{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,23]],"date-time":"2026-10-23T21:13:36Z","timestamp":1792790016508,"version":"4.5.0"},"reference-count":55,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2026,10,6]],"date-time":"2026-10-06T00:00:00Z","timestamp":1791244800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,10,6]],"date-time":"2026-10-06T00:00:00Z","timestamp":1791244800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["U2436601"],"award-info":[{"award-number":["U2436601"]}],"id":[{"id":"https:\/\/ror.org\/01h0zpd94","id-type":"ROR","asserted-by":"crossref"}]},{"award":["U2436601"],"award-info":[{"award-number":["U2436601"]}],"id":[{"id":"https:\/\/ror.org\/01h0zpd94","id-type":"ROR","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Urban rail passenger flow forecasting, based on large-scale automatic fare collection (AFC) sensor networks, is typically evaluated on ordinary days, yet operators most need accurate predictions during event days. The AFC infrastructure comprises 191 sensor-equipped stations with turnstile transducers that generate 95.7 million discrete sensing events (inboard\/outboard readings) over 57 days, constituting a high-velocity, multimodal spatiotemporal sensor stream. This study presents a topology-driven adaptive graph network (TDAG-Net) for event-day forecasting across a full metro system. The model learns spatial dependencies end-to-end by merging multi-scale temporal convolution with an adaptive graph attention branch through learned gates. An attention long short-term memory (LSTM) then decodes 60-min forecasts for all 191 stations at once. A ticket\u2013persona module reads 25 fare channels as four rider types to decompose demand surges by traveler profile. The proposed model reduces root mean square error (RMSE) by 9.29% and achieves the best performance on 22 flagged event days. Ablation reveals that adaptive adjacency is the dominant component and reduces horizon decay to 17.3% from 33.0%. Counter to expectation, persona-specific graphs are 98.55% identical, and splitting inputs by rider type degrades accuracy by 10.36% RMSE. On event peaks, the tourist\/visitor share rises from 5.7% to 16.5%, indicating that event-day crowd management should prioritize unfamiliar riders over regular commuters.<\/jats:p>","DOI":"10.3390\/s26196316","type":"journal-article","created":{"date-parts":[[2026,10,6]],"date-time":"2026-10-06T16:21:46Z","timestamp":1791303706000},"page":"6316","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Adaptive Graph Learning with Persona-Aware Attribution for Event-Resilient Metro Passenger Flow Prediction"],"prefix":"10.3390","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-1277-0949","authenticated-orcid":false,"given":"Ahmed","family":"Raza","sequence":"first","affiliation":[{"name":"School of Electronic and Information Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4729-7406","authenticated-orcid":false,"given":"Guangjie","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Electronic and Information Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jie","family":"Cheng","sequence":"additional","affiliation":[{"name":"School of Electronic and Information Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Weiqian","family":"Wu","sequence":"additional","affiliation":[{"name":"School of Electronic and Information Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8543-8439","authenticated-orcid":false,"given":"Muhammad Usman","family":"Shoukat","sequence":"additional","affiliation":[{"name":"Energy and Transportation Domain, Beijing Institute of Technology, Zhuhai 519088, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,10,6]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Wang, N., Dong, W., Liang, X., Guo, J., and Dong, Y. 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