Predicting Individual Mobility Pattern by Identifying Indoor Trajectories in Transport Hub

The proliferation of the Indoor Positioning System (IPS) has enabled unprecedented opportunities for exploring individual mobility patterns (IMPs) within transport hubs. IMP prediction studies in indoor environments face three critical limitations: suffering from incompatible data cleaning, ignoring the characteristic of trajectories having fewer “stay” phases, and lacking adequate individual historical trajectories. This study proposes an IMPs prediction framework by designing a novel floor filter algorithm (FFA), a hybrid Douglas-Peucker and Density-Based Spatial Clustering of Applications with Noise (DP-DBSCAN), and a various-order Markov chain tree (VOMC-Tree) model. Within this framework, the FFA is utilized to eliminate the effects of “floor hopping”. Additionally, the DP-DBSCAN algorithm is designed to identify Key Points (KPs) within trajectories, conquering the limitation of fewer “stay” phases. To overcome the lack of individual historical trajectories, the VOMC-Tree model is proposed for online predictions of passengers’ IMPs based on offline training. To evaluate the performance of the prediction framework, a case study is conducted using a Bluetooth-based indoor trajectory dataset collected from Beijing Capital International Airport (BCIA) of China and an expanded dataset. Experimental results demonstrate that our framework outperforms baseline models across various scales of training datasets, as further evidenced by an analytical unpredictability ratio. The VOMC-Tree model performs well in predicting the sharing rate of transportation modes. The predictions of IMPs can be applied to intelligent systems for the management of transport hubs and the optimization of passenger flow. The predictions regarding the sharing rate are beneficial to the planning and scheduling of multi-traffic modes system.