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Reference TypeConference Paper (Conference Proceedings)
Last Update2017: administrator
Metadata Last Update2020: administrator
Citation KeyGomesSantVida:2017:InViTr
TitleInteractive Visualization of Traffic Dynamics Based on Trajectory Data
Access Date2021, Jan. 25
Number of Files1
Size2276 KiB
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Author1 Gomes, George Allan Menezes
2 Santos, Emanuele
3 Vidal, Creto A.
Affiliation1 Federal University of Ceará, Fortaleza, Brazil
2 Federal University of Ceará, Fortaleza, Brazil
3 Federal University of Ceará, Fortaleza, Brazil
EditorTorchelsen, Rafael Piccin
Nascimento, Erickson Rangel do
Panozzo, Daniele
Liu, Zicheng
Farias, Mylène
Viera, Thales
Sacht, Leonardo
Ferreira, Nivan
Comba, João Luiz Dihl
Hirata, Nina
Schiavon Porto, Marcelo
Vital, Creto
Pagot, Christian Azambuja
Petronetto, Fabiano
Clua, Esteban
Cardeal, Flávio
Conference NameConference on Graphics, Patterns and Images, 30 (SIBGRAPI)
Conference LocationNiterói, RJ
DateOct. 17-20, 2017
Book TitleProceedings
PublisherIEEE Computer Society
Publisher CityLos Alamitos
Tertiary TypeFull Paper
History2017-08-21 17:31:23 :: -> administrator ::
2020-02-19 02:01:30 :: administrator -> :: 2017
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Is the master or a copy?is the master
Content Stagecompleted
Content TypeExternal Contribution
Keywordsinteractive visualization, traffic visualization, traffic patterns, spatiotemporal, visual exploration.
AbstractUrbanization 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.
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