A system for Real-time anomaly detection and trip rating in taxi driving that uses vehicle telemetry data such as speed, acceleration, yaw rate, steering angle, and GPS, which allows fair and consistent safety evaluation and helps fleet operators monitor and improve service quality.
IYOLO, an enhanced YOLOv8-based framework for simultaneous detection and classification of vehicles, drivers, and passengers on highways, aiming to distinguish drivers from passengers and establish one-to-one vehicle-driver associations is proposed.
Yang Zhang, Peihua Lv, Hongjin Ren et al.· International Conference on...· 0 citations
These findings demonstrate that RF–Bayesian provides a stable, interpretable, and computationally efficient framework for smartphone-based driver behavior classification, with practical relevance for telematics, fleet safety management, driver feedback systems, and intelligent transportation safety applications.
A.A. Al-Rababah, S. M. Rahman· Neural computing & applicati...· 0 citations
This paper reviews QAR data-driven methods for flight anomaly detection and risk warning from the perspective of statistical learning and aviation data science and provides a structured reference for using QAR data to support aviation safety assessment, risk warning, and operational decision-making.
Fang Wang, Yixin Zhang, Yongzheng Wang et al.· Aerospace· 0 citations
This research establishes a statistically robust and deployable foundation for next-generation intelligent transportation systems by coupling Bayesian learning theory with edge computing design.
S. M. Hosseini, V. Kiani, Hadi Sadoghi-Yazdi· Computing· 0 citations
This research aims to provide a smart city architecture that can detect accidents and track traffic in realtime using edge-cloud computing, deep learning-based video analytics, and IoT sensing and exhibits low response time, robustness under varying traffic and lighting conditions, and outstanding detection accuracy.
R. Elankavi, Imran Alam, Mogadala Mounika et al.· ITM Web of Conferences· 0 citations
The methodology offers a cost-effective, privacy-preserving, and interpretable approach for congestion monitoring using ubiquitous smartphone sensors, with real-time, edge-deployable inference and applications in Advanced Traveler Information Systems (ATIS) and intelligent transportation systems in resource-constrained...
