IMPROVING SERVICE QUALITY AT TRANSPORT HUBS THROUGH DYNAMIC PASSENGER FLOW MANAGEMENT

This article describes a methodology for dynamic passenger flow management at transport hubs using automatic passenger counting data and predictive models based on neural networks. This approach enables realtime service level assessment, forecasting transport demand, and identifying congested areas to regulate flows, improving service quality. The results are applicable to hub design, modernization, and the implementation of intelligent transport systems to optimize public transport