Neural Network for Travel Demand Forecast using GIS and Remote Sensing
This paper 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, in this paper, model’s basic formulation and the results of a case study conducted in Boston metropolitan area.