Towards a neural network based model for travel demand forecast : GIS and remote sensing approach

Abstract

A formulation of travel forecast model based on geographical–spatial data of urban areas is presented. It focus on land use – transportation system interaction in order to represent displacements (trips) needs of human activities within the urban area. The geographical–spatial data mainly originated from satellite and aerial photograph images, are obtained exploring Geographical Information Systems and Remote Sensing. Neural Networks are used to establish an “artificial intelligent” comprehension of the urban dynamic that affect travel demand process. A case study in Boston Metropolitan Area proved the methodology’s efficiency, given that the consideration of geographical spatial variables resulted in a good result of trip forecast.