Transport Vehicles Research Articles

Acoustic camera applied on sound transmission identification in closed spaces

In the ongoing context of office partitioning, users are increasingly paying attention to acoustic insulation between rooms. The issues commonly related are translated by confidentiality problems and annoying use of sound-enabled videoconferencing. In practice, when an insulation defect is perceived, standardized acoustic insulation measurements are carried out to characterize the overall insulation performance. However, it is often challenging to precisely identify by ear the various paths of sound transmission: through the partition, by poorly caulked junctions, through the ceiling, through the facade, etc. Acoustic antenna solutions employed to identify the location and frequency content of sources have existed for several decades. But, in addition of being expensive and bulky, until now their use was reserved for detection of sources on transportation vehicles (trains, cars, planes, etc.) or industrial equipment (wind turbines, boiler rooms, etc.). In recent years, more accessible and practical antenna solutions known as "acoustic camera" have become available. In this sense, we have been handling an acoustic camera to assist on the detection of the sound transmission paths between two rooms. This work address what has been experienced, outlining results obtained through the (evolving) methodology for sound transmission identification with acoustic camera in closed spaces.

Forecasting the volume of urban freight traffic

The efficient development of transportation vehicles in conditions of stiff competition and global digitalization depends on the correct methodology and forecasting of freight transportation. A review of scientific literature showed that all studies are directed towards the railway mode of transportation. The research conducted in this work focuses on freight transportation by road transport in the city of Almaty, where trucks remain the sole means of delivering goods from one point to another at the local transportation level. The main goal of the research presented in this article is to analyze statistical indicators of transportation volumes and freight turnover by road transport and develop software aimed at forecasting the expected volume of freight transportation. In logistics planning and management, the moving average method is employed to predict truckload volumes and freight turnover for the next year. This method works by averaging data points to smooth out random fluctuations, thereby providing a more stable forecast. This phenomenon arises from replacing the original levels of the time series with arithmetic means calculated for certain time intervals. To facilitate transportation volume planning and substantiate the optimality of freight transportation, it is necessary to have up-to-date statistical data. These statistic data serve as the basis for constructing a dynamic representation of automobile traffic volumes and freight turnover in the city of Almaty. The growth rates of indicators are calculated to assess dynamics. The forecasting aspect of freight transportation volume is addressed by developing an algorithm based on the moving average method.

A New Approach for Improving Flame Retardancy of Automotive Interior Upholstery

This study presents the flame retardant (FR) performance of chemically treated automotive upholstery fabrics using two different impregnation methods of Resin Transfer Molding (RTM) and supercritical carbon dioxide (scCO2). Referring to the related standards, untreated seat fabric obtained from seat upholstery of a bus (neat fabric, NF) and treated fabric samples underwent burning rate (BR) and limiting oxygen index (LOI) tests to compare effect of treatment and impregnation methods on FR performance. Thermal analysis was also conducted on the samples considering onset degradation temperatures and char yields. The results showed that BR and LOI of all samples were in acceptable range and treatment provided enhancement in FR performance of NF. The treated sample using scCO2 method gave the highest LOI value of 32% and the lowest BR of 21 mm/min subtending to 18.5% increase in LOI and 30% reduction in BR compared to those of NF. The performance of treatment in RTM was worse than that of scCO2 and better than that of NF. The results confirm that both treatment and methods used in this study give promising results for safety against fire in transportation vehicles.

Designing and Implementing a Public Urban Transport Scheduling System Based on Artificial Intelligence for Smart Cities

Many countries encourage their populations to use public urban transport to decrease pollution and traffic congestion. However, this can generate overcrowded routes at certain times and low economic efficiency for public urban transport companies when buses carry few passengers. This article proposes a Public Urban Transport Scheduling System (PUTSS) algorithm for allocating a public urban transport fleet based on the number of passengers waiting for a bus and considering the efficiency of public urban transport companies. The PUTSS algorithm integrates artificial intelligence (AI) methods to identify the number of people waiting at each station through real-time image acquisition. The technique presented is Azure Computer Vision. In a case study, the accuracy of correctly identifying the number of persons in an image was computed using the Microsoft Azure Computer Vision service. The proposed PUTSS algorithm also uses Google Maps Service for congestion-level identification. Employing these modern tools in the algorithm makes improving public urban transport services possible. The algorithm is integrated into a software application developed in C#, simulating a real-world scenario involving two public urban transport vehicles. The global accuracy rate of 89.81% demonstrates the practical applicability of the software product.

