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Mechatronics and Intelligent Transportation Systems
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Mechatronics and Intelligent Transportation Systems (MITS)
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ISSN (print): 2958-020X
ISSN (online): 2958-0218
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2024: Vol. 3
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Mechatronics and Intelligent Transportation Systems (MITS) offers an in-depth exploration of the evolving fields of mechatronics and intelligent transportation. This journal uniquely focuses on the fusion of mechanical engineering, electronic control, and intelligent systems, positioning itself at the forefront of technological advancements in transportation. MITS stands out for its commitment to bridging the gap between theoretical research and practical, real-world applications in mechatronics and transportation systems. Targeting both academic researchers and industry professionals, MITS provides a comprehensive platform for disseminating groundbreaking work in smart transportation technologies and mechatronic systems. The journal is characterized by its thorough coverage of topics like autonomous vehicles, robotics in transportation, and innovative control systems. Published quarterly by Acadlore, the journal typically releases its four issues in March, June, September, and December each year.

  • Professional Service - Every article submitted undergoes an intensive yet swift peer review and editing process, adhering to the highest publication standards.

  • Prompt Publication - Thanks to our proficiency in orchestrating the peer-review, editing, and production processes, all accepted articles see rapid publication.

  • Open Access - Every published article is instantly accessible to a global readership, allowing for uninhibited sharing across various platforms at any time.

Editor(s)-in-chief(2)
yougang sun
Tongji University, China
1989yoga@tongji.edu.cn | website
Research interests: Rail Transit; Maglev Vehicle Dynamics; Nonlinear Control and Intelligent Monitoring; Underactuated Robot; Electromechanical System Control
ana vulevic
Institute of Architecture and Urban & Spatial Planning of Serbia (IAUS), Serbia
anavukvu@gmail.com
Research interests: Urban Planning; Transportation Planning; Accessibility; Mobility; Environment Protection

Aims & Scope

Aims

Mechatronics and Intelligent Transportation Systems (MITS) is a cutting-edge journal dedicated to the latest advancements in mechatronics and intelligent transportation systems, along with their synergistic integration. MITS stands out as a platform for global researchers to present their novel and innovative ideas, particularly in areas such as intelligent vehicle control systems, mechanical engineering in transportation, and smart transportation infrastructure. The journal invites diverse forms of original submissions, including reviews, research papers, and short communications, along with Special Issues on specific topics. MITS is particularly keen on research that enhances transportation planning and operations through the application of new mechatronic technologies.

MITS aims to be a premier source for detailed theoretical and experimental research in its field, imposing no limits on paper length to ensure comprehensive and replicable studies. The journal's distinctive features include:

  • Every publication benefits from prominent indexing, ensuring widespread recognition.

  • A distinguished editorial team upholds unparalleled quality and broad appeal.

  • Seamless online discoverability of each article maximizes its global reach.

  • An author-centric and transparent publication process enhances submission experience.

Scope

The scope of MITS encompasses a broad range of topics, setting it apart from other journals with its focus on the intersection of mechatronics and intelligent transportation:

  • Computer Vision in Transportation: Explores advanced computer vision techniques for traffic monitoring, vehicle detection, and autonomous driving systems.

  • Image Processing Technologies: Focuses on the application of image processing in traffic signal recognition, lane detection, and vehicle classification.

  • Intelligent Transportation Infrastructure: Studies the development of smart roads, traffic management systems, and infrastructure that support intelligent transportation solutions.

  • E-Transportation Innovations: Examines electronic transportation advancements, including electric vehicles, charging infrastructure, and e-mobility services.

  • Development of Intelligent Vehicles: Research on designing and developing intelligent vehicles, encompassing aspects of automation, connectivity, and electrification.

  • Control Systems for Intelligent Vehicles: Investigates advanced control algorithms and systems for enhancing the safety and efficiency of autonomous vehicles.

  • Industrial Design in Transportation: Looks at the role of industrial design in vehicle aesthetics, ergonomics, and user experience.

  • Product Modeling and Design: Explores the process of conceptualizing and creating models for transportation products, emphasizing functionality and user interface.

  • Intelligent Mechatronic Control: Covers the integration of mechatronics in the control of transportation systems, including robotics and automation.

  • Connected Vehicle Technologies: Focuses on vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications for improved traffic flow and safety.

  • Auto Navigation and Driver Assistance: Studies navigation systems and driver assistance technologies such as adaptive cruise control and parking assistance.

  • Electrical Engineering in Transportation: Investigates the role of electrical and electronic engineering in the development of transportation systems, focusing on circuit design, signal processing, and power systems.

