EFFICIENT METHODS FOR THE TRAFFIC ASSIGNMENT PROBLEM

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چکیده

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15 صفحه اول

Applying Projection-Based Methods to the Asymmetric Traffic Assignment Problem

This paper examines the application of a path-based algorithm, to the static and fixed demand asymmetric traffic assignment problem. The algorithm is of the simplicial decomposition type and it solves the equilibration or master problem step by means of five existing projection methods for variational inequality problems in order to evaluate their performance on real traffic networks. The proje...

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Efficient Traffic Assignment for Public Transit Networks

We study the problem of computing traffic assignments for public transit networks: Given a public transit network and a demand (i.e. a list of passengers, each with associated origin, destination, and departure time), the objective is to compute the utilization of every vehicle. Efficient assignment algorithms are a core component of many urban traffic planning tools. In this work, we present a...

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Signature Methods for the Assignment Problem

JSTOR is a not-for-profit service that helps scholars, researchers, and students discover, use, and build upon a wide range of content in a trusted digital archive. We use information technology and tools to increase productivity and facilitate new forms of scholarship. For more information about JSTOR, please contact [email protected]. INFORMS is collaborating with JSTOR to digitize, preserve ...

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/ the Multi - Modal Traffic Assignment Problem

Traffic equilibrium analysis 4~s provided useful insight into the transportation planning proce~s. Even for deterministic demand models, though, the state-of-the-art does not include any efficient approach that is applicable for general equilibrium models. Existing convex programming approaches, which are efficient and guarantee convergence, are restrj.cted to single commodity (mode, user class...

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ژورنال

عنوان ژورنال: The International Conference on Applied Mechanics and Mechanical Engineering

سال: 1984

ISSN: 2636-4360

DOI: 10.21608/amme.1984.49924