Разработка трудноизвлекаемых запасов угля мобильными горнодобывающими комплексами без постоянного присутствия людей в зоне ведения горных работ

Today, millions of tons of coil remain in the subsoil and are written off as losses. These include hard-to-recover reserves unfit for mining with traditional high-performance means of integrated mechanization. One of the all-purpose technical solutions to the problem of developing such reserves can be technologies using robotic mobile mining equipment based on a heading-and-winning machine and a self-propelled car. Flowcharts for coal deposit development have been designed for seams unfit for mining with integrated mechanization means (medium thick seams, thick seams, flat and steeply inclined seams). These include open-pit and underground mining of marginal reserves from the side of the pit wall and limited reserves from the daytime surface performed by mobile mechanization means. During the operation of these means, the following process operations are repeated: coal breaking, loading into a self-propelled car, transportation along a preset trajectory, unloading into a reloader or other transport vehicle, or a warehouse on the bench. These operations can be robotized, which will increase both the safety and efficiency of coal mining. In order to perform coal-mining operations without constant attendance of maintenance staff, the existing mechanization means must be retrofitted with machine vision and a remote control system. Moreover, a robotized control system for the set of equipment must be developed to resolve the following problems. For the heading-and-winning machine: transportation (feed) along a straight line; transportation along the preset curve (formation of a diagonal entry); coal breaking at the preset thickness; coal breaking within coal-rock boundaries or along a preset cross-sectional contour; loading of broken coal into a self-propelled car; and control of the car loading level. For a self-propelled car: transportation along a preset trajectory from the loading point to the unloading point; control and management of the completeness of loading (unloading).

Exploring intercity travel decision-making in a developing country: Insights from COVID-19 impacts in Iran

This study examines the impact of COVID-19 on intercity travel mode choices in Mazandaran province, Iran, addressing critical gaps in understanding how pandemics affect travel behavior in developing countries. The research focuses on how socio-economic factors, perceived health risks, and travel time influence individuals’ choice of transportation modes during the pandemic. Using a stated preference survey method with 669 participants, the study assessed how concerns about virus transmission and adherence to health protocols shape travel decisions. Discrete choice modeling was employed to predict travel mode shares between public transport and personal vehicles. The findings reveal that COVID-19 risk perception, socio-economic factors, and travel time significantly impact travel behavior. Specifically, heightened perceived risk of infection resulted in a 25% reduction in public transportation use, with individuals increasingly opting for personal vehicles. Additionally, strict adherence to health protocols, such as mask-wearing and vehicle cleaning, improved safety perception, leading to a 40% increase in confidence in public transport. The study also found that socio-economic factors like age, income, and education significantly shaped travel preferences. These insights provide valuable guidance for public health policymakers and transportation authorities to enhance the safety and management of intercity travel during ongoing and future pandemics.

A planning framework for intra-city electric bus road transportation systems

Gasoline-based vehicle transportation plays a crucial role in environmental pollution, which eventually leads to serious health issues in urban areas. The scope of large-scale deployment of personalized electric vehicles in the near future can lean toward such issues but can overburden local distribution grids (LDGs), and the entities are producing green energy. In this context, an electric bus road transportation system (EBRTS) can be seen as an effective and promising way, although it is capital-intensive. EBRTs can be successfully deployed by developing a well-tailored planning model that encompasses amicable solutions while considering the conflicting interests of electric utilities and investors/transporters.This paper proposes a planning model for intra-city EBRTS, especially for metropolitan cities, to extract tangible benefits in favour of electric utilities and private players. The proposed framework envisages a bus fleet charging model and financial model within the spatial, technical, and financial constraints yet preserves the interests of electric utilities and investors by providing tentative solutions with all information necessary from the investor’s perspective. The methodology is free from optimization as the realistic constraints inherently employ search space reduction. The simulation results on a real public transportation system reveal the proposed methodology’s simplicity, rationality, applicability, and flexibility.

