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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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Through the deployment of bibliometric techniques and network visualizations, this analysis synthesizes the evolution and trajectories of autonomous driving research from 2002 to May 2024, as captured in the Scopus database encompassing 342 scholarly documents. This study was conducted to delineate the developmental contours, thematic emphases, and the expansive growth trajectory within this field, particularly noting a surge in scholarly outputs since 2017. Such growth has been primarily facilitated by breakthroughs in artificial intelligence and sensor technologies, along with burgeoning interdisciplinary collaborations and escalating academic and industrial investments. A meticulous examination of publication trends, document types, subject areas, and geographic distributions elucidates the multidisciplinary and international nature of this burgeoning field. Key thematic clusters identified—comprising foundational technologies, environmental sustainability, safety measures, regulatory frameworks, user experience, connectivity, and technological innovations—underscore the prevailing research directions and emerging focal areas poised to shape future autonomous mobility solutions. Notably, increased emphasis on environmental sustainability and regulatory frameworks has been observed, highlighting their critical roles in advancing and integrating autonomous driving systems. This study provides pivotal insights for researchers, policymakers, and industry stakeholders, fostering a nuanced understanding of the field’s dynamics and facilitating strategic alignments and innovations in autonomous mobility. The emergent research domains and collaborative networks revealed herein not only map the current landscape but also guide future scholarly endeavors in autonomous driving systems globally.

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In the field of pedestrian re-identification (ReID), the challenge of matching occluded pedestrian images with holistic images across different camera views is significant. Traditional approaches have predominantly addressed non-pedestrian occlusions, neglecting other prevalent forms such as motion blur resulting from rapid pedestrian movement or camera focus discrepancies. This study introduces the MotionBlur module, a novel data augmentation strategy designed to enhance model performance under these specific conditions. Appropriate regions are selected on the original image for the application of convolutional blurring operations, which are characterized by predetermined lengths and frequencies of displacement. This method effectively simulates the common occurrence of motion blur observed in real-world scenarios. Moreover, the incorporation of multiple directional blurring accounts for a variety of potential situations within the dataset, thereby increasing the robustness of the data augmentation. Experimental evaluations conducted on datasets containing both occluded and holistic pedestrian images have demonstrated that models augmented with the MotionBlur module surpass existing methods in overall performance.

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To address the lack of multi-perspective, real-time monitoring and management of operations and equipment in automated container terminals, a digital twin system targeted at monitoring automated container terminal equipment has been designed and developed. Based on the concept of a five-dimensional model of digital twins, a digital twin framework for monitoring automated container terminal equipment was constructed. The system's maintainability is enhanced through a layered design, which also reduces coupling between different functional modules. A multi-dimensional, multi-scale virtual scene was built and model consistency evaluations were conducted to verify the system. The system's operational efficiency was improved by optimizing model rendering with discrete level of detail (LOD) techniques. A multi-layered distributed solution for the digital twin system was proposed to achieve multi-perspective monitoring. Ultimately, using a specific automated container terminal as a case study, a system prototype was developed, realizing multi-perspective digital monitoring of terminal operations and equipment. This project offers a solution for the application of digital twin technology in the field of automated container terminals and promotes the development of intelligent digital terminals.

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The acceleration of urbanization and the consequent increase in population have exacerbated urban road traffic issues, such as congestion, frequent accidents, and vehicle violations, posing significant challenges to urban development. Traditional manual traffic management methods are proving inadequate in meeting the demands of rapidly evolving urban environments, necessitating an enhancement in the intelligence level of urban road traffic management systems. Recent advancements in computer vision and deep learning technologies have highlighted the potential of image processing and machine learning-based traffic management systems. In this context, the application of object detection and tracking technologies, particularly the YOLOv5 and Deep learning-based Simple Online and Realtime Tracking (DeepSORT) algorithms, has emerged as a pivotal approach for the intelligent management of urban traffic. This study employs these advanced object detection and tracking technologies to identify, classify, track, and measure vehicles on the road through video analysis, thereby providing robust support for urban traffic management decisions and planning. Utilizing digital twin technology, a virtual replica of traffic flow is constructed from camera data, serving as the dataset for training different YOLOv5 algorithm variants (YOLOv5s, YOLOv5m, and YOLOv5l). Upon comparison of training outcomes, the YOLOv5s model is selected for vehicle detection and recognition in video feeds. Subsequently, the DeepSORT algorithm is applied for vehicle tracking and matching, facilitating the calculation of vehicles' average speed based on tracking data and the temporal interval between adjacent frames. Results, stored in Comma-Separated Value (CSV) format for future analysis, indicate that the system is capable of accurately identifying, tracking, and computing the average speed of vehicles across various traffic scenarios, thereby significantly supporting urban traffic management and advancing the intelligent development of urban road traffic. This approach underscores the critical role of integrating cutting-edge object detection and tracking technologies with digital twin models in enhancing urban traffic management systems.

Open Access
Research article
Evaluating the Road Environment Through the Lens of Professional Drivers: A Traffic Safety Perspective
aleksandar trifunović ,
aleksandar senić ,
svetlana čičević ,
tijana ivanišević ,
vedran vukšić ,
sreten simović
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Available online: 03-03-2024

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In the context of traffic safety, the interplay between the road environment and the human factor emerges as a critical determinant of the severity of road crash consequences. This study was designed to explore the perceptions of professional drivers regarding the road environment, with a particular focus on the elements that either contribute to or mitigate safety risks. A comprehensive survey was conducted, wherein 118 professional drivers from the Republic of Serbia were asked to rate photographs depicting various road environments in terms of safety. The investigation aimed to elucidate the extent to which these drivers recognize and assess road hazards, as well as to examine potential variations in their evaluations based on demographic characteristics. The findings underscore the significant impact of the road environment on traffic safety, particularly highlighting the role of solid obstacles such as trees, pillars, and masonry objects. When vehicles veer off the road, collisions with these obstacles frequently result in exacerbated outcomes of road crashes. The methodology employed in this research involved a quantitative analysis of the survey responses, ensuring a systematic evaluation of the drivers' perceptions. The study contributes to the existing body of knowledge by offering insights into the evaluative processes of professional drivers concerning the road environment, thereby informing strategies aimed at enhancing driver safety.

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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

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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.

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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

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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

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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.

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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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