CLUSTMOSA: Clustering for GPS trajectory data based on multi-objective simulated annealing to develop mobility application

Mobility analysis is the core idea of many applications such as vehicle navigation, trajectory anal-ysis, POI recommendation, and traffic flow analysis. These applications collect huge spatio-temporal information represented as trajectories of a moving object such as a vehicle or people using Global Positioning System enabled devices. Various techniques are evolved to process, manage and extract useful information from trajectories. Among these techniques, clustering plays an important and integral role in developing various mobility applications. Popular traditional clustering techniques such as DBSCAN, K-means, OPTICS, hierarchical clustering, and DJ-clustering are used for this purpose. However, these techniques suffer from major issues such as entrapping in local optima and being less effective in varying densities. Further, these methods have low search capability in search space, work upon single criteria optimization, and are less scalable for the big dataset. To overcome these issues, a new multi-objective criterion-based evolutionary clustering termed CLUSTMOSA is proposed. It exploits the search capability of archived multi-objective simulated annealing (AMOSA) to cluster the dataset. It stabilizes the exploratory and exploitative behavior of the solution. In this paper, three clustering evaluation metrics are simultaneously exploited as objective functions of CLUSTMOSA. Also, a new segmentation method is presented using bearing measurement for trajectory data. It helps to eliminate multiple waypoints localized over the straight roads and prevents multiple cluster formations for the same segment. To investigate the performance, the proposed CLUSTMOSA, along with a new segmentation method using bearing measurement is compared with the state-of-art methods of trajectory data mining. The extensive experiments and analysis prove the superiority of our clustering model over state-of-art approaches.(c) 2022 Elsevier B.V. All rights reserved.

Automated summary

Mobility analysis is essential for many applications, and clustering is a crucial technique in developing these applications. Traditional clustering techniques have limitations, such as being trapped in local optima and less effective in varying densities. To overcome these issues, a new multi-objective criterion-based evolutionary clustering method called CLUSTMOSA is proposed. It utilizes archived multi-objective simulated annealing (AMOSA) for clustering and improves the search capability. The performance of CLUSTMOSA, along with a new segmentation method, is compared with state-of-the-art methods, and the experiments prove its superiority.