山东大学学报 (工学版) ›› 2024, Vol. 54 ›› Issue (2): 36-46.doi: 10.6040/j.issn.1672-3961.0.2023.230
• 交通运输工程—智慧交通专题 • 上一篇
赵涛1,张宁2,王小超3,马川义2,田源1*,张圣涛2,杨梓梁1
ZHAO Tao1, ZHANG Ning2, WANG Xiaochao3, MA Chuanyi2, TIAN Yuan1*, ZHANG Shengtao2, YANG Ziliang1
摘要： 为保证高速公路合流区路段的交通安全,减少交通冲突,提出一种基于图神经网络轨迹预测的合流区交通冲突预测方法。该方法包括基于时空图卷积神经网络的轨迹预测方法以及基于预测轨迹的交通冲突预测方法。利用Mirror-Traffic数据集进行交通冲突指标阈值计算,并通过一定的轨迹数据处理方法得到适用的数据,进行网络模型训练和验证。结果表明,严重冲突的后侵入时间(post-encroachment time, PET)阈值为2.0 s,轻微冲突的PET阈值为5.36 s。该轨迹预测方法的平均位移误差为1.5 m,最终位移误差为2.1 m,时间成本为0.59 s,与其他4种方法相比,本研究方法的轨迹预测整体效果最好。在交通冲突预测方面,采用准确率、精确率、召回率和F1评价交通冲突预测模型,结果表明交通冲突预测效果较好。本研究提出的方法保证了预测的正确性,增强了预警系统下行车的安全性,提高了预警系统下的合流区通行效率。
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