Edge Computing Framework for Real-Time Traffic Prediction

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@article{1723981,
      author = {Ravi Kumar Sharma, Priya Nair, Aditya Prakash Singh, Tharun Pagadala},
      title = {Edge Computing Framework for Real-Time Traffic Prediction},
      journal = {Iconic Research And Engineering Journals},
      year = {2024},
      volume = {8},
      number = {6},
      pages = {1358-1364},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1723981.pdf},
      abstract = {With the rapid pace of urbanization, the expansion of the number of vehicles, and the requirement for more efficient management of traffic and to improve the quality of life in cities, intelligent traffic management systems are now a must in smart cities. In this paper, we propose an edge computing framework for real-time traffic prediction (ECF-RTP) to process traffic data streams in near-real time with minimum latency using distributed edge nodes. The proposed architecture combines the light-weight convolutional neural networks (LCNN) and a spatiotemporal graph attention mechanism over a hierarchical edge-cloud continuum. This system will allow the offloading of computation to edge nodes placed at strategic traffic intersections, which will result in much lower round-trip latencies than centralized cloud-based systems. A formal system model is defined and then the design and implementation of the ECF-RTP scheme based on federated learning for maintaining data privacy at distributed nodes. Results on real-world traffic datasets collected from three major metropolitan corridors in India have shown the framework's ability to forecast traffic flows with less than 100ms of prediction latency, and a 62% reduction in traffic backhauls bandwidth consumption compared to traditional cloud-only architectures with short-term accuracy of 94.3%.},
      keywords = {edge computing; traffic prediction; federated learning; spatiotemporal graph networks; smart city; real-time systems},
      month = {December},
      doi = {https://doi.org/10.64388/IREV8I6-1723981}
  }