Applying Spectral Clustering to Decode Mobility Patterns in Athens, Greece

The limited availability of mobility data makes it challenging to model demand, especially its spatiotemporal variations. Simultaneously, traditional transport modeling tools often rely on less disaggregated approaches, leading to gaps in understanding. To overcome these limitations, this study introduces the spectral clustering method to uncover major demand patterns considering various transport modes. It focuses on Athens, Greece, and utilizes a set of 1347 reported trips. The study reveals six distinct trip clusters. The first group, “an evening stroll nearby”, captures short distance tours typically undertaken by walking. The second cluster, “my work is nearby but I use my car” highlights a significant trend where individuals with short commutes, less than 6 km, predominantly use private cars. The third cluster, “commuting by metro”, features long-distance trips primarily for work. The fourth cluster reveals long-distance work-related trips with private cars, favored by active residents with high income. The fifth pattern, “trips of young people”, involves midnight recreational and moderate-distance morning trips for education, with an increased usage of public transport. The sixth cluster concerns short distance tours for various activities. The findings indicate that the second cluster’s high reliance on private cars for short trips is problematic. Reducing this reliance should be a priority for policymakers.