TMSeer: Visual analysis of city-level travel modes using cellular signaling data

Travel behavior analysis is crucial for various applications such as urban planning, transportation management, social behavior analysis, and business intelligence. Owing to the wide use of mobile phones, the massive cellular signaling data (CSD) provides us with an unprecedented opportunity to investigate people’s travel modes. However, it is challenging to explore such massive CSD due to its intrinsic sparsity, large size, and complexity. To fill this gap, we propose TMSeer , a visual analytics approach to help domain experts explore CSD and analyze city-level travel modes of people. Specifically, we first design an unsupervised method that combines rule-based heuristics (RBH) and Gaussian Mixture Model (GMM) to infer travel modes from CSD. We also take advantage of GMM’s characteristics to assess the uncertainty of inferred results. Then, we present novel visualizations to enable interactive multi-level exploration of CSD and in-depth analysis of travel modes: a map view with heatmaps to show the spatial distribution of urban traffic, a region view with hybrid radial diagrams based on clock and directional metaphors to display the traffic of different travel modes at the regional level; and a path view to visualize the detailed paths of different modes between regions. In particular, an enhanced Sankey diagram is designed to visualize the details of the movement, together with the novel band design to show the efficiency of travel modes in the path. We conducted two case studies and expert interviews with domain experts to evaluate our approach. The results demonstrate the effectiveness and usability of TMSeer in analyzing travel modes of people and urban mobility.