The transport network is a complex system that benefits from detailed data on user mobility. Analyzing user trajectories through clustering or classification methods can provide valuable insights into mobility patterns. Extracting transport modes from these trajectories using classification methods enhances the understanding of user mobility. The complexity of classification methods varies, with some classifying a few transport modes, such as walking, running, bicycling, and driving. In contrast, others classify up to seven modes or use private, unpublished datasets. A key challenge in transport mode classification is ensuring the comparability of different methods across various contexts. Additionally, comparing results is further complicated by the insufficient use of existing standardized benchmark, which in the case of transport mode classification, must contain a structured testing framework and a dataset on which the testing will be conducted. This research introduces a process for collecting data to develop a new transport mode classification dataset. The goal is to enhance the benchmark by evaluating classification methods across diverse traffic patterns and geographic areas, thereby assessing their spatial independence. Spatial independence is crucial because it ensures that classification methods remain accurate regardless of geographic variations. This improves comparability by enabling consistent evaluation of methods across regions, as the improved benchmark addresses spatial independence and ensures robustness for real-world deployment. The current benchmark in literature examines three types of independence: user, position, and time independence. Our tests employ a multilevel method based on Transition State Matrices (TSMs) and the Random Forest (RF) algorithm for transport mode classification. The results demonstrate that the multilevel method maintains spatial independence and achieves higher accuracy compared to the original benchmark problem.

中文翻译：

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通过将空间独立性与多模式数据集集成来增强交通模式分类基准


传输网络是一个复杂的系统，受益于用户移动性的详细数据。通过聚类或分类方法分析用户轨迹可以提供对移动模式的宝贵见解。使用分类方法从这些轨迹中提取运输模式可以增强对用户移动性的理解。分类方法的复杂程度各不相同，有些分类方法对几种交通方式进行分类，例如步行、跑步、骑自行车和驾驶。相比之下，其他组织对多达七种模式进行分类或使用私有的、未发布的数据集。运输模式分类的一个关键挑战是确保不同方法在不同环境中的可比性。此外，由于现有标准化基准的使用不足，比较结果变得更加复杂，在运输方式分类的情况下，该基准必须包含一个结构化的测试框架和一个将进行测试的数据集。本研究介绍了一种收集数据以开发新的交通模式分类数据集的过程。目标是通过评估不同交通模式和地理区域的分类方法来增强基准，从而评估其空间独立性。空间独立性至关重要，因为它可以确保分类方法保持准确，而不受地理变化的影响。这通过支持跨区域对方法进行一致评估来提高可比性，因为改进的基准解决了空间独立性问题并确保了实际部署的稳健性。目前的文献基准研究了三种类型的独立性：用户、位置和时间独立性。 我们的测试采用基于过渡状态矩阵 （TSM） 和随机森林 （RF） 算法的多级方法进行运输模式分类。结果表明，与原始基准问题相比，多级方法保持了空间独立性并获得了更高的准确性。