Integrated 5G-IoT-AI Digital Escort Model for Trans-Caspian Freight Logistics: Simulation and Validation

Freight logistics on the Trans-Caspian International Transport Route (Middle Corridor) suffer from long dwell times, limited cargo visibility and frequent incidents. This study quantifies how a digital escort of freight, built on a four-layer 5G-IoT-AI architecture, could change the key performance indicators of the Kazakhstan section of the corridor. Each architectural layer was mapped to a measurable mechanism and coefficient, and three segments of a Kazakhstan freight forwarder’s chain were simulated, including the railway corridor (Monte Carlo simulation, N = 10,000), the port of Aktau (discrete-event simulation) and China–Europe transit, compared with a route attractiveness index. In the baseline digital scenario, railway lead time fell by 6.3% (Cohen’s d = 1.26) and expected incidents per container by 35.0%. At the port, operational errors decreased by 34.8%, whereas dwell time decreased by only 3.2% because ferry waiting time remained the dominant contributor to overall port dwell time.r. A factorial analysis showed that AI-supported processing drives lead time, IoT monitoring drives incidents and variability, and 5G affects only response time. With the current monitored share of about 35%, the lead-time gain fell below the practical-relevance threshold. The Kazakhstan route ranked first for 95.8% of 100,000 random weight vectors and remained first when the visibility criterion was removed.