In Transport Mode Detection, a great diversity of methodologies exist according to the choice made on sensors, preprocessing, model used, etc. In this domain, the comparisons between each option are not always complete. Experiments on a public, real-life dataset are led here to evaluate carefully each of the choices that were made, with a specific emphasis on data fusion methods. Our most surprising finding is that none of the methods we implemented from the literature is better than a simple late fusion. Two important decisions are the choice of a sensor and the choice of a representation for the data: we found that using 2D convolutions on spectrograms with a logarithmic axis for the frequencies was better than 1-dimensional temporal representations.

中文翻译：

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交通模式检测深度学习的数据融合：案例研究

在传输模式检测中，根据对传感器、预处理、使用的模型等所做的选择，存在多种方法。在该领域中，每个选项之间的比较并不总是完整的。在公开的、真实的数据集上进行的实验被引导到这里，以仔细评估所做的每一个选择，特别强调数据融合方法。我们最令人惊讶的发现是，我们从文献中实现的方法中没有一种比简单的后期融合更好。两个重要的决定是传感器的选择和数据表示的选择：我们发现在频谱图上使用对数轴表示频率的 2D 卷积比一维时间表示更好。