An end-to-end distributed deep learning system for real-time passenger flow measurement in transport interchanges

Abstract

As urban populations continue to grow, managing and optimizing urban mobility has become increasingly complex, especially in multimodal transport interchanges. Accurate passenger flow measurement has therefore become essential for operators to mitigate congestion and improve service efficiency. This work proposes a scalable and flexible end-to-end system designed to accurately measure and track passenger flow in real-time. The system integrates a distributed network of Edge-AI sensor nodes with deep learning algorithms for local passenger detection and tracking, while a central processing server aggregates node outputs to derive flow counts. This approach overcomes the limitations of traditional single-sensor solutions by effectively handling occlusion and complex spatial configurations across multiple access points. Validated in a high-transited transport hub, results show that the system achieves accuracy rates between 94.03% and 99.30% even under crowded conditions with flow rates of 100 persons per minute, demonstrating its robustness and practical applicability in dynamic, high-density environments.