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Block-based construction worker trajectory prediction method driven by site risk
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AI
DOI：10.1016/j.autcon.2024.105721.png)
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摘要
En 中文
Different from pedestrian trajectory prediction, construction worker trajectories are usually affected by risks and have complex movement patterns. Track point-based prediction methods require high prediction accuracy for safety management. This paper presents a block-based construction worker trajectory prediction method driven by site risk. First, the construction site is divided into multiple blocks and the site safety risk within different blocks is quantified. Second, stopping, large and small turning segments in worker's trajectory are detected to divide the dataset. Finally, a transformer-based trajectory prediction model for construction workers is developed and trained separately for different datasets. The worker's next go-to block and the duration within the next block are predicted. The results show that the accuracy of block direction prediction within 120 degrees reach 93%, and the error of duration prediction can be 0.52 s. The study has theoretical and practical value for promoting blockbased safety management and enhancing safety proactivity.
Keyword:
Construction workers
Safety risk
Movement pattern
Trajectory prediction
Transformer
期刊
IF:
11.5
论文数:
6.3K
被引数:
4.2W
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引用论文
Simulating travel paths of construction site workers via deep reinforcement learning considering their spatial cognition and wayfinding behavior考虑建筑工地工人的空间认知和寻路行为，通过深度强化学习模拟建筑工地工人的出行路径
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