Authors : Bebe Bebe Ngwaba; Augustin Pambi Tadiamba

Volume/Issue : Volume 11 - 2026, Issue 7 - July

Google Scholar : https://tinyurl.com/y5f25u6y

Scribd : https://tinyurl.com/4fn8c9vb

DOI : https://doi.org/10.38124/ijisrt/26jul347

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : Traffic-violation monitoring in Kinshasa is constrained by discontinuous controls, fragmented case records, and limited traceability. This study develops a socio-technical framework for a traceable traffic-violation intelligence system and tests the feasibility of an analytical prototype in Gombe and Barumbu. A convergent mixed-methods design combined 200 questionnaires, 18 semi-structured interviews, 24 observation sessions, and 22 documentary sources. These materials were triangulated to identify operational needs, institutional constraints, and functional requirements. An anonymised experimental dataset of 500 cases was then used to test a supervised classification workflow for predicting violation-severity levels (low, medium, and high). The prototype used one-hot encoding, a stratified 75/25 train-test split, and a Random Forest classifier with 150 trees and balanced class weights. The field evidence shows distinct local profiles: Gombe records more dynamic violations, particularly speeding and red-light running, whereas Barumbu presents more illegal stopping, road-use conflicts, and overloading. The most pronounced barriers are insufficient digital tools, weak archiving, limited staff training, poor coordination, and unreliable energy or connectivity.

Keywords : Traffic-Violation Monitoring; Data Governance; Machine Learning; Computer Vision; Random Forest; Kinshasa.

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