  • Lidar and 3D Sensing Technologies: Examines the use of lidar and three-dimensional sensors in vehicle navigation, obstacle detection, and environment mapping.

  • Proximity Sensors in Transportation: Discusses the application of proximity sensors in collision avoidance systems and traffic monitoring.

  • Mechatronic Products and Applications: Exploration of mechatronic products and their applications in transportation.

  • Modeling and Control of Mechatronic Systems: Strategies for modeling and controlling complex mechatronic systems in transportation.

  • Smart Energy Systems in Transportation: Integration of smart energy solutions in future transportation systems.

Articles
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Abstract

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In the face of the increasingly demanding of goods transportation and storage, the orchestration of cold chain logistics emerges as a critical and multifaceted endeavor. This study, addressing a notable gap in literature, establishes a comprehensive framework for temperature monitoring within cold chain logistics, focusing particularly on transportation and warehousing aspects. The complexity of managing temperature-sensitive goods is amplified by the burgeoning number of entities involved in this sector, underscoring the need for a robust monitoring approach. Recent global challenges have precipitated a series of disruptive events, further complicating the reliable transport of temperature-sensitive commodities. In light of these challenges, the necessity for meticulous temperature control during both transportation and warehousing phases is paramount; lapses in this regard could lead to grave consequences. A thorough analysis of existing cold chain delivery systems was conducted, alongside an examination of various temperature monitoring devices utilized in vehicle cargo compartments and storage facilities. The study not only scrutinizes current trends but also introduces novel solutions for effective monitoring. By exploring and evaluating these elements, the research contributes significantly to both theoretical and practical spheres, offering a solid foundation for future investigations and guidance for practitioners and decision-makers in the field. This exploration revealed the imperative for advanced sensor technologies and integrated data management systems, capable of providing real-time, accurate temperature readings throughout the entire cold chain process. The integration of smart transportation solutions, leveraging Internet of Things (IoT) technology, emerges as a pivotal factor in enhancing the reliability and efficiency of temperature monitoring. Additionally, the study underscores the importance of standardized protocols and practices across the industry to ensure consistency and reliability in temperature management. In conclusion, the framework proposed in this study not only addresses existing challenges in cold chain logistics but also paves the way for innovative approaches in temperature monitoring, fostering enhanced quality control and safety in the transportation and storage of temperature-sensitive goods.

Open Access
Research article
Cause Analysis of Whole Vehicle NVH Performance Degradation under Idle Conditions
haiping lai ,
huaguang xu ,
nian liu ,
jieliang guo ,
ruiqiang zhang ,
haigang wei
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Available online: 01-16-2024

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The NVH (noise, vibration, harshness) performance of automobiles is a key issue in enhancing user comfort. However, car manufacturers and original equipment manufacturers often invest more research and development effort into the new car performance at the initial design stage, neglecting the study of whole vehicle NVH durability and reliability, and this can significantly affect the user's riding experience. This paper focuses on the phenomenon of NVH performance degradation under idle conditions. Using LMS data acquisition equipment and software, vibration acceleration and frequency at 17 points on the vehicle, including the steering wheel, seat rail, and engine mount, were collected and analyzed. By conducting comparative experiments, the causes of NVH performance degradation after long mileage were explored. This aims to provide new ideas for improving the durability and reliability of whole vehicle NVH in future research and production.
Open Access
Research article
Simulation Analysis of Track Irregularity in High-Speed Maglev Systems Based on Universal Mechanism Software
xiangyang jia ,
haiyan qiang ,
cheng xiao ,
chenglin zhuang ,
pengyu yang ,
xueyan gao ,
sumei wang
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Available online: 12-25-2023

Abstract

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As high-speed magnetic levitation (Maglev) technology continues to advance, the safety, stability, and passenger comfort of high-speed Maglev trains during operation are subject to increasingly stringent requirements. In this background, this study attempts to develop a stability simulation model for high-speed Maglev vehicles travelling at different speeds using the software Universal Mechanism (UM) and give a comprehensive analysis. High-speed Maglev trains are now an advanced mode of transportation, they possess many advantages including high safety, low emissions, low energy consumption, less noise, and stronger climbing capabilities. The safety, stability, and comfort level of high-speed Maglev trains are closely related to their operational speed and the irregularities of the tracks. This study takes the Shanghai TR08 Maglev train as the subject and models it in the UM to simulate and analyze the subject. With the help of this model, the responses given by the subject to track irregularities when it runs at different speeds are simulated, and the changes in stability metrics such as the Sperling Index are analyzed. After that, this study also investigates the relationship between operational speed, track irregularity, and stability, and the findings of this study could provide valuable insights for optimizing the design of high-speed Maglev trains and controlling of track irregularities.

