Hub Map: A new approach for visualizing traffic data sets with multi-attribute link data

Andrew Simmons; Iman Avazpour; Hai L. Vu; Rajesh Vasa

doi:10.1109/VLHCC.2015.7357220

Hub Map: A new approach for visualizing traffic data sets with multi-attribute link data

Andrew Simmons, Iman Avazpour, Hai L. Vu, Rajesh Vasa

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Other

3 Citations (Scopus)

Abstract

Visualizing road traffic datasets involves representing junctions, their links, and the attributes of those links. Current traffic visualization techniques are not sufficient for professional traffic engineers, as they are limited in the number of attributes that can be represented. This paper proposes a new approach to visualize multiple attributes on graph edges without compromising their visibility. In particular, we introduce a parameterized connector symbol that increases the number of attributes that can be displayed on graph edges. We demonstrate that our approach can significantly increase the number of traffic parameters that can be displayed compared to existing traffic visualizations.

Original language	English
Title of host publication	Proceedings - 2015 IEEE Symposium on Visual Languages and Human-Centric Computing, VL/HCC 2015
Subtitle of host publication	18 - 22 October, 2015 Atlanta, USA
Editors	Zhen Li, Claudia Ermel, Scott D. Fleming
Place of Publication	Piscataway, NJ
Publisher	IEEE, Institute of Electrical and Electronics Engineers
Pages	219-223
Number of pages	5
ISBN (Electronic)	9781467374576, 9781467374569
DOIs	https://doi.org/10.1109/VLHCC.2015.7357220
Publication status	Published - 14 Dec 2015
Externally published	Yes
Event	IEEE Symposium on Visual Languages and Human-Centric Computing 2015 - Atlanta, United States of America Duration: 18 Oct 2015 → 22 Oct 2015 https://ieeexplore.ieee.org/xpl/conhome/7347691/proceeding (Proceedings)

Conference

Conference	IEEE Symposium on Visual Languages and Human-Centric Computing 2015
Abbreviated title	VL/HCC 2015
Country/Territory	United States of America
City	Atlanta
Period	18/10/15 → 22/10/15
Internet address	https://ieeexplore.ieee.org/xpl/conhome/7347691/proceeding (Proceedings)

Access to Document

10.1109/VLHCC.2015.7357220

Cite this

Simmons, A., Avazpour, I., Vu, H. L., & Vasa, R. (2015). Hub Map: A new approach for visualizing traffic data sets with multi-attribute link data. In Z. Li, C. Ermel, & S. D. Fleming (Eds.), Proceedings - 2015 IEEE Symposium on Visual Languages and Human-Centric Computing, VL/HCC 2015: 18 - 22 October, 2015 Atlanta, USA (pp. 219-223). Article 7357220 IEEE, Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/VLHCC.2015.7357220

Simmons, Andrew ; Avazpour, Iman ; Vu, Hai L. et al. / Hub Map : A new approach for visualizing traffic data sets with multi-attribute link data. Proceedings - 2015 IEEE Symposium on Visual Languages and Human-Centric Computing, VL/HCC 2015: 18 - 22 October, 2015 Atlanta, USA. editor / Zhen Li ; Claudia Ermel ; Scott D. Fleming. Piscataway, NJ : IEEE, Institute of Electrical and Electronics Engineers, 2015. pp. 219-223

@inproceedings{3a2016cdea9f49a8a29b2e419b8fc6a3,

title = "Hub Map: A new approach for visualizing traffic data sets with multi-attribute link data",

abstract = "Visualizing road traffic datasets involves representing junctions, their links, and the attributes of those links. Current traffic visualization techniques are not sufficient for professional traffic engineers, as they are limited in the number of attributes that can be represented. This paper proposes a new approach to visualize multiple attributes on graph edges without compromising their visibility. In particular, we introduce a parameterized connector symbol that increases the number of attributes that can be displayed on graph edges. We demonstrate that our approach can significantly increase the number of traffic parameters that can be displayed compared to existing traffic visualizations.",

author = "Andrew Simmons and Iman Avazpour and Vu, {Hai L.} and Rajesh Vasa",

year = "2015",

month = dec,

day = "14",

doi = "10.1109/VLHCC.2015.7357220",

language = "English",

pages = "219--223",

editor = "Zhen Li and Ermel, {Claudia } and Fleming, {Scott D. }",

booktitle = "Proceedings - 2015 IEEE Symposium on Visual Languages and Human-Centric Computing, VL/HCC 2015",

