The transportation industry, particularly the trucking sector, is prone to
workplace accidents and fatalities. Accidents involving large trucks accounted
for a considerable percentage of overall traffic fatalities. Recognizing the
crucial role of safety climate in accident prevention, researchers have sought
to understand its factors and measure its impact within organizations. While
existing data-driven safety climate studies have made remarkable progress,
clustering employees based on their safety climate perception is innovative and
has not been extensively utilized in research. Identifying clusters of drivers
based on their safety climate perception allows the organization to profile its
workforce and devise more impactful interventions. The lack of utilizing the
clustering approach could be due to difficulties interpreting or explaining the
factors influencing employees' cluster membership. Moreover, existing
safety-related studies did not compare multiple clustering algorithms,
resulting in potential bias. To address these issues, this study introduces an
interpretable clustering approach for safety climate analysis. This study
compares 5 algorithms for clustering truck drivers based on their safety
climate perceptions. It proposes a novel method for quantitatively evaluating
partial dependence plots (QPDP). To better interpret the clustering results,
this study introduces different interpretable machine learning measures (SHAP,
PFI, and QPDP). Drawing on data collected from more than 7,000 American truck
drivers, this study significantly contributes to the scientific literature. It
highlights the critical role of supervisory care promotion in distinguishing
various driver groups. The Python code is available at
this https URL

卡车司机的安全氛围对事故预防起着重要作用，通过聚类分析司机的安全氛围感知有助于组织更有针对性地干预。本研究提出了一个可解释的聚类方法来分析安全氛围，并比较了 5 种聚类算法。在收集了超过 7000 名美国卡车司机的数据基础上，研究发现监管照料在区分不同司机群体中起关键作用。

一种可解释的聚类方法用于安全气候分析：研究驾驶员群体在安全气候感知中的区分

An interpretable clustering approach to safety climate analysis:  examining driver group distinction in safety climate perceptions

Since NASA put forward the concept of the digital twin in 2010, many
industries have put forward the dynamic goal of digital development, and the
transportation industry is also among them. With more and more companies laying
out on this virgin land, the digital twin transportation industry has grown
rapidly and gradually formed a complete scientific research system. However,
under the largely mature framework, there are still many loophole problems that
need to be solved. In the process of constructing a road network with point
cloud information, we summarize several major features of the point cloud
collected by laser scanners and analyze the potential problems of constructing
the network, such as misjudging the feature points as ground points and grid
voids. On this basis, we reviewed relevant literature and proposed targeted
solutions, such as building a point cloud pyramid modeled after the image
pyramid, expanding the virtual grid, etc., applying CSF for ground-point cloud
extraction, and constructing a road network model using the PTD (progressive
density-based filter) algorithm. For the problem of road sign detection, we
optimize the remote sensing data in the ground point cloud by enhancing the
information density using edge detection, improving the data quality by
removing the low intensity points, and achieving 90% accuracy of road text
recognition using PaddleOCR and Densenet. As for the real-time digital twin
traffic, we design the P2PRN network using the backbone of MPR-GAN for 2D
feature generation and SuperGlue for 2D feature matching, rendering the
viewpoints according to the matching optimization points, completing the
multimodal matching task after several iterations, and successfully calculating
the road camera position with 10{\deg} and 15m accuracy.

构建一个包含数字孪生、交通行业、点云、道路网络和道路标志检测的研究论文的关键词和总结。