Truck Drivers Research Articles

Identity Disclosure and Anthropomorphism in Voice Chatbot Design: A Field Experiment

Fueled by the widespread adoption of algorithms and artificial intelligence, the use of chatbots has become increasingly popular in various business contexts. In this paper, we study how to effectively and appropriately use voice chatbots, particularly by leveraging the two design features identity disclosure and anthropomorphism, and evaluate their impact on the firm operational performance. In collaboration with a large truck-sharing platform, we conducted a field experiment that randomly assigned 11,000 truck drivers to receive outbound calls from the voice chatbot dispatcher of our focal platform. Our empirical results suggest that disclosing the identity of the chatbot at the beginning of the conversation negatively affects operational performance, leading to around 11% reduction in the response probability. However, humanizing the voice chatbot by adding our proposed anthropomorphism features (i.e., interjections and filler words) significantly improves response probability, conversation length, and the probability of order acceptance intention by over 5.6%, 24.9%, and 10.1%, respectively. Moreover, even when the chatbot’s identity is disclosed along with humanizing features, the operational outcomes still improve. This finding suggests that enhancing anthropomorphism may potentially counteract the negative effects of chatbot identity disclosure. Finally, we propose one plausible explanation for the performance improvement—the enhanced trust between humans and algorithms—and provide empirical evidence that drivers are more likely to disclose information to chatbot dispatchers with anthropomorphism features. Our proposed anthropomorphism improvement solutions are currently being implemented and utilized by our collaborator platform. This paper has been This paper was accepted by Felipe Caro for the Special Issue on the Human-Algorithm Connection. Funding: This study is supported by the National Natural Science Foundation of China [Grants 72172169 and 91646125], Program for Innovation Research at the Central University of Finance and Economic, and Shanghai Pujiang Program. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2022.03833 .

Joint optimization of truck-drone routing for last-mile deliveries in urban areas

This paper presents a joint routing optimization model to satisfy the on-demand customer requirements by minimising the transportation and time penalty costs of trucks and drones, considering the constraints on paths and delivery time of trucks and drones, the drone battery capacity limit, and the customer demands. The improved genetic algorithm is proposed to solve the optimization model followed by the computational experiments demonstrating the superiority of the improved genetic algorithm over the original genetic algorithm in computational efficiency, economy, and punctuality. Further, the comparative analysis demonstrates that the multi-drone joint delivery mode considering the customer demands performs better than the other delivery modes and that the delivery routes can be re-optimised promptly to satisfy the real-time customer demands. The sensitivity analysis on the drone design parameters provides theoretical insights into deploying the size and speed of drone platoons when promoting truck-drone joint delivery in applications.

Research on work-stress recognition for deep ground miners based on depth-separable convolutional neural network

In deep mine production operations, the challenging operating environment intensifies the workload and pressure on coal miners. Long-term exposure to high-intensity operating pressure can seriously impact the physical and mental health of miners, leading to unsafe behaviors and accidents. To identify the pressure of miners' operations, this paper examines various driving scenarios, such as the deep-well tunneling machine cutting the wall and opening the alley, the shoveling machine shoveling ore, and the pickup truck driver transporting. The paper randomly collects facial images of miners during each operation using an explosion-proof CCD camera to obtain the facial expression characteristic data of miners. The Ferface2013 facial expression dataset was used to establish the dataset. The depth separable convolutional neural network MiniXception was used for training and to output the classification results of the pressure degree of deep shaft miners. A MiniXception-based miners' operating pressure recognition model was established. The training time, precision, recall, F1 score, and classification accuracy confusion matrix were selected. The study evaluated the effectiveness of the recognition model by measuring its training time, precision, recall, F1 score, and classification accuracy confusion matrix. The results indicate that the model has a correct recognition rate of 88% for the pressure state, 91% for the pleasure state, and 74% for the normal state. The overall accuracy of the model is 0.843. Therefore, the MiniXception recognition model is suitable for recognizing the pressure of miners' operations in deep mines. This can meet practical needs and is useful for preventing major accidents in mines, managing on-site safety, and managing safety in non-hazardous areas. It has important theoretical and practical significance.