publisher = "IEEE, Institute of Electrical and Electronics Engineers",

address = "United States of America",

note = "IEEE Symposium on Visual Languages and Human-Centric Computing 2015, VL/HCC 2015 ; Conference date: 18-10-2015 Through 22-10-2015",

url = "https://ieeexplore.ieee.org/xpl/conhome/7347691/proceeding",

}

Simmons, A, Avazpour, I, Vu, HL & Vasa, R 2015, Hub Map: A new approach for visualizing traffic data sets with multi-attribute link data. in Z Li, C Ermel & SD Fleming (eds), Proceedings - 2015 IEEE Symposium on Visual Languages and Human-Centric Computing, VL/HCC 2015: 18 - 22 October, 2015 Atlanta, USA., 7357220, IEEE, Institute of Electrical and Electronics Engineers, Piscataway, NJ, pp. 219-223, IEEE Symposium on Visual Languages and Human-Centric Computing 2015, Atlanta, Georgia, United States of America, 18/10/15. https://doi.org/10.1109/VLHCC.2015.7357220

Hub Map: A new approach for visualizing traffic data sets with multi-attribute link data. / Simmons, Andrew; Avazpour, Iman; Vu, Hai L. et al.
Proceedings - 2015 IEEE Symposium on Visual Languages and Human-Centric Computing, VL/HCC 2015: 18 - 22 October, 2015 Atlanta, USA. ed. / Zhen Li; Claudia Ermel; Scott D. Fleming. Piscataway, NJ: IEEE, Institute of Electrical and Electronics Engineers, 2015. p. 219-223 7357220.

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Other

TY - GEN

T1 - Hub Map

T2 - IEEE Symposium on Visual Languages and Human-Centric Computing 2015

AU - Simmons, Andrew

AU - Avazpour, Iman

AU - Vu, Hai L.

AU - Vasa, Rajesh

PY - 2015/12/14

Y1 - 2015/12/14

N2 - Visualizing road traffic datasets involves representing junctions, their links, and the attributes of those links. Current traffic visualization techniques are not sufficient for professional traffic engineers, as they are limited in the number of attributes that can be represented. This paper proposes a new approach to visualize multiple attributes on graph edges without compromising their visibility. In particular, we introduce a parameterized connector symbol that increases the number of attributes that can be displayed on graph edges. We demonstrate that our approach can significantly increase the number of traffic parameters that can be displayed compared to existing traffic visualizations.

AB - Visualizing road traffic datasets involves representing junctions, their links, and the attributes of those links. Current traffic visualization techniques are not sufficient for professional traffic engineers, as they are limited in the number of attributes that can be represented. This paper proposes a new approach to visualize multiple attributes on graph edges without compromising their visibility. In particular, we introduce a parameterized connector symbol that increases the number of attributes that can be displayed on graph edges. We demonstrate that our approach can significantly increase the number of traffic parameters that can be displayed compared to existing traffic visualizations.

UR - http://www.scopus.com/inward/record.url?scp=84959878193&partnerID=8YFLogxK

U2 - 10.1109/VLHCC.2015.7357220

DO - 10.1109/VLHCC.2015.7357220

M3 - Conference Paper

AN - SCOPUS:84959878193

SP - 219

EP - 223

BT - Proceedings - 2015 IEEE Symposium on Visual Languages and Human-Centric Computing, VL/HCC 2015

A2 - Li, Zhen

A2 - Ermel, Claudia

A2 - Fleming, Scott D.

PB - IEEE, Institute of Electrical and Electronics Engineers

CY - Piscataway, NJ

Y2 - 18 October 2015 through 22 October 2015

ER -

Simmons A, Avazpour I, Vu HL, Vasa R. Hub Map: A new approach for visualizing traffic data sets with multi-attribute link data. In Li Z, Ermel C, Fleming SD, editors, Proceedings - 2015 IEEE Symposium on Visual Languages and Human-Centric Computing, VL/HCC 2015: 18 - 22 October, 2015 Atlanta, USA. Piscataway, NJ: IEEE, Institute of Electrical and Electronics Engineers. 2015. p. 219-223. 7357220 doi: 10.1109/VLHCC.2015.7357220

Hub Map: A new approach for visualizing traffic data sets with multi-attribute link data

Abstract

Conference

Access to Document

Other files and links

Cite this