Short-term trajectory prediction for individual metro passengers integrating diverse mobility patterns with adaptive location-awareness

Abstract

Short-term trajectory prediction (StTP) for individual metro passengers is of great importance in intelligent transportation systems and real-time security risk management. Existing research efforts still have shortcomings in constructing an interpretable StTP model with limited historical trajectory data for individual metro passengers. This paper thus intends to enhance the existing StTP methods in two aspects: (1) encoding the trajectory sequences with universal and personalized mobility rules to explain the mobility patterns of metro passengers, and (2) constructing an interpretable StTP model with adaptive location-awareness to improve the prediction accuracy. Along this line, this study develops a novel framework for individual metro passengers by integrating diverse mobility patterns with adaptive location-awareness (StTP-ML). Particularly, a trajectory representation approach is proposed to process the observed Wi-Fi probe data and transfer them into structured trajectories. Then, the diverse mobility patterns of individual passengers are discovered, including temporal periodicity, spatial symmetry, and sequence correlation. Finally, an adaptive location-aware model integrated with individual diverse mobility patterns-priority strategy is constructed. To evaluate the proposed StTP-ML model, this research also conducts experiments on real-world passenger trajectory data at a busy metro station in Shanghai, China. The experimental results show that the average accuracy rate of the StTP-ML-E model is improved up to 84%, which outperforms all other baseline models in the test. It demonstrates that the proposed StTP-ML model can integrate more interpretable mobility patterns than the baseline models and provide more accurate trajectories for individual metro passengers in future time periods.