A Systematic Review on Personal Route Prediction Techniques based on Trajectory data

With the advancement of location-acquisition and mobile computing devices in today’s life, the trajectory data obtained from Global Positioning System (GPS) is becoming large which makes route prediction a very challenging task. The route prediction can be done in two modes-offline route prediction and online route prediction in terms of acquiring performance and accuracy. There are many different techniques of route prediction which mainly consists of three consecutive tasks- i) Route abstraction ii) Frequent route pattern mining iii) Route Prediction applied in the context of different application domains. Route prediction plays an important role in many location-based applications including vehicular ad-hoc networks, route navigation, traffic control and congestion estimation, Place recommendation and many more. Trajectory data have different useful information hiding into it like background data and contextual information. So, Privacy is a big concern while sharing this personal trajectory data with the server predicting the best optimal route. Many techniques have been proposed by researchers for ensuring privacy of user data along with maintaining availability and usability of the data. Rather than doing all pre-processing computations on a single machine, many algorithms have been proposed to make system scalable. In this paper, a comparative analysis of various existing techniques of route prediction along with privacy preservation and scalability issues is discussed with pros and cons. The main aim of this paper is to give insights of the different algorithms of the route prediction with the directions of future research.