NOVEL SYSTEM FOR COMPLIANCE MONITORING OF SOUTH AFRICAN CROSS-BORDER TRANSPORT

Abstract

Cross-border road transport corridors form a vital element of regional economies. To remain globally competitive, compliance enforcement must be reconciled with streamlined trade flows. This requires the use of IoT (Internet-of-things) technology to collect data in real time from ongoing operations, and AI (artificial intelligence) applied to complex data sets to implement intelligent Green Lane systems. These techniques must be applied to vehicle, route, overload control, customs, and transport operator management system data to non-intrusively verify compliance in each functional area. Data from different sources must be combined using matching identifiers and interpreted against the background of trade and transport regulations. Statistical techniques are utilised to identify non-compliant behaviour, ensuring that only compliant operators can apply for cross-border permits, and enabling preferential treatment based on compliance scores. Compliance outcomes are accessible through a cloud-based web dashboard, allowing both commercial operators and cross-border authorities to observe compliance behaviour and take corrective action in real time. The system provides decision support by explaining the reasons for allocated compliance scores and by comparing the compliance ratings of different vehicles and operators. The incentives provided to operators will enable the simultaneous improvement of trade efficiencies and trade compliance, resulting in improved economic performance of the region.