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.
