A major problem of vertical alignment recreation is to automatically attribute the measured points to geometric elements (i.e., grades and vertical curves) and to efficiently recreate the vertical alignment with constraints. Most existing methods are nonoptimal in theory, semiautomatic, or inefficient in recreating an alignment. A new approach is proposed for automatically determining segmentation into geometric elements from measured points and efficiently optimizing a recreated alignment with constraints. First, independent parameters defining an alignment, are proposed to represent a vertical alignment. Then, a statistical deflection angle (SDA) method is proposed to determine segmentation by exploring statistical features of the geometric elements. Analysis shows that the SDA method outperforms the curvature method in distinguishing between grades and curves. Patterns of the segmentation process are found, and a segmentation algorithm is provided. Further, an optimization model is proposed to recreate the alignment with constraints. Experiment results demonstrate that this approach is highly efficient and effective compared with existing methods, reducing the number of searched alignments from tens of thousands to tens, while improving the value of the objective function.

中文翻译：

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整合分段和参数估计以重新创建垂直对齐方式

垂直对齐重新创建的主要问题是自动将测量点归因于几何元素（即坡度和垂直曲线），并有效地约束重新创建垂直对齐。大多数现有方法在理论上都不是最佳方法，半自动方法或在重新创建比对时效率低下。提出了一种新方法，该方法可自动确定从测量点到几何元素的分割，并有效地优化约束条件下重新创建的对齐方式。首先，提出了定义路线的独立参数来表示垂直路线。然后，提出了一种统计偏转角（SDA）方法，通过探索几何元素的统计特征来确定分割。分析表明，在区分坡度和曲线时，SDA方法优于曲率方法。找到了分割过程的模式，并提供了分割算法。此外，提出了优化模型以重新创建具有约束条件的对齐方式。实验结果表明，与现有方法相比，该方法高效，有效，将搜索的比对数从数万减少到数万，同时提高了目标函数的价值。