Existing research on merging behavior generally prioritize the application of various algorithms, but often overlooks the fine-grained process and analysis of trajectories. This leads to the neglect of surrounding vehicle matching, the opaqueness of indicators definition, and reproducible crisis. To address these gaps, this paper presents a reproducible approach to merging behavior analysis. Specifically, we outline the causes of subjectivity and irreproducibility in existing studies. Thereafter, we employ lanelet2 High Definition (HD) map to construct a reproducible framework, that minimizes subjectivities, defines standardized indicators, identifies alongside vehicles, and divides scenarios. A comparative macroscopic and microscopic analysis is subsequently conducted. More importantly, this paper adheres to the Reproducible Research concept, providing all the source codes and reproduction instructions. Our results demonstrate that although scenarios with alongside vehicles occur in less than 6% of cases, their characteristics are significantly different from others, and these scenarios are often accompanied by high risk. This paper refines the understanding of merging behavior, raises awareness of reproducible studies, and serves as a watershed moment.

翻译：现有并道行为研究通常偏重各类算法的应用，却常忽视轨迹的精细化过程与分析，导致周边车辆匹配缺失、指标定义不透明以及可复现性危机。针对这些不足，本文提出一种可复现的并道行为分析方法。首先，我们系统阐述了现有研究中主观性与不可复现性的成因；随后，采用Lanelet2高精地图构建可复现框架，以最小化主观性、定义标准化指标、识别并行车辆并划分场景，进而开展宏观与微观对比分析。更重要的是，本文遵循可复现研究理念，公开全部源代码及复现说明。结果表明：尽管并行车辆场景在全部案例中占比不足6%，但其特征与其他场景存在显著差异，且此类场景常伴随高风险。本文深化了对并道行为的理解，提升了研究的可复现意识，具有里程碑意义。