A comprehensive survey on trajectory-based location prediction

摘要

Abstract Recent explorative growth in telecommunication and telepathy technology has flooded the market with location-based data, which paves the way for location-aware prediction services. These applications have vast domain influence in route navigation, recommendation system, traffic-congestion control, ecological study, climatological forecast, and many more. Research efforts are spent on the put-forward overall picture of location prediction through trajectory data. This survey offers an extensive overview of location prediction enveloping basic definitions and concepts, data sources, approaches, and applications. Moreover, Spatial–Temporal pattern-based prediction models are discussed, highlighting the advantages and disadvantages of each. Sequential, periodic, and frequent pattern mining advances are noted. This paper presents a recent deep learning methodology for extracting features of a large trajectory. Distributive big data models using Hadoop and MapReduce frameworks are recorded. Location prediction using the social media platform is mentioned. Content-based and semantic mining models are studied. Tables and diagrams are displayed to provide at a glance view to facilitate smooth understanding. Furthermore, application and challenges are addressed related to the next location prediction. The overall conclusion of the survey and future directions are also listed.