcement, increasing the number of speed limit signs, felling roadside trees, and practicing mass casualty incidents should be implemented.

Analisis Produktivitas Alat Gali Muat dan Alat Angkut Pada Pengupasan Lapisan Tanah Penutup (Overburden) Di Pt. Cahaya Riau Mandiri Kabupaten Lahat Provinsi Sumatera Selatan

PT Cahaya Riau Mandiri is a company engaged in coal mining that operates in Lahat Regency, South Sumatra Province with an open pit mining system. PT Cahaya Riau Mandiri has a production target of 70,000 tons/month in one shift. The purpose of this study is to determine the actual production of excavating and hauling equipment and the compatibility factor in coal mining, knowing the obstacles to effective working time in overcoming work obstacles that can be avoided so that loss time can be reduced and the resulting production can be achieved according to the company's target. The research method used in this research is quantitative and the stages in the preparation of this writing start from literature study, field observation, field data collection, data processing, data analysis, discussion and conclusions and suggestions. Based on the research and calculations that have been carried out, it is obtained that the actual production of the Liugong CLG952E excavator digging and loading equipment is 46. 522, 30 tons / month and the Iveco 682 dump truck conveyance is 44. 525, 25 tons / month with a compatibility factor of 0, 56. The strategy that can be done in increasing production is to improve work barriers, filling factors, tool compatibility and circulation time by using the basic work reference from the company. After improving the work of the Liugong CLG952E excavator and Iveco 682 dump truck transport equipment, the production of the Liugong CLG952E excavator increased from 46. 522, 30 tons / month to 70. 068, 54 tons / month and the Iveco 682 dump truck transport equipment increased from 44. 525, 25 tons / month to 70. 048, 21 tons/month.

Анализ проблем грузовых перевозок в городском сообщении

The main problems of truck road transport in urban communication in the Russian Federation are considered. The economic and social importance of urban freight motor transport is noted. Specific problems of the functioning of urban freight transport are presented, including high wear and tear of trucks for urban transportation, lack of infrastructure and congestion of the road environment, weak implementation of information technologies, insufficient qualification of personnel

화물차의 제동장치에서 발생하는 과열 예측방안 연구

Recently, due to the increase in domestic and international online e-commerce platforms and the increase in container traffic at domestic ports, the operating ratio of large trucks has increased, and the number of truck fires is continuously increasing. In particular, spontaneous combustion is the most common cause of truck fires. Various academic approaches have been attempted to prevent truck fires, but due to the lack of research on the spontaneous tire ignition phenomenon that occurs during braking, this research directly designed and manufactured an experimental device to establish an environment similar to the braking system of a truck. A non-contact temperature sensor was installed on the brake device of the experimental device to collect temperature data generated from the brake device. Based on the data collected from the temperature sensor of the brake device and the temperature sensor on the tire surface, the ARIMA model among the time series prediction models was used to Appropriate parameters were selected to suit the temperature change trend, and as a result of comparing and analyzing the measured and predicted data, an accuracy of over 90% was obtained. Based on this, a plan was proposed to reduce the rate of fires in trucks by providing real-time warnings and support for truck drivers to respond to overheating phenomena occurring in the braking system.