Visualization of individual and global mobility patterns

Supervisors: Moreno Colombo, Julien Nembrini

Student: Siyu Deng

Project status: Ongoing

Year: 2025

This study explores the reconstruction and visualization of individual and collective mobility patterns using anonymized data collected through the SWICE mobile app. The dataset contains geolocation, timestamps, and transport mode information, offering a comprehensive representation of daily travel behaviors across Switzerland. To reconstruct realistic travel routes between origin and destination points, an A-star pathfinding algorithm was applied to geohash-based frequency matrices representing transportation usage. Several preprocessing steps, including data cleaning, geospatial filtering, matrix reduction, convolution smoothing, and reweighting, were implemented to improve spatial continuity, reduce noise, and enhance computational efficiency.

The reconstructed trajectories successfully reproduced realistic mobility flows and modal patterns, preserving both large-scale and local-level travel characteristics. Visualization results highlight major transport corridors and urban mobility hotspots, enabling a clearer understanding of how different transport modes contribute to national mobility patterns. Comparative analyses between original and reconstructed datasets confirmed the accuracy and consistency of the proposed reconstruction method, particularly for long-distance trips such as train and car travel, while minor deviations occurred for short-distance modes like walking and cycling.

A user study was conducted to evaluate the usability and interpretability of the developed visualization interfaces. The System Usability Scale results indicated high user satisfaction, with average scores of 86.45 for us interface versions and 84.3 for the other interface versions, respectively. Participants found the system intuitive and informative, though some reported that dense route overlaps occasionally hindered quick path identification.

Overall, the findings demonstrate that combining probabilistic route reconstruction with interactive visualization provides valuable insights into urban mobility and sustainability while preserving user privacy. The proposed approach offers a foundation for future research in privacy-aware mobility analytics and environmentally informed transport planning.

Keywords: Mobility Data Reconstruction, Data Visualization, Urban Mobility, Anonymized Data, Transport Modes