Integration of an intelligent humanoid system into road traffic management at the Roads Agency: An approach based on DevOps knowledge-based systems and Big Data technologies

Abstract

Road traffic management requires information that is rapid, reliable and actionable for operational managers. This article proposes an architecture to support road surveillance, combining a humanoid device, a knowledge base, artificial intelligence models, a Big Data platform and a DevOps/MLOps pipeline. The approach falls within the scope of Design Science Research and comprises a targeted literature review, the formulation of requirements, architectural design and the definition of an evaluation protocol. The proposed system integrates field perception, edge processing, event ingestion, hybrid inference and human supervision. The numerical evaluation is limited to illustrative values and a fictitious confusion matrix comprising 1,000 events. Recalculation of this matrix yields an accuracy of 91.00 per cent, a precision of 91.11 per cent, a recall of 89.13 per cent and an F1 score of 90.11 per cent. These values demonstrate the calculation of the indicators, without establishing the operational effectiveness of the system. The analysis highlights the benefits of gradual integration and the need to compare the humanoid’s specific contribution with that of a fixed installation. The main contribution is a traceable design framework for road decision support. Its validation requires local data, reproducible trials, a controlled comparison and clarification of institutional responsibilities.

Keywords: Intelligent humanoid system; road traffic; knowledge-based system; artificial intelligence; Big Data; DevOps; MLOps; human supervision; Office des Routes.

Received Date: August 17, 2026

Accepted Date: September 09, 2026

Published Date: October 01, 2026

Available Online at: https://www.ijsrisjournal.com/index.php/ojsfiles/article/view/978