DISSIMILAR HARDOX AND LOW-ALLOY STEEL IN EXCAVATOR STRUCTURES

In the structure of excavators and other transport vehicles, it is observed that there is an increasing necessity to weld elements from the Hardox steels. The paper verifies the possibility to obtain accurate DMW (dissimilar metal welds from totally different grades of Hardox 450 steel with S355J2 steel). The microstructure and mechanical tests of the obtained various welds were analysed. Argon-based shielding gases with micro nitrogen additions were used for MAG welding. Gas mixtures with micro nitrogen additions of up to even 2000 ppm and their use in welding are an absolute novelty. The purpose of the manuscript was to find the correct parameters of making dissimilar joints with such modern mixtures and to determine the most suitable mixture for welding Hardox steel and low-alloy steel for use in the automotive industry.

A CAV-Lead speed advice approach considering local spatiotemporal traffic state near bottlenecks

Bottlenecks of the freeway generated especially by traffic accidents or temporary work zones contribute to significant reductions in system throughput and hinder the efficient traffic operations. It is imperative to take proactive measures to improve traffic state. With the rapid advancements in intelligent transportation, connected and autonomous vehicles (CAVs) have attracted much attention by its speculated capabilities in improving traffic safety and well-organized operational coordination. Therefore, reasonably utilizing the advantages of CAVs is possible to reduce the impact induced by bottlenecks. In this research, we propose a novel algorithm called CAV-Lead to obtain the CAV’s regulated speed under mixed CAVs and human-driven vehicles (HVs) environment to improve the overall utilization of the freeway capacity near bottlenecks. Firstly, we illustrate the basic principle of the CAV-Lead algorithm that takes both microscopic and macroscopic traffic characteristics into account. Then, based on the local spatiotemporal traffic state, the CAV-Lead algorithm is proposed to determine each CAV’s speed under mixed flow. Furthermore, a real-time simulation control framework considering the random behavior of HVs is presented. Moreover, several simulation evaluations including comparisons with basic scenarios and similar research are conducted under various CAV market penetration rates (MPRs). The results demonstrate that the CAV-Lead could improve the traffic performance, especially for the high traffic demand with certain MPRs.

A Driving Warning System for Explosive Transport Vehicles Based on Object Detection Algorithm.

Due to the flammable and explosive nature of explosives, there are significant potential hazards and risks during transportation. During the operation of explosive transport vehicles, there are often situations where the vehicles around them approach or change lanes abnormally, resulting in insufficient avoidance and collision, leading to serious consequences such as explosions and fires. Therefore, in response to the above issues, this article has developed an explosive transport vehicle driving warning system based on object detection algorithms. Consumer-level cameras are flexibly arranged around the vehicle body to monitor surrounding vehicles. Using the YOLOv4 object detection algorithm to identify and distance surrounding vehicles, using a game theory-based cellular automaton model to simulate the actual operation of vehicles, simulating the driver's decision-making behavior when encountering other vehicles approaching or changing lanes abnormally during actual driving. The cellular automaton model was used to simulate two scenarios of explosive transport vehicles equipped with and without warning systems. The results show that when explosive transport vehicles encounter the above-mentioned dangerous situations, the warning system can timely issue warnings, remind drivers to make decisions, avoid risks, ensure the safety of vehicle operation, and verify the effectiveness of the warning system.

Assessment of Sustainable Transportation Model Using Energy-Efficient Algorithm

The proliferation and extensive utilization of vehicles have escalated energy use and increased environmental damage. Utilizing large amounts of information on metropolitan traffic patterns can enhance vehicle transportation's environmental and financial aspects. This can be achieved through an efficient decrease in fuel consumption and contaminants. This study aims to present a novel framework for evaluating the Sustainable Transportation Model with Energy-Efficient Algorithm (STM-EE) by utilizing Route Planning Algorithms (RPAs) in a simulated environment. The CARLA simulator was used to compare the widely used conventional Dijkstra technique and an Integrated Genetic Algorithm (IGA) that integrates Genetic Algorithm (GA) with Particle Swarm Optimization (PSO) in different driving scenarios. This study aims to quantify the effects of RPA decisions on the energy consumption of vehicles. The efficiency of comparing the two RPAs has been enhanced by employing an offline energy estimate methodology. The proposed architecture is assessed through EE simulations to demonstrate its efficacy and adaptability. The results support the development of energy-efficient RPA solutions, which contribute to advancing the STM field.

