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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.<\/jats:p>","DOI":"10.1007\/s10489-025-06954-9","type":"journal-article","created":{"date-parts":[[2025,11,6]],"date-time":"2025-11-06T12:23:35Z","timestamp":1762431815000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["An end-to-end distributed deep learning system for real-time passenger flow measurement in transport interchanges"],"prefix":"10.1007","volume":"55","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0433-8941","authenticated-orcid":false,"given":"Eduardo","family":"Salas","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8367-2934","authenticated-orcid":false,"given":"Pedro J.","family":"Navarro","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3311-8414","authenticated-orcid":false,"given":"Francisca","family":"Rosique","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1578-0188","authenticated-orcid":false,"given":"Juan","family":"Benavente","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7266-3124","authenticated-orcid":false,"given":"Ana Mar\u00eda","family":"Rivadeneira","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,11,6]]},"reference":[{"key":"6954_CR1","volume-title":"World Cities Report 2022: Envisaging the Future of Cities, United Nations Human Settlements Programme (UN-Habitat)","author":"N Khor","year":"2022","unstructured":"Khor N, Arimah B, Otieno R, Oostrum M, Mutinda M, Martins J, Arku G, Cast\u00e1n Broto V, Chatwin M, Dijkstra L et al (2022) World Cities Report 2022: Envisaging the Future of Cities, United Nations Human Settlements Programme (UN-Habitat). 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All image captures were processed on-device by Edge-AI cameras, without transmission or storage of raw video outside the device, and without identifying or tracking any individual beyond a transient, anonymous count. Therefore, institutional ethics committee approval for human research was not required.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Research involving Human Participants and\/or Animals"}},{"value":"The sensors operated in a public space where there is no reasonable expectation of privacy and no images were recorded that would allow personal identification. Consequently, obtaining individual informed consent was neither feasible nor required. All material captured exclusively for model training was immediately anonymized, used only for bounding-box annotation, and securely deleted after labeling was complete.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Informed Consent"}},{"value":"To quantify metrics such as precision, recall, F1-score, and overall accuracy, two-minute video recordings were made with the installed sensors during periods of both high and low passenger flow. These recordings were used solely to simulate the system and extract aggregated passenger-flow data; at no point were faces or other identifiable features manually reviewed. All videos were encrypted and stored on a local, access-restricted server and retained only for the duration of the metric computation process, after which they were permanently destroyed.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Data Recording for System Performance Evaluation"}}],"article-number":"1078"}}