@InProceedings{GomesSantVida:2017:InViTr,
author = "Gomes, George Allan Menezes and Santos, Emanuele and Vidal, Creto
A.",
affiliation = "Federal University of Cear{\'a}, Fortaleza, Brazil and Federal
University of Cear{\'a}, Fortaleza, Brazil and Federal University
of Cear{\'a}, Fortaleza, Brazil",
title = "Interactive Visualization of Traffic Dynamics Based on Trajectory
Data",
booktitle = "Proceedings...",
year = "2017",
editor = "Torchelsen, Rafael Piccin and Nascimento, Erickson Rangel do and
Panozzo, Daniele and Liu, Zicheng and Farias, Myl{\`e}ne and
Viera, Thales and Sacht, Leonardo and Ferreira, Nivan and Comba,
Jo{\~a}o Luiz Dihl and Hirata, Nina and Schiavon Porto, Marcelo
and Vital, Creto and Pagot, Christian Azambuja and Petronetto,
Fabiano and Clua, Esteban and Cardeal, Fl{\'a}vio",
organization = "Conference on Graphics, Patterns and Images, 30. (SIBGRAPI)",
publisher = "IEEE Computer Society",
address = "Los Alamitos",
keywords = "interactive visualization, traffic visualization, traffic
patterns, spatiotemporal, visual exploration.",
abstract = "Urbanization is accelerating worldwide, giving rise to serious
traffic problems. Traffic wave, known as stop-and-go traffic or
phantom intersection, is one of the most significant traffic
oscillation patterns studied in Traffic Engineering. Usually these
studies are numerical experiments that investigate specific
places, such as a crossroad or a highway section, and their
findings cannot, therefore, be easily applied to sensing device
data in a systematic computational approach. In this regard,
visual analytics can help by combining automated analysis with
interactive visualization for effective understanding, reasoning,
and decision-making. In this paper, we present a novel approach
for visualizing traffic oscillation patterns by visualizing the
objects' movement in space over time, inspired by vector field
visualization. We propose an algorithm to control and synchronize
the visualization time; a systematic stepwise methodology for
exploring sensing device data; and a visualization tool that
computes the trajectory data in parallel on the GPU at interactive
frame rates. Moreover, our approach is designed to support both
batch-processed and streaming data applications. 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.",
conference-location = "Niter{\'o}i, RJ, Brazil",
conference-year = "17-20 Oct. 2017",
doi = "10.1109/SIBGRAPI.2017.21",
url = "http://dx.doi.org/10.1109/SIBGRAPI.2017.21",
language = "en",
ibi = "8JMKD3MGPAW/3PFQJLH",
url = "http://urlib.net/ibi/8JMKD3MGPAW/3PFQJLH",
targetfile = "PID4959881.pdf",
urlaccessdate = "2024, Apr. 29"
}