Collision causal discovery and real-time prediction of freeway tunnels: A novel dual-task approach

IF 6.7 1区 工程技术 Q1 CONSTRUCTION & BUILDING TECHNOLOGY
Jieling Jin , Helai Huang , Ye Li , Jianjun Dai
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引用次数: 0

Abstract

Although tunnels are critical traffic nodes in freeway networks, academic research addressing their real-time traffic safety is noticeably lacking. This study proposes a novel dual-task approach to analyze causal precursors and predict real-time collision risks in freeway tunnels. Unlike traditional models, which often trade off between predictive accuracy and causal depth, the proposed approach achieves both high causal interpretability and predictive performance. The approach utilizes a structural agnostic model (SAM) to discover causal precursors of freeway tunnel collisions using observational data. The collision causal graph data is then constructed based on the causal relationships identified by SAM. Additionally, the Causal Directed Graph Convolutional Networks (CDGCN) model is developed to capture causal relationships in the graph for real-time collision prediction. Utilizing freeway tunnel collision analysis data collected from the Caltrans Performance Measurement System, the approach performs dual tasks: identifying causal precursors of collisions and predicting future collisions in real time. The SAM results reveal five critical causal precursors influencing the likelihood of collisions. Comparative analyses with existing interpretable machine learning models show similarities between the causal precursors of tunnel collision risk revealed by SAM and the important correlation precursors identified by the comparative models. However, correlation is not the same as causation. When tested against current state-of-the-art real-time collision predictive models, the proposed CDGCN model demonstrates superior accuracy, especially on datasets containing causally relevant precursors, highlighting the potential of this approach for feature selection and risk prediction. This advancement not only provides a practical framework for mitigating collision risks in freeway tunnels but also makes a significant contribution to traffic safety research.
高速公路隧道碰撞因果发现与实时预测:新型双任务方法
虽然隧道是高速公路网络中的重要交通节点,但针对其实时交通安全的学术研究却明显不足。本研究提出了一种新颖的双任务方法,用于分析因果前兆和预测高速公路隧道的实时碰撞风险。传统模型通常会在预测准确性和因果深度之间进行权衡,而本研究提出的方法则不同,它既能实现较高的因果可解释性,又能提高预测性能。该方法利用结构不可知模型(SAM),通过观察数据发现高速公路隧道碰撞的因果前兆。然后根据 SAM 确定的因果关系构建碰撞因果图数据。此外,还开发了因果定向图卷积网络(CDGCN)模型,以捕捉图中的因果关系,从而进行实时碰撞预测。利用从加州交通局性能测量系统收集的高速公路隧道碰撞分析数据,该方法执行了双重任务:识别碰撞的因果前兆和实时预测未来的碰撞。SAM 的结果揭示了影响碰撞可能性的五个关键因果前兆。与现有可解释机器学习模型的比较分析表明,SAM 所揭示的隧道碰撞风险因果前兆与比较模型所确定的重要相关性前兆之间存在相似之处。然而,相关性并不等于因果关系。在与当前最先进的实时碰撞预测模型进行测试时,所提出的 CDGCN 模型表现出了更高的准确性,尤其是在包含因果相关前兆的数据集上,这凸显了该方法在特征选择和风险预测方面的潜力。这一进展不仅为降低高速公路隧道中的碰撞风险提供了一个实用框架,也为交通安全研究做出了重大贡献。
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来源期刊
Tunnelling and Underground Space Technology
Tunnelling and Underground Space Technology 工程技术-工程:土木
CiteScore
11.90
自引率
18.80%
发文量
454
审稿时长
10.8 months
期刊介绍: Tunnelling and Underground Space Technology is an international journal which publishes authoritative articles encompassing the development of innovative uses of underground space and the results of high quality research into improved, more cost-effective techniques for the planning, geo-investigation, design, construction, operation and maintenance of underground and earth-sheltered structures. The journal provides an effective vehicle for the improved worldwide exchange of information on developments in underground technology - and the experience gained from its use - and is strongly committed to publishing papers on the interdisciplinary aspects of creating, planning, and regulating underground space.
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