A Hybrid IoV-MEC-AI Framework for Road Safety: An Ex Ante Feasibility Assessment on the General Rumiñahui Highway

Road traffic crashes remain a major public safety concern in Ecuador, particularly on the General Rumiñahui Highway, which is a high-demand mountainous corridor characterized by a complex road geometry, variable traffic conditions, changing environmental conditions, and limited proactive early warning capabilities. This study proposes a hybrid Internet of Vehicles (IoV)–multi-access edge computing (MEC)–artificial intelligence (AI) framework and assesses its technical and contextual feasibility through an ex ante analysis of the target corridor. The three-layer architecture integrates roadside, vehicular, and environmental sensing for data acquisition; MEC-enabled local processing, YOLOv8-based visual perception, and V2V, V2I, and V2P communications at the edge; and cloud-based spatiotemporal crash risk estimation using a CNN-LSTM model. ISO 37106:2021 and ISO 37122:2019 are adopted as design references for interoperability, smart city integration, and mobility-related monitoring. The feasibility assessment combines evidence from functionally comparable IoV, computer vision, predictive modeling, and MEC/V2X studies with an analysis of candidate Ecuadorian institutional data sources and corridor-specific deployment constraints. Literature-derived performance metrics are used only as feasibility evidence and are not interpreted as empirical measurements or the expected performance of the proposed framework. The results indicate that the architecture is technically plausible and contextually feasible under explicit conditions, providing a structured basis for Phase II digital twin evaluation followed by an instrumented field pilot under real operating conditions.