Neural network for travel demand forecast using GIS and remote sensing

André Dantas · Keiichi YAMAMOTO · Marcus Vinicius Lamar

16 citations0/0 files storedInternational Joint Conference on Neural Networks (IJCNN)

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Transportation Planning and Optimization Urban Transport and Accessibility Traffic Prediction and Management Techniques

Abstract

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.

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1990Transportation engineering: an introduction 1997Parallel modelling and neural networks: An overview for transportation/land use systems

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2014A taxonomy of demand uncensoring methods in revenue management 2012Travel Mode Choice Modeling: A Comparison of Bayesian Networks and Neural Networks 2012Convergence of LBS and AI 2004Trip generation : comparison of neural networks and regression models

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