Stop the Bleed-Wait for the Ambulance or Get in the Car and Drive? A Post Hoc Analysis of an EAST Multicenter Trial.

Background: The Stop the Bleed campaign gives bystanders an active role in prehospital hemorrhage control. Whether extending bystanders' role to private vehicle transport (PVT) for urban penetrating trauma improves survival is unknown, but past research has found benefit to police and PVT. We hypothesized that for penetrating trauma in an urban environment, where prehospital procedures have been proven harmful, PVT improves outcomes compared to any EMS or advanced life support (ALS) transport.Methods: Post-hoc analysis of an EAST multicenter trial was performed on adult patients with penetrating torso/proximal extremity trauma at 25 urban trauma centers from 5/2019-5/2020. Patients were allocated to PVT and any EMS or ALS transport using nearest neighbor propensity score matching. Univariate analyses included Wilcoxon signed rank or McNemar's Test and logistic regression.Results: Of 1999 penetrating trauma patients in urban settings, 397 (19.9%) had PVT, 1433 (71.7%) ALS transport, and 169 (8.5%) basic life support (BLS) transport. Propensity matching yielded 778 patients, distributed equally into balanced groups. PVT patients were primarily male (90.5%), Black (71.2%), and sustained gunshot wounds (68.9%). ALS transport had significantly higher ED mortality (3.9% vs 1.9%, P = 0.03). There was no difference in in-hospital mortality rate, hospital LOS, or complications for all EMS or ALS only transport patients.Conclusion: Compared to PVT, ALS, which provides more prehospital procedures than BLS, provided no survival benefit for penetrating trauma patients in urban settings. Bystander education incorporating PVT for early arrival of penetrating trauma patients in urban settings to definitive care merits further investigation.

Leveraging Artificial Intelligence for Optimizing Logistics Performance: A Comprehensive Review

Objective - Artificial Intelligence (AI) has become a pivotal technology in transforming logistics performance. This paper aims to comprehensively understand how AI-enabled solutions improve efficiency, accuracy, and responsiveness in logistics operations. The study focuses on synthesizing current research to explore AI applications across various logistics domains, such as predictive analytics, autonomous vehicles, and smart warehousing. Methodology/Technique – A systematic review approach was used to gather and analyze existing literature on AI applications in logistics. The review covered studies published in recent years, highlighting the advancements and impact of AI on logistics processes. The methodology included selecting relevant sources, categorizing AI applications, and assessing their effects on different logistics functions. Finding – The findings reveal that AI adoption substantially improves logistics operations, including enhanced operational performance, cost reduction, and increased customer satisfaction. Specific AI applications, such as predictive analytics for demand forecasting, autonomous vehicles for transportation, and smart warehousing for inventory management, were identified as key contributors to these improvements. However, challenges such as data privacy concerns and integration complexities were also noted. Novelty – This study's novelty lies in its comprehensive analysis of AI applications across various logistics domains, offering a holistic view of AI's role in optimizing logistics performance. This paper highlights the benefits of AI adoption and addresses the associated challenges, providing insights into future research directions and practical implications for leveraging AI in logistics. Type of Paper: Review JEL Classification: C61, C62, D83. Keywords: Artificial Intelligence; Logistics; Performance Improvement; Predictive Analytics; Autonomous Vehicles; Smart Warehousing Reference to this paper should be referred to as follows: Fatorachian, H. (2024). Leveraging Artificial Intelligence for Optimizing Logistics Performance: A Comprehensive Review, GATR-Global J. Bus. Soc. Sci. Review, 12(3), 146–160. https://doi.org/10.35609/gjbssr.2024.12.3(5) _____________________________________

Dynamic job allocation method of multiple agricultural machinery cooperation based on improved ant colony algorithm

