Abstract
Abstract. Preliminary mobile was considered to be a device to make human connections easier. But today the consumption of this device has been evolved to a platform for gaming, web surfing and GPS-enabled application capabilities. Embedding GPS in handheld devices, altered them to significant trajectory data gathering facilities. Raw GPS trajectory data is a series of points which contains hidden information. For revealing hidden information in traces, trajectory data analysis is needed. One of the most beneficial concealed information in trajectory data is user activity patterns. In each pattern, there are multiple stops and moves which identifies users visited places and tasks. This paper proposes an approach to discover user daily activity patterns from GPS trajectories using association rules. Finding user patterns needs extraction of user’s visited places from stops and moves of GPS trajectories. In order to locate stops and moves, we have implemented a place recognition algorithm. After extraction of visited points an advanced association rule mining algorithm, called Apriori was used to extract user activity patterns. This study outlined that there are useful patterns in each trajectory that can be emerged from raw GPS data using association rule mining techniques in order to find out about multiple users’ behaviour in a system and can be utilized in various location-based applications.
Highlights
The mobile phones were a device of communication; but today thanks to the technology advancements we can use them as tracking gadgets
This paper aims to depict a solution based on Apriori algorithm to find out user behavioural patterns from user’s trajectory data by generating rules
We proposed a method to reveal user behavioral patterns from GPS trajectory data using place recognition and association rule mining
Summary
The mobile phones were a device of communication; but today thanks to the technology advancements we can use them as tracking gadgets. Mobile devices with GPS (Global Positioning System) receivers are able to capture and record traces of users’ movement, including coordinates, timestamps, elevation and other attributes such as velocity and bearing. Owing to GPS-enabled mobiles, huge amounts of user traces are recorded and stored from user daily activities. The need for using hidden information in trajectories developed studies in various fields such as urban planning, transportation, surveillance and security (Gudmundsson, Laube et al 2011). Extracting useful information from raw GPS trajectory data is a complex task. Trajectory data is basically a series of points which is not mostly human readable and needs interpretation; trajectory analysis methods are necessary to extract useful information about users’ behaviour (Zheng, Zhang et al 2009)
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