Abstract

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The automation of railway signalling control table preparation, a task historically marked by labor-intensity and susceptibility to error, is critically examined in this study. Traditional manual methods of generating these tables not only demand extensive effort but also bear the risk of errors, potentially leading to severe consequences in subsequent project phases if overlooked. This research, therefore, underscores the imperative for automation in this domain. An extensive review of existing methodologies in the field forms the foundation of this investigation, culminating in the enhancement of a select approach with advanced automation capabilities. The outcome is a standardized procedure, adaptable with minimal modifications to the unique national signalling norms of various countries. This procedure promises to streamline project execution in railway signalling, reducing both time and error margins. Such a standardized, automated approach is particularly pertinent to the Republic of Serbia, where this study is situated, but its implications extend globally. Key technologies employed include AutoCAD and Mathematica, which facilitate the requirements-driven automation process. This research not only contributes to the academic discourse on railway signalling automation but also offers a practical blueprint for its implementation across diverse national contexts.
Open Access
Research article
Machine Learning for Road Accident Severity Prediction
koteswararao kodepogu ,
vijaya bharathi manjeti ,
atchutha bhavani siriki
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Available online: 12-04-2023

Abstract

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In the realm of road safety management, the development of predictive models to estimate the severity of road accidents is paramount. This study focuses on the multifaceted nature of factors influencing accident severity, encompassing both vehicular attributes such as speed and size, and road characteristics like design and traffic volume. Additionally, the impact of variables, including driver demographics, experience, and external conditions such as weather, are considered. Recent advancements in data analysis and machine learning (ML) techniques have directed attention toward their application in predicting traffic accident severity. Unlike traditional statistical methods, ML models are adept at capturing complex variable interactions, thereby offering enhanced prediction accuracy. However, the efficacy of these models is intrinsically tied to the quality and comprehensiveness of the utilized data. This research underscores the imperative of uniform data collection and reporting methodologies. Through a meticulous analysis of existing literature, the paper delineates the foundational concepts, theoretical frameworks, and data sources prevalent in the field. The findings advocate for the continuous development and refinement of sophisticated models, aiming to diminish the frequency and gravity of road accidents. Such efforts contribute significantly to the enhancement of traffic control and public safety measures.
Open Access
Research article
Economic Feasibility of Solar-Powered Electric Vehicle Charging Stations: A Case Study in Ngawi, Indonesia
singgih dwi prasetyo ,
farrel julio regannanta ,
mochamad subchan mauludin ,
zainal arifin
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Available online: 11-27-2023

Abstract

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In the context of increasing electric vehicle (EV) prevalence, the integration of renewable energy sources, particularly solar energy, into EV charging infrastructure has gained significant attention. This study investigates the economic viability of grid-connected photovoltaic (PV) systems for EV charging stations in Ngawi City, Indonesia, selected due to its substantial solar energy potential and ongoing renewable energy initiatives. Key factors influencing the economic feasibility of these systems include load requirements, renewable energy potential, system capacity, levelized cost of electricity, payback period, net present cost (NPC), and cost of energy (COE). A comprehensive techno-economic assessment was conducted to estimate the capital recovery time, incorporating both utilization costs and payback periods. The analysis utilized the Hybrid Optimization Model for Electric Renewables (HOMER) software, focusing on the application of PV energy in EV charging stations within Ngawi Regency. Findings indicate that a PV system-based generation approach can adequately meet the power needs of EV charging stations. Notably, this system is capable of generating surplus energy, which presents an opportunity for additional revenue, thus enhancing its economic attractiveness. The analysis determined that to produce an annual output of 562,227 kWh, a total of 1245 PV modules, each with a 370-watt capacity, are necessary. This off-grid PLTS system, relying exclusively on PV modules for electrical energy generation, can sufficiently supply a daily load of 342.99 kWh for an EV charging station. The study underscores the potential of solar-powered EV charging stations in contributing to sustainable urban development, reinforcing the integration of renewable energy into urban infrastructure.

