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Research Article
State-space Modeling and Data Analysis of Electric Two-wheeler Performance Metrics in the American Market
Issue:
Volume 10, Issue 6, December 2025
Pages:
135-149
Received:
24 June 2025
Accepted:
13 August 2025
Published:
9 December 2025
Abstract: Electric two-wheelers have emerged as a pivotal segment in the global EV revolution, especially in densely populated and urbanized regions where compact mobility solutions are in high demand. In the United States, while the adoption of four-wheeled electric vehicles has seen extensive research, the performance dynamics of electric two-wheelers remains underexplored. This research addresses this gap by developing regression-based state-space models to investigate key performance parameters. The study applies multiple regression models (linear, quadratic, cubic) to derive functional relationships between variables such as battery capacity, motor power, acceleration, range, and price. We aim to identify and quantify the interrelationships between key design and performance parameters, including battery capacity, motor power, acceleration, range, and base price. By employing regression-based state-space modeling with linear, quadratic, and cubic formulations, we extract functional patterns that shape the behavior and market positioning of these vehicles. Our data-centric methodology offers critical insights into how technical specifications influence affordability and adoption potential, particularly in the context of urban mobility. This work advances the broader discourse on electric vehicle innovation by spotlighting lightweight electric mobility tailored to American cityscapes. The findings have potential implications for manufacturers, policymakers, and urban planners seeking sustainable alternatives to car-centric infrastructure. As consumer interest in cost-effective and energy-efficient transport grows, understanding these relationships becomes essential for guiding future design and investment strategies.
Abstract: Electric two-wheelers have emerged as a pivotal segment in the global EV revolution, especially in densely populated and urbanized regions where compact mobility solutions are in high demand. In the United States, while the adoption of four-wheeled electric vehicles has seen extensive research, the performance dynamics of electric two-wheelers rema...
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Research Article
Quantitative Analysis of Crack Growth and Severity in Reinforced Concrete Structures Using Deep Learning and Computer Vision
Kalyan Naik Vankudothu*
,
Wisam Bukaita
Issue:
Volume 10, Issue 6, December 2025
Pages:
150-167
Received:
19 November 2025
Accepted:
1 December 2025
Published:
31 December 2025
DOI:
10.11648/j.ajtte.20251006.12
Downloads:
Views:
Abstract: The accurate and objective assessment of reinforced concrete structures is paramount for maintaining structural integrity and optimizing long-term maintenance planning. This study introduces a unified deep learning and computer vision framework designed for the automated detection, classification, and standards-aligned quantitative analysis of concrete cracks. The methodology begins with the automated categorization of an approximately 7,000-image concrete surface dataset into seven specific defect types including Thermal, Serviceability, and Strength Failure Cracks based on geometric metrics like crack length and width. This automated pre-classification step successfully mitigates the subjectivity and inconsistency associated with traditional manual labeling, providing a robust foundation for model training. A Convolutional Neural Network (CNN), implemented using Python, TensorFlow, and Keras, was trained over 50 epochs to detect and classify these categorized defects. The model achieved a final classification accuracy of 91.1%, demonstrating strong generalization and outperforming models trained on unrefined datasets. Following detection, a quantitative damage measurement module utilizes Otsu thresholding, morphological filtering, and skeletonization to precisely extract geometric parameters. Automated functions estimated key crack metrics, including length (5–180mm) and width (0.2–4.5mm), and surface deterioration percentage. These measurements are used to assign a severity grade (minor, moderate, or severe), aligned with established ACI 224R-01 and ACI 318-19 guidelines. Visualization techniques, such as severity-based color coding and multi-panel views, enhance the interpretability and validate both the detection accuracy and measurement reliability. By integrating automated data refinement, CNN-based recognition, and objective standards-aligned quantitative assessment, this framework provides a scalable and reliable tool for real-time structural health monitoring.
Abstract: The accurate and objective assessment of reinforced concrete structures is paramount for maintaining structural integrity and optimizing long-term maintenance planning. This study introduces a unified deep learning and computer vision framework designed for the automated detection, classification, and standards-aligned quantitative analysis of conc...
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Research Article
A Framework for the TTE-less Railway: An End-to-end Operational Model for Automated Verification, Passenger Safety, and Service Management
Partha Majumdar*
Issue:
Volume 10, Issue 6, December 2025
Pages:
168-182
Received:
24 November 2025
Accepted:
15 December 2025
Published:
31 December 2025
DOI:
10.11648/j.ajtte.20251006.13
Downloads:
Views:
Abstract: This analysis presents a comprehensive, end-to-end operational model for a railway system without Travelling Ticket Examiners (TTEs), arguing that a simple technology replacement for ticket checking is fundamentally flawed. An operational deconstruction reveals the TTE's primary functions are not enforcement but the management of on-board safety, security, and passenger service, which cannot be automated. Consequently, a successful solution must be an integrated, four-part socio-technical system. The first phase, Automated Perimeter Control, establishes station-level access using a matrix of validation technologies from NFC smartcards to biometric gateways. Still, this model fails in open networks with unstaffed platforms. To address this, the second phase introduces the 'Intelligent Carriage', a layer of in-transit monitoring using service-specific technology: IoT-based seat sensor grids with passenger-facing "traffic light" indicators for reserved-seating trains, and privacy-compliant, anonymous AI-driven Automated Passenger Counting for unreserved commuter cars. The third phase is the 'Central Nervous System', a high-concurrency, real-time data architecture modelled on China’s Passenger Service Record (PSR) system. This "brain" fuses live sensor feeds with the ticketing database to create an automated exception-handling system, flagging discrepancies like an "occupied but unbooked" seat. The final, critical phase addresses the non-automatable human element. It proposes that the TTE-less train is not unstaffed; instead, the enforcement-focused TTE is replaced by a service-and-safety-focused 'Passenger Welcome Host'. This new role does not proactively check tickets but responds only to system-generated alerts, while primarily focusing on high-value tasks such as passenger assistance, accessibility services, conflict de-escalation, and emergency response. This framework mandates robust solutions to bridge the digital divide for unbanked or non-smartphone users through cash-accepting kiosks. The business case shifts from labour savings to achieving near-total revenue protection, enhanced operational efficiency through rich data streams, and improved passenger throughput.
Abstract: This analysis presents a comprehensive, end-to-end operational model for a railway system without Travelling Ticket Examiners (TTEs), arguing that a simple technology replacement for ticket checking is fundamentally flawed. An operational deconstruction reveals the TTE's primary functions are not enforcement but the management of on-board safety, s...
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