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dc.contributor.authorWu, Fei
dc.contributor.authorZheng, Changjiang
dc.contributor.authorDu, Muqing
dc.contributor.authorMa, Genghua
dc.contributor.authorWu, Zhilong
dc.date.accessioned2026-08-20T20:53:26Z
dc.date.available2026-08-20T20:53:26Z
dc.date.copyright2025/06/03
dc.date.issued2025
dc.identifier.otherJTEPBS.TEENG-8964.pdf
dc.identifier.urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4313624
dc.description.abstractAbstractIntelligent transportation management in metro systems requires accurate and real-time predictions of passenger flow demand. However, current research has mainly focused on static metro systems, overlooking the system’s long-term continuous ...Practical ApplicationsThis research introduces IMGSN, an innovative prediction system designed for metro operators to better manage passenger flow. The system offers two key practical benefits: First, it automatically updates its predictions as the metro ...
dc.publisherAmerican Society of Civil Engineers
dc.titleAdaptive Passenger Flow Prediction in Metro Systems: An Incremental Learning Framework for Evolving Patterns
dc.typeJournal Article
dc.journal.volume151
dc.journal.issue8
dc.journal.titleJournal of Transportation Engineering, Part A: Systems
dc.identifier.doi10.1061/JTEPBS.TEENG-8964
dc.journal.fristpage04025058-1
dc.journal.lastpage04025058-19
dc.page19
dc.treeJournal of Transportation Engineering, Part A: Systems:;2025:;Volume ( 151 ):;issue: 008
dc.contenttypeFulltext


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