Abstract

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The optimization of traffic flow, enhancement of safety measures, and minimization of emissions in intelligent transportation systems (ITS) pivotally depend on the Vehicle License Plate Recognition (VLPR) technology. Challenges predominantly arise in the precise localization and accurate identification of license plates, which are critical for the applicability of VLPR across various domains, including law enforcement, traffic management, and both governmental and private sectors. Utilization in electronic toll collection, personal security, visitor management, and smart parking systems is commercially significant. In this investigation, a novel methodology grounded in the Kanade-Lucas-Tomasi (KLT) algorithm is introduced, targeting the localization, segmentation, and recognition of characters within license plates. Implementation was conducted utilizing MATLAB software, with grayscale images derived from both still cameras and video footage serving as the input. An extensive evaluation of the results revealed an accuracy of 99.267%, a precision of 100%, a recall of 99.267%, and an F-Score of 99.632%, thereby surpassing the performance of existing methodologies. The contribution of this research is significant in addressing critical challenges inherent in VLPR systems and achieving an enhanced performance standard.

Open Access
Research article
A Comprehensive Exploration of Resource Allocation Strategies within Vehicle Ad-Hoc Networks
sadashiviah sheela ,
kanathur ramaswamy nataraj ,
srikantaswamy mallikarjunaswamy
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Available online: 09-29-2023

Abstract

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In recent years, a surge in the utilisation of vehicle-to-vehicle (V2V) communication has been observed, serving as a pivotal factor in facilitating automatic control of vehicles without human intervention. This advancement has notably curtailed accident rates, mitigated traffic congestions, and augmented vehicular security. Consequently, a meticulous survey has been orchestrated in the domain of Vehicle Ad-Hoc Networks (VANETs), particularly as autonomous vehicles pervade urban landscapes. The necessity for resources to assure secure and consistent operations of an escalating fleet commensurately intensifies with the enlargement of the fleet itself. Intelligent Transportation Systems (ITS) hinge upon VANETs to furnish travellers with secure and pleasant journeys, pertinent information and entertainment, traffic management, route optimisation, and accident prevention. Nevertheless, a plethora of challenges inhibits the delivery of an adequate Quality of Service (QoS) within vehicular networks, such as congested and interrupted wireless channels, a progressively saturated and sprawling spectrum, hardware inconsistencies, and the swift expansion of vehicular communication systems. Contemporary networks and energy grids are subject to strain from daily and recreational activities. As demand perpetually ascends, a necessity arises for more refined tools and methodologies for resource management and a more precise distribution system. This investigation offers an exploration of the most recent practices and trends in VANET resource allocation, with the objective of garnering insights into the existing research landscape and its impelling forces.

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In this investigation, critical insights into the complex interactions between tyres and soil are explored through the utilization of nonlinear finite element analysis (FEA), bearing significant implications for vehicle dynamics, safety, and performance. Maximal shear stress values, identified through shear stress analyses, reveal a peak of 8.4 MPa in the tyre-road contact region and an approximately uniform shear stress of 1.703 MPa in alternative areas, laying the foundation for advancements in tyre design optimisation. It was demonstrated that tyre designs necessitate optimisation to specific ground materials to fulfil essential traction requirements and preclude sinking. For interfaces involving soil and neoprene rubber, the contact status at the mid-section zone was observed to be in a sticking condition, transitioning to sliding as the observation point moved away from the centre. The research highlighted that through nonlinear analysis, accurate predictions of tyre behaviour under fluctuating loads can be achieved, thereby aiding in the formulation of designs for more fuel-efficient tyres and enhanced wet-weather handling. However, the study recognises the constraints imposed by simplifications within the tyre model, omission of dynamic behavioural factors, and assumptions regarding unvarying friction coefficients. While the analysis was confined to particular material models and validation was executed primarily via numerical simulations, findings affirm that strategic application of nonlinear FEA elucidates pivotal factors in tyre-soil interaction, propelling the establishment of safer and more performance-oriented vehicle models.

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The role of transport infrastructure, especially railways, in shaping a nation's socio-economic and cultural dynamics is of paramount importance. The present research delves into the profound influence of the railway network on Iran's regional transformation, from its inception to present times. An in-depth historical evaluation uncovers the genesis and expansion of the Iranian railway system, linking it intricately with pivotal junctures in the nation's trajectory. Emphasis is placed on regions undergoing substantial developmental shifts, attributable to enhanced rail connectivity, offering distinct examples of varied growth paradigms. Economic repercussions manifest as interregional trade augmentation, resurgence of industries, and alterations in employment landscapes, thereby positing railways as an integral component of Iran's economic blueprint. Concurrently, an exhaustive scrutiny of socio-cultural realms underscores railways' pivotal role in fostering intercultural exchanges and expediting urbanisation trends. From an environmental perspective, the sustainability merits of rail transport are illuminated, accentuating the increasing pertinence of ecological considerations in railway's prospective expansion. Through meticulous case studies, a comparative narrative emerges between areas endowed with rail connectivity and those situated in relative isolation. The objective is to elucidate railways as instigators of transformative shifts. This study culminates with projections grounded in potential technological advancements poised to reshape Iran's railway infrastructure and the ensuing regional implications. Findings underscore railways' monumental impact on Iran's socio-economic fabric, illuminating their potential as change agents and offering invaluable insights for global infrastructure strategising.

