We present a methodology and a planning and design support software tool for evaluating walkability and pedestrian accessibility of places which are relevant for people’s capabilities, and thus an important component of quality of life in cities. A multicriteria evaluation model, at the core of the decision support system, is used to assign walkability scores to points in urban space. Walkability scores are obtained through algorithms which process spatial data and run the evaluation model in order to derive potential pedestrian routes along the street network, taking into account the quality of urban space on several attributes relevant for walkability. One of its notable characteristics is a certain reversal of perspective in evaluating walkability: the walkability score of a place does not reflect how that place is per se walkable, but instead how and where to can one walk from there, that is to say, what is the walkability the place is endowed with. This evaluation incorporates three intertwined elements: the number of destinations/opportunities reachable by foot, their walking distances, and the quality of the paths to these destinations. In this article, we furthermore demonstrate possible uses of the support system by reporting and discussing the results of a case-study assessment of a project for the Lisbon’s Segunda Circular (Second Ring Road). The software tool is made freely available for download.

In this paper the possibility of using cellular automata (CA) to build and test urban development models is explored. The AUGH project, which aims to construct a generic software environment for building urban automata, is also described. After careful evaluation it is concluded that CA, and other techniques from within the artificial worlds paradigm, are especially useful for representing those socioeconomic and urban development phenomena which are shaped by individual choices and decisions.

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