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Intelligent Transportation Systems (ITS) have been identified as a key application of the Internet of Things (IoT). In order to reduce latency and bandwidth needs, edge computing offers a distributed computing architecture that can process and analyse data closer to the source. In this paper, we propose an edge computing framework for data analysis in ITS and present experimental results to evaluate its performance. The proposed framework aims to address the challenges associated with traditional data analysis techniques by leveraging the power of distributed computing resources, advanced analytics, and real-time processing capabilities. The rationale behind the framework's design and architecture is to improve the efficiency, scalability, and adaptability of ITS solutions, ensuring faster decision-making, reduced latency, and enhanced overall transportation performance.
Sharma et al. (Fri,) studied this question.