A survey on next location prediction techniques, applications, and challenges

Abstract

AbstractNext location prediction has recently gained great attention from researchers due to its importance in different application areas. Recent growth of location-based service applications has vast domain influence such as traffic-flow prediction, weather forecast, and network resource optimization. Nowadays, due to the explosive increasing of positioning and sensor devices, big trajectory data are produced related to human movement. Using this big location-based trajectory data, researchers tend to predict human next location. Research efforts are spent on the put forward overall picture of next location prediction, and number of works has been done so as to realize robust next location prediction systems. However, in-depth study of those state-of-the-art works is required to know well the applications and challenges. Therefore, the aim of this paper is an extensive review on existing different next location prediction approaches. This work offers an extensive overview of location prediction enveloping basic definitions and concepts, data sources, approaches, and applications. In next location prediction, trajectory is represented by a sequence of timestamped geographical locations. It is challenging to analyze and mine trajectory data due to the complex characteristics reflected in human mobility, which is affected by multiple contextual information. Heterogeneous data generated from different sources, users’ random movement behavior, and the time sensitivity of trajectory data are some of the challenges. In this manuscript, we have discussed various location prediction approaches, applications, and challenges, and it sheds light on important points regarding future research directions. Furthermore, application and challenges are addressed related to the user’s next location prediction. Finally, we draw the overall conclusion of the survey, which is important for the development of robust next location prediction systems.