AI-Enhanced Traffic Flow Monitoring Algorithm Model for Highway Logistics Transportation Vehicles Based on ETC Gantry Marking Technology

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dc.contributor.authorZheng, Zhong
dc.contributor.authorHe, Wanxian
dc.contributor.authorWei, Ganglan
dc.date.accessioned2026-08-06T10:54:28Z
dc.date.available2026-08-06T10:54:28Z
dc.date.issued2026-01-14
dc.description.abstractFacing the challenges of real-time and accuracy in traffic monitoring technology. This article proposes a traffic flow monitoring algorithm model for highway logistics vehicles and all types of vehicles based on ETC gantry recognition technology. By constructing a gantry road network direction map, real-time driving record table, and anomaly detection algorithm, vehicle path tracking, traffic statistics, and abnormal behavior recognition are achieved. This model fully utilizes the data collection capability of the existing ETC gantry system, combined with the shortest path algorithm and spatiotemporal relationship analysis, to explore the feasibility of this model in real-time traffic monitoring, and is expected to provide a low-cost reference solution for intelligent highway management.
dc.identifier.citationAdvances in Transdisciplinary Engineering, 85, 164-172, 2026
dc.identifier.doi10.3233/ATDE251568
dc.identifier.issn2352751X
dc.identifier.other2-s2.0-105028163157
dc.identifier.urihttps://dspace.kmitl.ac.th/handle/123456789/17827
dc.sourceAdvances in Transdisciplinary Engineering
dc.subjectAlgorithmic model
dc.subjectAnomaly detection
dc.subjectartificial intelligence algorithm
dc.subjectGantry marking technology
dc.subjectTraffic monitoring
dc.titleAI-Enhanced Traffic Flow Monitoring Algorithm Model for Highway Logistics Transportation Vehicles Based on ETC Gantry Marking Technology
dc.typeConference Paper

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