# Applying Spectral Clustering to Decode Mobility Patterns in Athens, Greece
## 1 Introduction
Clustering is a type of unsupervised learning in which objects are grouped based on their inherent similarities. Various clustering techniques exist, including hierarchical, partitional, grid-based, density-based, and model-based approaches [23]. In travel behavior research, cluster analyses have been applied to group passengers with similar preferences and needs [24]. The most highly used method is the Latent Class Cluster Analysis (LCCA), which is ideal when the imported dataset consists solely of categorical variables. For instance, [PERSON] et al. [25] utilized LCCA on a survey to explore how variations in trip patterns are influenced by socio-demographics, household characteristics, and travel-related attitudes. Similarly, [PERSON] and [PERSON] applied Latent Profile Analysis to identify socio-demographic groups with a high dependence on cars [26]. However, grouping travel plans is quite complex due to the inclusion of both continuous and categorical (or dummy) variables in the collected datasets. [PERSON] et al. [27] found clustering techniques effective in identifying significant similarities among the plans of social groups. Indeed, using a k-means clustering method, they achieved to identify popular travel schedules. [PERSON] et al. [28, 29] proposed an activity scheduler by applying a novel fuzzy C-Means clustering method. In this approach, each data point is assigned a probability of belonging to multiple clusters, which leads to the formation of more homogeneous activity patterns. Although, there is much research on clustering travelers and daily travel schedules, no study has yet concentrated on trips. This study assumes that trip patterns are associated with noticeable repetitions that can be utilized to overcome data availability limitations appearing in Athens Metropolitan Area (AMA), Greece. Moreover, it hypothesizes that these repetitions are correlated with socio-demographic characteristics. This starting hypothesis is reasonable, as it has already been confirmed by [PERSON] and [PERSON] [30]. Therefore, the objective of this research is to identify key demand clusters for each transport mode by applying a flexible and scalable clustering method, called Spectral Clustering. To do so, trips collected by a recent revealed preferences survey are classified into clusters and their dimensions are analyzed in-depth. The developed tools are also useful for training and improving an open-source package called AthensPop (version 0.1) (AthensPop GitHub repository: [[https://github.com/[PERSON]/athenspop](https://github.com/[PERSON]/athenspop)]([https://github.com/[PERSON]/athenspop](https://github.com/[PERSON]/athenspop)) (accessed on 28 February 2025) taking into account key demand repetitions. AthensPop regenerates synthetic disaggregate demand by importing small sets of travel diaries that can be quickly collected in Athens. The paper is structured as follows: the overall methodological framework is given in Section 2, and the results are presented and discussed in Section 3, before ending with some valid conclusions and recommendations in the last section.