Efficient detection of traffic crashes has been a significant matter of concern with regards to expressway safety management. The current challenge is that, despite collecting vast amounts of data, expressway detection equipment is plagued by low data utilization rates, unreliable crash detection models, and inadequate real-time updating capabilities. This study is to develop an effective digital twin framework for the detection of traffic crashes on expressways. Firstly, the digital twin technology is used to create a virtual entity of the real expressway. A fusion method for macro and micro traffic data is proposed based on the location of multi-source detectors on a digital twin platform. Then, a traffic crash detection model is developed using the ThunderGBM algorithm and interpreted by the SHAP method. Furthermore, a distributed strategy for detecting traffic crashes is suggested, where various models are employed concurrently on the digital twin platform to enhance the general detection ability and reliability of the models. Finally, the efficacy of the digital twin framework is confirmed through a case study of certain sections of the Nanjing Ring expressway. This study is expected to lay the groundwork for expressway digital twin studies and offer technical assistance for expressway traffic management.

中文翻译：

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基于宏观和微观数据融合的高速公路高效交通事故检测：数字孪生框架

有效检测交通事故一直是高速公路安全管理的一个重要问题。当前面临的挑战是，尽管收集了大量数据，但高速公路检测设备仍存在数据利用率低、碰撞检测模型不可靠、实时更新能力不足等问题。本研究旨在开发一种有效的数字孪生框架来检测高速公路上的交通事故。首先，利用数字孪生技术创建真实高速公路的虚拟实体。提出了一种基于数字孪生平台上多源探测器定位的宏微观交通数据融合方法。然后，使用 ThunderGBM 算法开发交通事故检测模型，并通过 SHAP 方法进行解释。此外，还提出了一种分布式交通事故检测策略，在数字孪生平台上同时采用各种模型，以增强模型的总体检测能力和可靠性。最后，通过南京绕城高速部分路段的案例研究，证实了数字孪生框架的有效性。该研究有望为高速公路数字孪生研究奠定基础，并为高速公路交通管理提供技术帮助。