# Applying Spectral Clustering to Decode Mobility Patterns in Athens, Greece
## 1 Introduction  
Climate change, rapid urbanization, and of course emerging social inequalities are some of the present issues which challenge the efficiency and sustainability of transport networks and cities [1, 2]. Mobility has become a major factor in energy consumption, CO\({}_{2}\) emissions, air pollution, traffic noise in urban areas [3, 4, 5], and even in the spread of viruses like COVID-19 during the pandemic [6, 7, 8]. Nevertheless, mobility remains still an unknown and unpredictable factor [9]. This impacts the effectiveness of policies, such as road pricing or parking restriction, as well as the efficiency of new promising and innovative modes that shared mobility encloses, are often not assessed in advance but rather after their implementation [10]. The scarcity of mobility data, such as travel diaries, is one of the major reasons why travel behavior is an x-factor in many cities in both the developed and developing world. For example, in Athens, Greece, the last Origin-Destination (OD) survey in Athens was conducted by Transport for Athens in 2006, when the first metro lines were constructed [11]. Since then, systematic data about transport demand has not been recorded again by any official body. While some municipalities collect travel data to support their plans, accessto these data are often restricted due to privacy concerns [12]. The same issue arises with platforms and services that handle large datasets, such as GPS trajectory data and location posts on social media. Simultaneously, traditional transport modeling tools utilized a less disaggregated approach, often overlooking significant determinants of travel demand and their interactions, such as land use changes, socio-demographic characteristics, spatial inequalities, and economic shifts. The classic four-stage model was indeed more static than dynamic [13]. [PERSON] and [PERSON] [14] have mentioned that integrity, interdependencies, high spatiotemporal aggregation, and behavioral basis are the main drawback of classical modeling techniques. [PERSON] et al. [15] argue that four-stage models fail to account for individuals' decisions and their interactions that reshape travel behavior. [PERSON] [16] have shown that self-determination as a user of a particular transport service was found to increase the likelihood of using it. Individual \"mobility identity\" does not only explain the transport mode choice for a single trip but also the frequency of using a transport mode. Before heavily criticizing past models, it is important to consider that computational power was limited in the past. This made it challenging to process and analyze the large datasets required for disaggregate models. Additionally, artificial intelligence and advanced machine learning algorithms were not available at the time, so that complicated interrelationships among variables of transport demand can be handled and modeled. Various advanced analytical methods are now applied in transport science to create synthetic populations and better predict \"the unpredictable\" travel demand. Factor Analysis and Analysis of Variance (ANOVA), as modern statistical techniques, have been used to eliminate the multiple dimensions of mobility [17]. Iterative Proportional Fitting has been employed in developing synthetic populations and activity-based models [18]. Monte Carlo Simulation methods were utilized to account for heterogeneity in traveler behavior and the factors influencing it [19]. Hierarchical models that integrate graph-theoretical and combinatorial optimization concepts have been contributed to reconstructing trip sequences from OD matrices in cities with significant data limitations [20]. Hidden Markov Models methods have been applied to forecast travel behavior [21], while neural Networks are now used for the deep generative modeling of synthetic travelers [22]. Nevertheless, a recent challenge that has emerged is not the accuracy of projections--which has generally improved--but the interpretability of the numerous and complex transport models.