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Investigation on the Use of Graph Signal Processing for an Intelligent Taxis Transportation System
This thesis demonstrates the benefits of using Graph Signal Processing (GSP) techniques for an intelligent taxis transportation system. Graph Signal Processing, an application arising to handle multiple source signals on a graph, has developed into an active field of research during the last several years due to its ability to analyze enormous datasets or dynamic data that usually pose a challenge to researchers. One of the most significant operations of Graph Signal Processing that arises in many areas is noise reduction. This thesis introduces a possible method of using Graph Signal Processing and its operations to analyze signals in a network of taxi stand locations. Two examples are given using real data of taxis' and stands' locations in San Francisco where the number of taxis around these stands is the detected signal. The results showed the effectiveness of using Graph Fourier Transform to detect the anomalies in the signals which represent unusual transportation activities or driver distributions within the taxi network. Signal denoising is addressed by using four techniques which are often based on the signal filtering methods. The first technique used the low pass filter, followed by a harmonic decline filter, and then standard and modified Kalman filters, including the case for uncertain observation or process noise between the standard and adaptive Kalman filters. The results are compared with the other filters.
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