Visualização interativa de dinâmicas de tráfego através de dados de trajetórias

Abstract

Urbanization is accelerating worldwide, giving rise to serious traffic problems. With the increasing availability of location acquisition technologies, massive movement data are collected continuously in a streaming manner. These data are a valuable source to help transit agencies to identify abnormal events that require immediate attention to better direct traffic. In this regard, visual analytics can help by combining automated analysis with interactive visualization for effective understanding, reasoning, and decision-making. Traditional approaches aggregate movement by employing the concept of time-window discretization and exploring an entire dataset. However, they can present inconsistencies in time and space with the real traffic dynamics. In this thesis, we present a novel approach to discover global and local mobility patterns in real time. Different from other existing approaches, our method tracks the evolution of the objects’ movement in real time. We believe that no other approach captures and keeps track of how the hot routes evolve in an incremental manner. Moreover, we conducted extensive experiments on real-world and simulated datasets to evaluate the effectiveness of our method. We also present the benefits and limitations of our visualization proposal based on domain expert feedback. Finally, we present performance tests with very encouraging results to support our approach in visualizing the total traffic flow of a big city. The results demonstrate that our method scales linearly with the size of the dataset, and is able to deal with large datasets and with streams of high-sampling rates.