Predicting Next Useful Location With Context-Awareness: The
  State-Of-The-Art
Alireza Nezhadettehad Arkady Zaslavsky Rakib Abdur Siraj Ahmed Shaikh Seng W. Loke Guang-Li Huang Alireza Hassani
Predicting the future location of mobile objects reinforces location-aware
services with proactive intelligence and helps businesses and decision-makers
with better planning and near real-time scheduling in different applications
such as traffic congestion control, location-aware advertisements, and
monitoring public health and well-being. The recent developments in the
smartphone and location sensors technology and the prevalence of using
location-based social networks alongside the improvements in artificial
intelligence and machine learning techniques provide an excellent opportunity
to exploit massive amounts of historical and real-time contextual information
to recognise mobility patterns and achieve more accurate and intelligent
predictions. This survey provides a comprehensive overview of the next useful
location prediction problem with context-awareness. First, we explain the
concepts of context and context-awareness and define the next location
prediction problem. Then we analyse nearly thirty studies in this field
concerning the prediction method, the challenges addressed, the datasets and
metrics used for training and evaluating the model, and the types of context
incorporated. Finally, we discuss the advantages and disadvantages of different
approaches, focusing on the usefulness of the predicted location and
identifying the open challenges and future work on this subject by introducing
two potential use cases of next location prediction in the automotive industry.