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dc.contributor.authorLeu, Pei-Yu
dc.contributor.authorHaroon, Saquib M.
dc.contributor.authorKhadka, Sunil
dc.contributor.authorRyan, Alyssa
dc.contributor.authorZhang, Liang
dc.date.accessioned2026-08-20T20:58:05Z
dc.date.available2026-08-20T20:58:05Z
dc.date.copyright2026/05/08
dc.date.issued2026
dc.identifier.otherJTEPBS.TEENG-9533.pdf
dc.identifier.urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4313745
dc.description.abstractAbstractTransportation professionals increasingly rely on large data sets to understand travel behavior and improve system performance. However, many agencies face challenges accessing and using these data due to limited programming experience and complex ...Practical ApplicationsTransportation agencies and practitioners are increasingly working with large and diverse data sets, such as expansive vehicle speed, volume, and pedestrian activity data sets. However, many agencies, particularly at the local level, ...
dc.publisherAmerican Society of Civil Engineers
dc.titleLeveraging AI Techniques to Facilitate User-Friendly Transportation Data Retrieval for Speed and Volume Data
dc.typeJournal Article
dc.journal.volume152
dc.journal.issue7
dc.journal.titleJournal of Transportation Engineering, Part A: Systems
dc.identifier.doi10.1061/JTEPBS.TEENG-9533
dc.journal.fristpage04026043-1
dc.journal.lastpage04026043-10
dc.page10
dc.treeJournal of Transportation Engineering, Part A: Systems:;2026:;Volume ( 152 ):;issue: 007
dc.contenttypeFulltext


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