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Open AccessCommunication

Exploiting Spatial Abstraction in Predictive Analytics of Vehicle Traffic

1
Fraunhofer Institute IAIS, Schloss Birlinghoven, 53757 Sankt Augustin, Germany
2
Department of Computer Science, City University London, Northamton Sqaure, London EC1V OHB, UK
3
Istituto di Scienza e Tecnologie dell'Informazione, Consiglio Nazionale delle Ricerche, 56124 Pisa, Italy
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.This communication paper extends an unpublished discussion presentation at IEEE VIS 2014 Workshop on Visualization for Predictive Analytics, Paris, November 2014.
Academic Editors: Jochen Schiewe and Wolfgang Kainz
ISPRS Int. J. Geo-Inf. 2015, 4(2), 591-606; https://doi.org/10.3390/ijgi4020591
Received: 2 December 2014 / Revised: 20 February 2015 / Accepted: 7 April 2015 / Published: 15 April 2015
(This article belongs to the Special Issue Geo-Information Fostering Innovative Solutions for Smart Cities)
By applying visual analytics techniques to vehicle traffic data, we found a way to visualize and study the relationships between the traffic intensity and movement speed on links of a spatially abstracted transportation network. We observed that the traffic intensities and speeds in an abstracted network are interrelated in the same way as they are in a detailed street network at the level of street segments. We developed interactive visual interfaces that support representing these interdependencies by mathematical models. To test the possibility of utilizing them for performing traffic simulations on the basis of abstracted transportation networks, we devised a prototypical simulation algorithm employing these dependency models. The algorithm is embedded in an interactive visual environment for defining traffic scenarios, running simulations, and exploring their results. Our research demonstrates a principal possibility of performing traffic simulations on the basis of spatially abstracted transportation networks using dependency models derived from real traffic data. This possibility needs to be comprehensively investigated and tested in collaboration with transportation domain specialists. View Full-Text
Keywords: visual analytics; mobility; traffic modeling; traffic simulation visual analytics; mobility; traffic modeling; traffic simulation
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Andrienko, N.; Andrienko, G.; Rinzivillo, S. Exploiting Spatial Abstraction in Predictive Analytics of Vehicle Traffic. ISPRS Int. J. Geo-Inf. 2015, 4, 591-606.

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