The rapid growth of autonomous and Connected Vehicles (CVs) has led to a massive increase in Vehicular Big Data (VBD). While this data is transforming the Intelligent Transportation System (ITS), it also poses significant challenges in processing, communication, and resource scalability. Existing cloud solutions offer scalable resources; however, incur long delays and costs due to distant data communication. Conversely, edge computing reduces latency by processing data closer to the source; however, struggles to scale with the high volume and velocity of VBD. This paper introduces a novel Regional Computing (RC) paradigm for VBD offloading, with a key focus on adapting to traffic variations during peak and off-peak hours. Situated between edge and cloud layers, the RC layer enables near-source processing while maintaining higher capacity than edge or fog nodes. We propose a dynamic offloading algorithm that continuously monitors workload intensity, network utilization, and temporal traffic patterns to smartly offload tasks to the optimal tier (vehicle, regional, or cloud). This strategy ensures responsiveness across fluctuating conditions while minimizing delay, congestion, and energy consumption. To validate the proposed architecture, we develop a custom Python-based simulator, RegionalEdgeSimPy, specifically designed for VBD scenarios. Simulation results demonstrate that the proposed framework significantly reduces processing latency, energy usage, and operational costs compared to traditional models, offering a scalable and effective alternative for next-generation vehicular networks.
Badshah et al. (Wed,) studied this question.