Resilient AI-Powered Transportation Systems Leveraging Convolutional and Deep Neural Networks
DOI:
https://doi.org/10.15662/IJARCST.2023.0602003Keywords:
AI-powered transportation, Resilient systems, Convolutional neural networks, Deep neural networks, Data pipelines, Predictive analytics, Traffic optimization, Real-time decision-making, Anomaly detection, Intelligent transport systemsAbstract
This paper presents a resilient AI-powered framework for transportation systems, leveraging convolutional neural networks (CNNs) and deep neural networks (DNNs) to enhance operational efficiency, safety, and real-time decision-making. Modern transportation networks generate vast amounts of heterogeneous data from sensors, cameras, and traffic management systems, requiring robust data pipelines and intelligent analytics for effective system management. The proposed framework employs CNNs for visual perception tasks, such as object detection and traffic monitoring, while DNNs handle complex predictive analytics, including demand forecasting, route optimization, and anomaly detection. Resilient data pipeline architectures ensure secure, scalable, and low-latency processing of high-volume data streams, maintaining system reliability under dynamic and uncertain conditions. Experimental results demonstrate improvements in traffic flow, incident response times, and predictive accuracy, highlighting the potential of combining deep learning with resilient AI frameworks to create intelligent, adaptive, and robust transportation ecosystems.
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