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In the quest for autonomous vehicle safety and road infrastructure management, traffic sign recognition (TSR) remains paramount. Recent advancements in accuracy across various benchmarks have been identified in the literature concerning this essential task. Such technology might remain absent in older vehicles, while integration into Advanced Driver Assistance Systems (ADAS) is common in more recent models. Yet, the capability of these systems to function proficiently under diverse driving conditions has not been widely investigated. A framework has been devised to allow a moving vehicle to detect traffic signs, targeting the enhancement of driver safety and the diminishment of accidents. The present research introduces an innovative methodology, amalgamating the extreme learning machine (ELM) method with deep-learning paradigms, in response to experimental discoveries. As a pioneering computational approach in neural network-based learning, ELM facilitates rapid training and commendable generalization. An accuracy of 95.00% was achieved by the proposed model. By utilizing the Horse Herd Optimization method (HHOA), the memory consumption is minimized in the more sophisticated approach of stacked ELM (SELM) within the deep-learning framework. This study contributes to the understanding of potential challenges that may be encountered during TSR tasks, and lays the groundwork for future investigation by proffering a diverse set of evaluations for various road scenarios. Consistency in the utilization of professional terms is maintained throughout.

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Amidst the evolving dynamics of modern economic life, there emerges an escalating demand for faster modes of travel. In response, advancements in the realm of high-speed maglev train technology, targeting speeds of up to 600 km/h, are being persistently pursued in China. Central to ensuring the stable operation of these high-speed trains is a thorough understanding of the inherent magnetic fields and their electromagnetic interactions during high-speed transits. In this context, the Long Stator Linear Synchronous Motor (LSM) of Tongji University's maglev prototype is investigated. Through an analytical lens, a simplified model of LSM was dissected using the energy method. The distribution patterns of air gap magnetic flux were then ascertained through Fourier transformation coupled with the equivalent current layer, culminating in the derivation of a theoretical equation for electromagnetic forces. A two-dimensional finite element model subsequently shed light on the magnetic induction intensity distribution intricacies inherent to the long stator linear motor. Concurrently, potential end effects impinging on the motor's performance were explored. This comprehensive analysis further revealed the interplay between electromagnetic force, excitation current, and armature current. The observations encapsulated distinct magnetic field distribution patterns, nonlinear interdependencies between current and magnetic force, and pronounced saturation characteristics. Collectively, these findings furnish a robust theoretical scaffold for the simplification and optimization of electromagnetic forces in high-speed maglev systems.

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This study delves into the crucial task of embedding climate change resilience within the sphere of railway infrastructure planning and design in India. As climate change continues to threaten global transportation systems, the creation of robust, sustainable infrastructure becomes indispensable for minimizing its impacts. Initial investigation entails assessing both existing and anticipated climate change scenarios in India, encompassing elements like temperature fluctuations, changes in precipitation, and severe weather phenomena. Following this, the study proceeds to pinpoint the specific risks and vulnerabilities that the Indian railway system stands to confront due to these climatic shifts. A thorough exploration of current adaptation policies and strategies provides a framework to merge these into railway infrastructure planning and design, using a mix of literary review, best practices, and international case studies as resources. The Indian railway network undergoes a meticulous analysis to evaluate its vulnerability, leading to the identification of key adaptation measures like devising new railway tracks, enhancing the existing infrastructure, adopting resilience-based technologies, and implementing nature-centric solutions. The research probes the economic, social, and environmental ramifications of these measures, underlining the long-term sustainability and beneficial impacts on the transportation industry. Expert interviews, stakeholder consultations, and policy analysis culminate in a set of recommendations for policymakers, urban planners, and transportation authorities. These recommendations aim to shape the progression of a climate-resilient railway infrastructure in the light of India’s distinct challenges. Such an integration of climate change adaptation strategies contributes towards a more robust and sustainable transportation system. This study enriches the existing body of knowledge on climate change adaptation in transportation, offering valuable perspectives for policymakers, practitioners, and researchers aiming for climate resilience in the railway sector.

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