En-route recharging scheduling strategy for electric trucks serving inter-terminal container transportation

ABSTRACT Container ports are increasingly deploying electric trucks (ETs) for inter-terminal transportation (ITT). However, en-route recharging activities, which reduce ITT efficiency, are necessary for ETs because of limited battery capacity. This study proposes a real-time strategy to schedule the en-route recharging activities for ETs. The strategy dynamically monitors the ITT system, and simultaneously designates charging piles for ETs and determines their positions in the service sequences. An ET may be designated to a remote but shorter-queued pile. The total queue time of the ETs served by a designated pile can be reduced by adjusting the service sequence. The effectiveness is demonstrated in comparison with an alternative strategy in a real-world case. The results indicate that the proposed strategy effectively reduces container throughput time by 21.8% and ET energy consumption by 12.3%. Sensitivity analyses suggest that the strategy is applicable for ports with multiple charging stations and moderate ET fleet size.

Analisis Kendala Pelayanan Jasa Bongkar Muat Peti Kemas Terhadap Kelancaran Operasional pada Perusahaan Ekspedisi Muatan Kapal Laut

Container services are services related to the use of containers or containers in the process of shipping goods. The container itself is a special container used to pack and transport goods efficiently, especially in trade. In the process of putting goods or stuffing goods that are packaged in the form of pallets, metal boxes and bulk. The process to enter a 20-foot or 40-foot container is determined by the number of buyer requests. The author chose qualitative descriptive research because this research is in accordance with the nature of the author's research which is descriptive and uses interview techniques, documentation, and others. In addition, this research is also in accordance with the practical research objectives. Mistakes in container stock management can have a devastating impact on the company, such as damage to goods, additional costs, and a tarnished reputation. To improve container loading and unloading services, companies need to improve communication, employee training, truck checks, forklift maintenance, and collaboration with service users. The process of shipping goods through the port involves various stages and parties such as consignees, suppliers, shippers, and field executives. The results of the study can be found that the process of container loading and unloading services in expedition companies experiences various obstacles, such as late truck drivers, delays in sending documents, shortage of containers, damage to container moving equipment at the depot, delays in the start of working hours, and too many containers that do not comply with the specifications by the exporter.

Carbon Footprint Quantification and Reduction Potential of Ecological Revetment in Water Net Region of China: Case Study in Yancheng, Jiangsu Province

With emphasis on constructing low-carbon cities, the renovation of the riverbank highlights energy conservation and carbon reduction. However, methods and standards for quantifying carbon emissions during ecological river channel construction are currently lacking. There is a scientific gap in research into carbon footprint assessment and reduction potential in ecological revetment technologies in water networks of China. This study attempts to clarify the carbon emission factors of different ecological revetment technologies and explore the carbon reduction potential during the construction stage of ecological rivers from the river revetment design, construction process and materials. The results show that in the carbon emission factors of six ecological revetment technologies, building materials have the largest adjusting potential for carbon reduction. The concrete material is responsible for 55.37–95.86% of carbon emissions in six ecological river technologies, with an average proportion of 69.96%. Accordingly, the concrete material emerges as the primary contributor to carbon emissions in ecological river engineering, followed by gasoline truck transportation and earthwork excavation. Moreover, the carbon emissions from ecological frame structures were the largest, followed by those of block structures, gabion structures, planted concrete and interlocking blocks and the wooden stake structure has the smallest carbon footprint. The choice of ecological revetment technologies is not only related to the realisation of regional water conservancy functions, but it also affects the carbon emissions of water conservancy projects. Engineers and decision-makers should pay great attention to the optimal design of the project, selection of low-carbon materials, energy saving and emission reduction in the construction process. This research not only provides guidance for design units in selecting appropriate river revetment technologies but also offers a theoretical foundation and data support for construction units to optimise their construction process management.

Improving Public Health Emergency Communication Along the U.S. Southern Border: Insights From a COVID-19 Pilot Campaign With Truck Drivers.

Tens of thousands of trucks cross the U.S.-Mexico border every day. Cross-border truckers' high mobility puts them at risk of acquiring and transmitting infectious diseases and creates challenges reaching them with emergency public health messaging due to their everchanging locations and limited English proficiency. Despite this community-level transmission risk and documented health disparities related to various infectious and noninfectious diseases experienced by truckers themselves, little has been published to provide practical recommendations on better reaching this audience through innovative outreach methods. This article describes a COVID-19 health promotion campaign that aimed to (1) identify, pilot test, and evaluate effective messages, channels, sources, and settings for reaching truckers on both sides of the U.S.-Mexico border and (2) build capacity and sustainability for messaging around future health emergencies. The pilot program ran for 6 weeks, June to August 2023, in three key commercial border crossings and delivered approximately 50,000,000 impressions, nearly 45% more impressions than expected. Considerations for practitioners include the areas of design, implementation, and evaluation. The results provide insight into how to design health promotion messages that resonate with cross-border truckers and how to place these messages where they will be seen, heard, and understood. This includes working effectively with community health workers (CHW), known locally as promotores; identifying local partners that allow CHW to set up onsite; and, working with partner organizations including employers. Practical insights for building evaluation metrics into traditional and grassroots outreach strategies to facilitate real-time optimization as well as continued learning across efforts are also described.

