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.
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.
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.
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.
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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