Loss Comparison: Optimized vs Non-Optimized.

<div><p>With the acceleration of urbanization and the increase in traffic volume, frequent traffic accidents have significantly impacted public safety and socio-economic conditions. Traditional methods for predicting traffic accidents often overlook spatiotemporal features and the comple...

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التفاصيل البيبلوغرافية
المؤلف الرئيسي:	Hao Li (31608) (author)
مؤلفون آخرون:	Linbing Chen (21238795) (author)
منشور في:	2025
الموضوعات:	Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified road network relationships providing robust support offering theoretical support new technical approach experimental results demonstrate complex spatial structure captures temporal dependencies term memory networks graph neural networks intelligent traffic management frequent traffic accidents complex traffic environments trajectories using lstm model &# 8217 innovative combined model traditional prediction methods insufficient prediction accuracy deep learning model traffic networks traditional methods deep learning traffic volume lstm ), xlink "> vehicle speed spatiotemporal features main contributions effectively models economic conditions changing distance
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Loss Comparison: Optimized vs Non-Optimized.

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