Contemporary large-scale and systematic agricultural operations demand the collaborative efforts of multiple agricultural machines with distinct functionalities. However, the failure of a single agricultural machine during collaborative operations jeopardizes the entire undertaking. To address this challenge, this paper proposes a multi-machine collaborative dynamic job allocation method based on the improved ant colony algorithm. Initially, the improved ant colony algorithm is employed to determine the optimal solution for harvester scheduling. This solution is then fed into the data conversion algorithm to acquire the necessary unloading point information for transport vehicle scheduling. Subsequently, the improved ant colony algorithm is once again utilized to optimize the transport vehicle scheduling. In cases of agricultural machinery failure or changes in the operating environment, two distinct methods are employed based on the situation. The first involves double-layer rescheduling of both harvester and transport vehicles, while the second employs single-layer rescheduling exclusively for the transport vehicles, yielding the respective rescheduling results. The outcomes demonstrate that the proposed solution method effectively identifies the current optimal scheduling plan for both the harvester and transport vehicle in the event of malfunctions. Moreover, under the premise that the unproductive waiting time of the harvester is reduced to zero, and the number of transport vehicles is minimized, it achieves the minimization of operating time cost and transportation cost. This method exhibits significant potential for seamless integration into the practical application of unmanned farms, providing a foundation for addressing scheduling and management challenges in multi-agricultural machinery collaborative operations within complex farmland operating environments.

Study on the planning and scheduling strategies of the hydrogen supply network for road transportation in deep decarbonization scenarios

As the energy decarbonization continues to advance, hydrogen is gaining momentum. With the increasing demand for hydrogen and the lack of hydrogen infrastructure, it is urgent to solve the problem of hydrogen supply network layout planning. In order to reduce redundant construction of hydrogen supply network infrastructure and increase the rationality of its layout, this paper conducts research on the optimal layout planning of hydrogen supply network infrastructure in the transportation sector. By constructing a two-stage real-time scheduling model of hydrogen supply network, the unit cost composition of hydrogen supply network is more complete and accurate, and the location, technology type and quantity of each link of hydrogen supply network are obtained. The results show that the unit cost of China's hydrogen supply network is 27.8 CNY/kg in 2035 and 19.04 CNY/kg in 2050 under the baseline scenario. Compared with the results of existing literature, the two-stage real-time scheduling model can reduce by 7 % of the hydrogen energy supply network unit cost by decreasing the transportation vehicles and the investment capacity of fixed hydrogen storage equipment. The proposed method of the hydrogen supply network planning can provide policy-makers with the selection of sustainability pathways for dynamic hydrogen infrastructure development planning.

OPTIMALISASI KINERJA TRUK QUESTER CWE 370 MELALUI MODIFIKASI SISTEM VESSEL

The Quester CWE 370 truck is one of the most widely used transport vehicles in the industry, where reliability and operational efficiency are critical to ensuring a smooth supply chain. This research aims to optimize truck performance through vessel system modification, which focuses on improving fuel strength and efficiency. The modification process includes checking the raw material, cutting, welding, and smoothing, which is followed by testing to ensure fuel flow stability and better engine response. The results show that vessel system modifications not only improve fuel efficiency, but also extend engine life and reduce the need for routine maintenance. Thus, these modifications provide long-term benefits for the company in terms of operational cost savings and increased productivity.

Investigating the Viability of Rubber Crumbs from Waste Tyres as Partial Replacement for Coarse Aggregates in Concrete

The number of waste tyres is on the increase, because of the growing use of transport vehicles. Almost one trillion waste tyres are generated in the world annually. Established methods of disposal, recycling and re-use of waste tyres have failed to keep pace with generation, proven to be ineffective, cost-intensive and in some cases environmentally unsustainable. This study aims to investigate the effects of utilizing crumbs from discarded rubber as a partial replacement for coarse aggregates in concrete. Rubber crumbs of 10 – 20mm nominal size were produced manually from waste tyres. The rubber crumbs were used to replace 10%, 20% and 30% of coarse aggregates in design concrete of 20N/mm2 target compressive strength. The effects of this material on the slump, water absorption and compressive strength of concrete were examined. The inclusion of rubber crumbs resulted in a decline in the slump of concrete up to 10% relative to the control specimen. Water absorption increased marginally at 10% replacement compared to the control specimen and recorded a maximum value of 0.77% with 30% replacement after 28 days of curing. The compressive strength of the concrete was negatively affected by the rubber crumbs. The maximum value of 13.9N/mm2 was attained at 10% replacement after 28 days of curing. Rubberized concrete with 10% replacement of coarse aggregates can be used for non-structural concrete members such as roof slabs, non–load–bearing partition walls, and roadside barriers. Chemical treatment of rubber crumbs to improve surface adhesion properties should be encouraged.

