## 4 Mobile Bike Test
### Automated Data Analysis
#### 4.3.1 Clustering Algorithm Selection  
The sensor emits measurement signals at a frequency of approximately 20 Hz. Instead of producing one or two random data points, it should record a cluster of points that represent the distance of the overtaking vehicle during its pass. Hence, we require a cluster analysis to automatically identify vehicle passes.  
The exploration of clustering algorithms reveals distinct methodologies and applications across various domains, each with unique working principles, cluster determination methods, and optimal use cases [31]. For instance, K-Means, renowned for its application to large datasets with approximately spherical cluster shapes, necessitates a specified number of clusters and exhibits high sensitivity to outliers [32]. Contrastingly, DBSCAN, a density-based algorithm, autonomously determines the number of clusters, showcasing adeptness in identifying clusters of varied shapes and sizes while proficiently managing noise and outliers [33]. Hierarchical clustering, particularly agglomerative clustering, and the Gaussian mixture model (GMM) require a specified number of clusters and demonstrate moderate sensitivity to outliers, with the former being suitable for small datasets requiring a tree-like structure and the latter being apt for scenarios demanding probabilistic cluster assignments [34; 35]. Mean shift, a mode-seeking algorithm, determines clusters automatically and is notably utilized in computer vision and image segmentation [36]. Table 9 provides a brief comparison between the different clustering algorithms available.  
For the specific task of identifying close vehicle passes, DBSCAN emerges as a preferred choice among clustering algorithms. Unlike K-Means or Gaussian mixture models, DBSCAN does not require a predefined number of clusters, making it adept at discovering clusters of varied shapes and sizes. Moreover, DBSCAN's inherent ability to segregate noise or outliers ensures that sporadic, unrelated data points do not form erroneous clusters, a challenge that can affect some other algorithms. This noise-handling capability makes DBSCAN particularly suited for real-world data where outliers are common. For a dynamic setting like traffic with unpredictable passing patterns and potential anomalies, DBSCAN offers the robustness and adaptability necessary to discern meaningful clusters effectively.  
\begin{table}
\begin{tabular}{c c c c c} \hline  & **Working Principle** & **Number of Clusters** & **Sensitivity to Outliers** & **Use Cases** \\ \hline K-Means & Partitioning & Needs to be specified & High & \begin{tabular}{c} Large datasets: when the shape of clusters is \\ approximately spherical. \\ \end{tabular} \\ \hline DBSCAN & Density-based & Determined automatically & Low & \begin{tabular}{c} When cluster shape is \\ irregular, and \\ noise/outliers are present. \\ \end{tabular} \\ \hline Hierarchical & Agglomerative or divisive & \begin{tabular}{c} Visualized using \\ dendrogram, cut at \\ desired level \\ \end{tabular} & Moderate & \begin{tabular}{c} Small datasets: when \\ tree-like structure or \\ hierarchy is required. \\ \end{tabular} \\ \hline Agglomerative & \begin{tabular}{c} Groups data into objects \\ into a tree of clusters. \\ \end{tabular} & Needs to be specified & Moderate & \begin{tabular}{c} When a hierarchical \\ approach is desired. \\ \end{tabular} \\ \hline Gaussian Mixture Model (GMM) & Uses probability distributions & Needs to be specified & Moderate & \
\begin{tabular}{c} When clusters are \\ elliptical or when \\ probabilistic cluster \\ assignments are desired. \\ \end{tabular} \\ \hline Mean Shift & Mode seeking & Determined automatically & Moderate &
\begin{tabular}{c} Image segmentation, \\ computer vision. \\ \end{tabular} \\ \hline \end{tabular}
\end{table}
Table 9: General comparison between different types of clustering algorithms.