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用轨迹数据进行实时交通冲突预测的深度转移学习方法

Qinzhong Hou¹, Yonghao Yang¹, Jiatong Liang¹

¹School of Automotive Engineering, Harbin Institute of Technology, Weihai, China.

Accident; analysis and prevention

|February 18, 2025

概括

本研究引入了深度转移学习方法,以改进实时交通冲突预测模型. 带有转移学习的Gated-Transformer模型显著提高了在不同交通环境中的预测准确性.

科学领域:

运输工程运输工程
人工智能的人工智能
交通安全交通安全

背景情况:

实时交通冲突预测对于积极的交通安全系统至关重要.
现有的模型缺乏可转移性,由于不同的交通环境,在新地点应用时显示性能下降.
这种限制阻碍了预测性交通安全解决方案的广泛采用.

研究的目的:

提出一种新的深度转移学习方法,以提高实时交通冲突预测模型的可转移性.
评估不同深度学习架构 (Gated-Transformer,FCN,LSTM-FCN,MLSTM-FCN) 在交通冲突预测中用于特征提取的有效性.
通过将转移学习应用于在不同交通领域训练的模型来评估获得的性能改善.

主要方法:

开发了一个实时冲突预测框架,使用轨迹数据并考虑时间流量变化.
使用门式变压器,FCN,LSTM-FCN和MLSTM-FCN作为特征提取的骨干网络.
实现了基于最大平均差异的独立转移学习架构,以评估域名相似性.
经验评估的模型使用 exiD 数据集,区分源域和目标域.

主要成果:

与基线模型相比,式变压器模型在特征提取和预测方面表现优异,F1得分为0.864和AUC为0.980.
转移学习架构显著提高了应用于新领域的模型的预测性能.
对于Gated-Transformer模型,转移学习导致F1得分增加了11.9%,AUC增加了10.2%.
灵敏度分析建议最佳参数:滑动时间窗口为6秒,预警时间为5秒.

结论:

深度转移学习提供了一种可靠和有效的方法,可以提高实时交通冲突预测模型的可转移性.
门式变压器架构显示出交通冲突预测和域调整的巨大潜力.
结果为开发实际的交通安全预警系统提供了宝贵的见解,特别是在车辆到基础设施环境中.

关键词:

深度转移学习是指深度转移学习.封闭式变压器封闭式变压器合并区的合并地区.实时冲突预测和预测.轨道数据轨道数据

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