A rosin-functionalized plastic surface inactivates African swine fever virus.

African swine fever virus (ASFV) causes a severe hemorrhagic disease in pigs, leading to up to 100% case fatality. The virus May persist on solid surfaces for long periods; thus, fomites, such as contaminated clothing, footwear, farming tools, equipment, and transport vehicles, May contribute to the indirect transmission of the virus. Here, a plastic surface functionalized with tall oil rosin was tested against ASFV. The rosin-functionalized plastic reduced ASFV infectious virus titers by 1.3 log10 after 60 min of contact time and killed all detectable viruses after 120 min, leading to a ~ 6 log10 reduction. In contrast, the infectious virus titer of ASFV in contact with low-density polyethylene (LDPE) plastic reduced <1 log10 after 120 min. Transmission electron microscopy (TEM) showed significant morphological changes in the virus after 2 h of contact with the rosin-functionalized plastic surface, but no changes were observed with the LDPE plastic. The use of antiviral plastic in the farming sector could reduce the spread of ASFV through fomites and could thus be part of an integrated program to control ASFV.

Comprehensive review on evolution, progression, design, and exploration of electric bicycle

Many countries are moving to electric vehicles for daily transportation due to the scarcity of fossil fuels and the pollution emissions from internal combustion engines. The most promising technique to manage environmental pollution and energy security is the employment of electric vehicles. The electric bicycle is gaining an abundance of attention due to the various advantages of electric vehicles. Recently, people started using electric bicycles on a regular basis, and it is a suitable option for shorter daily rides. The present study was conducted using the Scopus database, which included an analysis of all papers pertaining to the content of electric bicycles up until the year 2023.The literature review delves into the profound impact and numerous benefits of electric bicycles, highlighting their potential for revolutionizing transportation. This exploration is informed by advancements in design and construction, offering insights into optimal electric bicycle development. One promising avenue for sustainability involves integrating solar technology into these vehicles, reducing reliance on conventional energy sources, and addressing environmental concerns. Discussions encompassed diverse aspects, including operation principles, throttle variations, commutation systems, self-charging mechanisms, health advantages, and solar-powered electric bicycles. To address this, the goal is to create universally acceptable electric bicycles to broaden their appeal and dispel misconceptions. In order to provide scientists and researchers with beneficial information to further optimize electric bicycles, the research fields pertaining to electric bicycles are reviewed in accordance with previous studies.

Optimizing underwater connectivity through multi-attribute decision-making for underwater IoT deployments using remote sensing technologies

The underwater Internet of Things (UIoT) and remote sensing are significant for biodiversity preservation, environmental protection, national security, disaster assistance, and technological innovation. Assigning tasks to autonomous underwater vehicles (AUVs) is a fundamental challenge in underwater technology and exploration. Remote sensing and AUVs are vital for pollution detection, disaster prevention, marine observation, and ocean monitoring. This work presents an optimized network connectivity using a multi-attribute decision-making approach for underwater IoT deployment. A feature engineering approach highlights the significant characteristics of underwater things, incorporating remote sensing data, and a multi-objective optimization method is used to select optimal UIoT for effective task allocation in deep-sea environments. A balance between data transmission, energy economy, and operational performance is necessary for efficient task distribution. Effective communication algorithms and protocols are needed to maintain environmental sustainability, protect marine ecosystems, and improve underwater monitoring enhanced by remote sensing technologies. Multi-criteria decision-making (MCDM) is beneficial for addressing various challenges in underwater technology, considering factors such as mission objectives, energy efficiency, environmental conditions, vehicle performance, safety, and much more. The proposed criteria importance through intercriteria correlation (CRITIC) methodology will assess technical competencies like communication, resilience, navigation, and safety in an underwater environment, leveraging remote sensing and aiding decision-makers in selecting appropriate undersea devices and vehicles for enhancing communication and transportation. This method prioritizes characteristics and aligns them with specific objectives, improving decision-making quality in the marine environment.