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

dc.contributor.authorZhong Zheng
dc.contributor.authorWanxian He
dc.contributor.authorGanglan Wei
dc.date.accessioned2026-05-08T19:26:06Z
dc.date.issued2026-1-14
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.doi10.3233/atde251568
dc.identifier.urihttps://dspace.kmitl.ac.th/handle/123456789/20421
dc.publisherAdvances in transdisciplinary engineering
dc.subjectTraffic Prediction and Management Techniques
dc.subjectTraffic control and management
dc.subjectAdvanced Data and IoT Technologies
dc.titleAI-Enhanced Traffic Flow Monitoring Algorithm Model for Highway Logistics Transportation Vehicles Based on ETC Gantry Marking Technology
dc.typeBook-chapter

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