Mining Transportation Modes and Significant Places from Individual GPS Trajectories
Date Issued
2009
Date
2009
Author(s)
Chang, Han-Wen
Abstract
The whereabouts of a person not only indicates her schedule, but also reflects her lifestyle. The transportation taken and the places visited indicate the habit and preference of the user. With the growing popularity of commercial GPS loggers and GPS-enabled mobile phones, the positions of a person could be obtained and logged, and further analyzed to infer the transportation taken and places visited. Moreover, some places are more significant than others in one''s daily life. These significant places shapes the life of the person. In this thesis, we created a prototype of a trajectory management service to annotate and visualize the trajectories. We adopted machine learning techniques to segment the trajectories and extract their features, and used supervised learning approach to train probabilistic models. We modeled the transportation mode learning problem as a sequence labeling problem using linear-chain conditional random fields (CRF). We compared the CRF model with support vector machines (SVM), and our results show that CRF outperforms SVM, when temporal relationship is considered. In addition, we adopted OPTICS clustering to find the places visited by the user. Results show that, among ten measures we used, visit frequency and stay duration predict the most significant places more accurately.
Subjects
Location-based Service
Trajectory Analysis
Spatial Data Mining
Conditional Random Field
Clustering
Type
thesis
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