Visualisation of uncertainty in probabilistic traffic models for policy and operations

Visualisation of uncertainty in probabilistic traffic models for policy and operations

Calvert, S.C.
Rypkema, J.
Holleman, B.
Azulay, D.
de Jong, A.

2017

This study investigates different methods to visualise uncertainty in static representations of probabilistic traffic models predictions on road-networks. Although various graphical cues may be used to represent uncertainty it is not a priori clear which of them are most suited for this purpose, since their legibility, intelligibility and the degree to which they interfere with other graphical elements in a representation differ widely. Several graphical uncertainty representations were therefore developed and analysed in expert sessions. A selection of the initial set of uncertainty visualisations was further evaluated in a cognitive alternative task-switching experiment. The results show that graphical representations are able to convey uncertainty information relatively accurately, while some uncertainty visualisations outperform others. It depends on the model and scenario which representation is most suited for a given application. This paper presents an overview of possible graphic uncertainty representations and the considerations involved when applying them to uncertainty in traffic model visualisations. © 2015, Springer Science+Business Media New York.

Flow visualization
Visualization
Graphical representations
Probabilistic
Static representation
Task-switching experiment
Traffic modelling
Uncertainty
Uncertainty informations
Uncertainty representation
Uncertainty analysis
Numerical model
Prediction
Probability
Traffic management
Transportation policy

http://resolver.tudelft.nl/uuid:5db17a83-6036-4927-acda-3fa90c55ee80

https://doi.org/10.1007/s11116-015-9673-3

954855

Transportation, 44 (44), 701-729

article