Visual Analysis of Multivariate Urban Traffic Data Resorting to Local Principal Curves

Silva, Carla; d'Orey, Pedro; Aguiar, Ana

Visual Analysis of Multivariate Urban Traffic Data Resorting to Local Principal Curves

Files

013-017.pdf (1.7 MB)

Date

2019

Authors

Silva, Carla
d'Orey, Pedro
Aguiar, Ana

Publisher

The Eurographics Association

Abstract

Traffic congestion causes major economic, environmental and social problems in modern cities. We present an interactive visualization tool to assist domain experts on the identification and analysis of traffic patterns at a city scale making use of multivariate empirical urban data and fundamental diagrams. The proposed method combines visualization techniques with an improved local principle curves method to model traffic dynamics and facilitate comparison of traffic patterns - resorting to the fitted curve with a confidence interval - between different road segments and for different external conditions. We demonstrate the proposed technique in an illustrative real-world case study in the city of Porto, Portugal.

        @inproceedings{10.2312:mlvis.20191159
,
booktitle = {Machine Learning Methods in Visualisation for Big Data
},
editor = {Archambault, Daniel and Nabney, Ian and Peltonen, Jaakko
},
title = {{Visual Analysis of Multivariate Urban Traffic Data Resorting to Local Principal Curves
}},
author = {Silva, Carla and 
d'Orey, Pedro and 
Aguiar, Ana
},
year = {2019
},
publisher = {The Eurographics Association
},
ISBN = {978-3-03868-089-5
},
DOI = {10.2312/mlvis.20191159
}
}