Changes in Traffic Jams and Injuries Impact on Acceptability of Automated Vehicles: A Strong Curvilinear Relation with no signs of Loss Aversion.

We investigate whether the acceptance of autonomous trucks as a function of their impact on traffic jams and injury rates are affected by loss aversion and diminishing sensitivity as described in prospect theory. In an online survey (N = 447), we presented randomised sets of values the replacement of human truck drivers would have on traffic jams and injuries. Adjusting for loss aversion provides no additional explained variance in the model, but adjusting for diminishing sensitivity does.

Human factors affecting truck – vulnerable road user safety: a scoping review

ABSTRACT Truck collisions with vulnerable road users (VRUs) are infrequent, but often severe or fatal. While research has investigated factors contributing to safety and collisions, a synthesis of human-related contributors remains missing. This scoping review follows PRISMA guidelines to synthesise studies on the effects of human factors on truck-VRU safety. With a focus on human factors related to pedestrians, cyclists, and truck drivers, five scientific databases were searched and 3,414 records were screened. Twenty-four articles met the inclusion criteria. Most were published after 2015, indicating a limited but growing focus on human factors affecting truck-VRU safety. Half of the studies analysed police collision reports, while others used qualitative data collection methods, like questionnaires and focus groups, or human subject experiments. Compared to passenger-vehicle literature, notably fewer behavioural studies were identified, highlighting a need for further behavioural human factors studies of truck-VRU interactions. Sociodemographic and vision-related factors were most frequently investigated and found to significantly affect collision occurrence and severity. Driving and cycling experience and training, and road user distractions were examined less, albeit being important. This review bridges a literature gap by focusing on human characteristics, states, decisions, and errors affecting truck-VRU safety, offering insights for road-user-centred mitigation strategies.

Truck drivers’ views on the road safety benefits of advanced driver assistance systems and Intelligent Transport Systems in Tanzania

Truck drivers’ views on the road safety benefits of advanced driver assistance systems and Intelligent Transport Systems in Tanzania

Global burden of HIV among long-distance truck drivers: a systematic review and meta-analysis

ObjectivesThe purpose of this study was to systematically summarise the empirical evidence on the prevalence of HIV among long-distance truck drivers (LDTDs) from all parts of the world.DesignA systematic review...

Successful percutaneous coronary intervention in a congenital single right coronary artery with acute myocardial infarction: A case report and literature review.

Single coronary artery (SCA) is a rare coronary artery malformation. SCA combined with atherosclerotic plaques can cause severe and widespread myocardial ischemia and infarction, leading to hemodynamic instability and even sudden death. A 48-year-old Chinese man was admitted for treatment of persistent chest tightness and panic for 5 hours. The patient was a lorry driver with high work intensity and mental stress, with body mass index of 33.78, history of smoking and alcohol consumption, but no history of hypertension and diabetes. Admission examination showed Troponin was 183.083 µg/L and CK-MB value was >300 µg/L. The patient was diagnosed with a congenital single right coronary artery (RCA) with acute myocardial infarction (AMI) by coronary angiography (CAG). Due to atherosclerotic plaques rupture, a complete occlusion of the proximal RCA with thrombolysis in myocardial infarction grade 0 of distal blood flow were found. The patient was treated with thrombus aspiration and thrombolytic therapy by percutaneous coronary intervention under the support of intra-aortic balloon pump. Postoperative the chest tightness and panic were relieved, and CAG revealed that the proximal thrombus of the RCA was reduced, and distal blood flow was restored to thrombolysis in myocardial infarction grade 3. After 2 weeks of intensive antithrombotic and lipid-regulating drug therapy, the patient was successfully discharged. Follow-up for 6 months, the patient was able to live and work normally without experiencing chest tightness and chest pain. Computed tomography angiography (CTA) confirmed a congenital single RCA with patent lumen and no severe stenosis. The congenital single RCA is very rare, and it is fatal in conjunction with acute coronary syndrome. Early detection and appropriate treatment is critical for AMI patient with single RCA. CAG is the gold standard for diagnosis of single RCA, and CTA is a necessary to describe the anatomical course of abnormal coronary arteries.

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.