Predictionof the user's future location based on past Trajectories

Abstract

Nowadays the use of technology is very common in human life and a large number of people have cell phones, PDAs, or fitness wristbands for their personal use. These modern devices have many powerful applications based on their hardware specifications. GPS hardware module is commonly available in all the above mentioned devices and it generates the GPS log data which are the key points of movement patterns for the individual. It is believed that continuously capturing the user GPS log data and then mining the trajectories patterns can be useful to predict the future location of the user. And it is useful in Smart Advertisement Campaign, Intelligent Transportation System and Smart Reminders, etc. Several studies have been carried out on monitoring the user's location. But there are some deficiencies regarding the individual to collective and even hybrid prediction. However, these studies have not utilized the user's daily routine trajectory patterns for assistance. This work aims to extract the meaningful trajectories patterns from large GPS data of daily routines which may be useful for the prediction of the next possible movement activities of individuals. The dataset which is used in this research is Geolife Trajectories in which 182 users' data captured over three years. There are 17,621 trajectories recorded by mobile phone GPS.In this research,the Hidden Markov Model(HMM) and K Means Clustering are used for the future prediction ofan individual’s movement. The proposedwork is concentrated on the user's past day routine as well as time frame. The model uses these parameters as input and able to give results like “Where is the person physically present when it is a Friday?” or time and day bounded queries like “Where is the person physically present between 5:00 pm to 8:00 pm on Sundays?”.The experiment is conducted over the 182 user’s data and achieve the individual accuracy is upto83% as well as combined accuracy is an average of 35.64% recorded.