Parallel modelling and neural networks: An overview for transportation/land use systems

We provide in this conceptual paper an overview of a parallel transportation/land use modelling environment. We argue that sequential urban modelling does not well represent complex urban dynamics. Instead, we suggest a parallel distributed processing structure composed of processors and links between processors. Each processor is a set of neurons and weights between neurons forming a neural network. For spatial systems neural networks have two main paradigms which are processes simulation and pattern association. Parallel distributed processing offers a new methodology to represent the relational structure between elements of a transportation/land use system and thus helping to model those systems. We also provide a set of advantages, drawbacks and some research directions about the usage of neural networks for spatial analysis and modelling.