# Applying Spectral Clustering to Decode Mobility Patterns in Athens, Greece
## 4 Discussion
### Scientific and Practical Recommendations  
Future research could explore possible integration of cluster analysis results in predictive models of travel behavior. The reduction in dimensionality through the clustering process would result in less complicated and more interpretable transport models. Clusters can contribute to development of synthetic populations and synthetic trips chains based on sociodemographic characteristics. Yet, this is not a straightforward process. The consideration of clusters as discrete alternatives would result in high endogeneity because these alternatives are not being exogenously defined but rather identified based on the observed behavior that we aim to understand. This is a theoretical question that need to be addressed in the future. In this context, additional variables could be added. Those extra variables could concern other characteristics of the movement itself, such as the cost of the trip, the comfort factor, and the accessibility. It is suggested to investigate to what extent the residents of Athens who frequently use the car for their daily trips are truly dependent on it and which factors influence their choice. Parking space non-availability is a tremendous problem is some neighborhoods of Athens and comprise an additional variable that form travel behavior. Last but not least, the development of a data harmonization framework to integrate all potential mobility data pieces (from official or unofficial bodies) is more crucial than ever. This new pipeline will ensure that valuable datasets are not lost and enable further analysis using the spectral clustering approach. The later will enable the analysis of long-term changes in the identified mobility patterns.  
Nevertheless, the outputs of this analysis can now be utilized in the transport planning process. As can be understood, high-cost projects currently underway, such as the construction of the new metro line 4 are likely to benefit long-distance commuters, enchasing the competition between metro and private car use for work-related trips (cluster 4 vs. cluster 3). But they are not enough. Cluster 2 exposes a critical mobility problem. Indeed, a relatively high percentage of respondents use private car for short-distance trips. Thereduction in this type of trips should be a priority for policymakers by developing a multi-level strategy. The improvement of bus services in terms of frequencies and coverage is more than ever necessary in order to decrease intra-zonal traffic flows. Ride-hailing and other shared mobility services can complement local transport systems and increase their efficiency. Push measures, like parking restrictions, is always an additional option to protect the neighborhoods of Athens by high attracting flows. Yet, municipalities should take actions to promote active mobility for daily short-distance trips not only in the evening (like cluster 1) but in the morning. This can of course be performed by increasing the walkability of the road network and creating cycling infrastructure for micro-mobility modes (e.g., e-bikes and e-scooters). Overall, the existence of the cluster 2 necessitates the implementation of multiple small-scale policies and interventions to protect the neighborhoods of Athens rather than large scale projects.