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.
