Neural network for travel demand forecast using GIS and remote sensing

Describes an application of neural networks in the development of a travel forecast model for transportation planning. The model intends to quantify trips within the urban area through the representation of the land use-transportation system interaction. The data to express such a complex interaction is mainly obtained from remote sensing images that are processed in a geographical information system. We present the model's basic formulation and the results of a case study conducted in the Boston metropolitan area.