Learn to Cluster Human Mobility Pattern for Post-Disaster Analysis

Disaster has a great impact on human mobility patterns. Clustering mobility patterns by generalizing group level characteristics from diverse individual trajectories plays a vital role in informing post-disaster recovery strategies. However, predefined clustering criteria or supervised labeling alone are insufficient to adequately capture the dynamics of mobility patterns during disaster events. Besides, the existing methods are not enough to capture the sensitive changes in mobility patterns in disaster events and handle the data of high-dimensional. This research investigates an embedded deep learning-based method which can automatically extract the groups' short-term feature of mobility patterns and achieves short-term mobility pattern clustering during disaster. The proposed method employs a Transformer-based temporal encoder to capture intra-day sequence patterns and integrates a VAE component with an embedded latent variable that directly encodes group-level mobility modes. We also design a compactness–separation loss that explicitly encourages within-mode feature compactness and between-mode feature separation. Based on massive mobile data, we conduct mobility pattern clustering on the case of 2011 Fukushima Earthquake. Compared to conventional clustering approaches, the proposed model structure is more discriminative and can capture the more sensitive changes between pre- and post- events. Compared to baselines with different loss functions, proposal methods can make more accurate fitting result and obtain more discrete clusters modes. Additionally, sensitivity analysis is conducted to examine the influence of key hyper parameters within the model. Based on the clustering outcomes, five representative mobility patterns are identified. We further analyze the spatial-temporal characteristics of mobility pattern changes during the disaster events and the recovery period of mobility pattern.