Proposed GNN-RMNet training accuracy and loss.

<div><p>Logistics networks are becoming increasingly complex and rely more heavily on real-time vehicle data, necessitating intelligent systems to monitor driver behavior and identify route anomalies. Traditional techniques struggle to capture the dynamic spatiotemporal relationships tha...

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Bibliographic Details
Main Author:	Eman Ali Aldhahri (22008594) (author)
Other Authors:	Abdulwahab Ali Almazroi (16436786) (author), Monagi Hassan Alkinani (22008597) (author), Mohammed Alqarni (11936462) (author), Elham Abdullah Alghamdi (22008600) (author), Nasir Ayub (17200014) (author)
Published:	2025
Subjects:	Science Policy Space Science Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified traditional techniques struggle multimodal sensor integration hybrid models shows future deployment concerns framework incorporates multimodal dynamic spatiotemporal relationships becoming increasingly complex anomaly scoring interpretability 32 ms ). vehicle sensor streams vehicle sensor dataset define driver actions structured driving data route deviation sensitivity route anomaly detection reduced inference latency identify route anomalies necessitating intelligent systems intelligent transportation systems monitor driver behavior rmnet seems promising preserving mobilenet components paper introduces gnn time vehicle data logistics </ p scalable driver analytics vehicle inference driver behavior route violations route optimization route deviations gps analytics gnn analytics data privacy world gps relational patterns real time operational inefficiencies learn contextual including cybersecurity generalization advantages edge nodes computational feasibility comparing gnn combines gnn big fleets aware logistics 96 %) 95 %). 5 %,
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Proposed GNN-RMNet training accuracy and loss.

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