A Recommendation System based on Clustering and Classification for Optimal Trajectory Analysis

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  • Moving objects such as people, animals, and vehicles have generated a huge amount of spatiotemporal data by using location-capture technologies and mobile devices. There is a high demand to analyze this collected data and extract the desired knowledge. In this study, we apply several data mining techniques on a trajectory dataset such as clustering, classification, sequential pattern mining, and time series analysis. Our model can detect the movement patterns of taxi trips in Porto city. We apply the Naïve Bayes classifier to predict the traffic status of each trip. We perform qualitative and quantitative analysis for our clustering method, then we evaluate the accuracy of the Naïve Bayes classifier. Finally, we discuss the implications of our methodology in terms of traffic jams, energy consumption, and air pollution. Our analysis results can be used to build a recommender system which can be beneficial for taxi drivers, passengers, and transportation authorities.

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  • Copyright © 2019 the author(s). Theses may be used for non-commercial research, educational, or related academic purposes only. Such uses include personal study, research, scholarship, and teaching. Theses may only be shared by linking to Carleton University Institutional Repository and no part may be used without proper attribution to the author. No part may be used for commercial purposes directly or indirectly via a for-profit platform; no adaptation or derivative works are permitted without consent from the copyright owner.
Date Created
  • 2019


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