How much past to see the future: a computational study in calibrating urban cellular automata

Abstract

The objective of this computational study was to investigate to which extent the availability and the way of use of historical maps may affect the quality of the calibration process of cellular automata (CA) urban models. The numerical experiments are based on a constrained CA applied to a case study. Since the model depends on a large number of parameters, we optimize the CA using cooperative coevolutionary particle swarms, which is an approach known for its ability to operate effectively in search spaces with a high number of dimensions. To cope with the relevant computational cost related to the high number of CA simulations required by our study, we use a parallelized CA model that takes advantage of the computing power of graphics processing units. The study has shown that the accuracy of simulations can be significantly influenced by both the number and position in time of the historical maps involved in the calibration.

Keywords:

• cooperative coevolution
• GPGPU
• urban models
• cellular automata

Acknowledgement

We gratefully acknowledge the support of nVIDIA Corporation with the donation of the Tesla K40 GPU used for this research.