FLCSS: A fuzzy‐based longest common subsequence method for uncertainty management in trajectory similarity measures

Abstract

The large quantity of movement data collected from various sources can be inherently uncertain and heterogeneous. In the movement data analysis and mining spectrum, computing the similarity of trajectories while considering the uncertainty and heterogeneity has been less addressed. Generally, two factors of sampling and positioning error cause uncertainty in trajectory databases. Therefore, in this research, a method based on the longest common subsequence (LCSS), named FLCSS, is proposed that uses fuzzy theory and the bead model to consider the uncertainty of trajectories originated from positioning and sampling errors. The performance of FLCSS is evaluated by implementations on real and synthetic datasets, and compared with six important and commonly used similarity measurement methods, namely, LCSS, edit distance on real sequence (EDR), dynamic time warping (DTW), edit distance with real penalty (ERP), Hausdorff distance (HD), and Fréchet distance (FD). The results show that FLCSS has a better performance compared to other methods, in terms of sensitivity to point displacement, noise, and different sampling rates. Furthermore, the high correlation between FLCSS and LCSS (ρ = 0.91) confirms the robustness of the proposed method in considering uncertainty in the trajectory databases.

Predicting Dust-Storm Transport Pathways Using a Convolutional Neural Network and Geographic Context for Impact Adaptation and Mitigation in Urban Areas

“…2023, 15, 2468 3 of 18 short period of time. MPOs are typically investigated by analyzing their trajectories [23,24]. While dust storms have severely affected residents and their physical assets, particularly in cities and metropolitan areas, few researchers have focused on predicting dust-storm pathways thus far, considering dust storms as MPOs.…”