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Solving a Dynamic User Equilibrium model based on splitting rates with Gradient Projection algorithms
Institution:1. Department of Civil, Architectural, and Environmental Engineering, The University of Texas at Austin, United States;2. Department of Civil and Environmental Engineering, Portland State University, United States;3. Department of Computer Science, The University of Texas at Austin, United States;4. Department of Civil Engineering, Indian Institute of Science, India;1. Institute for Transport Studies, The University of Leeds, Leeds, United Kingdom;2. Intelligent Transport Systems Lab, The Swinburne University of Technology, Melbourne, Australia;1. Univ. Lyon, ENTPE, IFSTTAR, LICIT, Lyon F-69518, France;2. School of Civil and Transportation Engineering, Beijing University of Civil Engineering and Architecture, Beijing 100044, China
Abstract:This article shows how Gradient Projection (GP) algorithms are capable of solving with high precision a Dynamic User Equilibrium (UE) model based on Splitting Rates, i.e. turning movements fractions by destination.Dynamic Traffic Assignment (DTA) is formulated as a Variational Inequality problem defined on temporal profiles of arc conditional probabilities that express a sequence of deterministic route choices taken at nodes by road users directed toward each destination.Congestion is represented through a macroscopic traffic model capable to reproduce a range of phenomena having increasing complexity, from links with bottleneck to intersections with spillback. Different time discretizations, from few seconds to few minutes, are also possible, which allows a range of applications from planning to operation.This assignment model, which is fully link based, is proved to be equivalent to a path based formulation. It also allows for the computation of a handy gap function for analyzing convergence to equilibrium.Numerical experiments on test networks are presented, showing that the proposed GP algorithms converge to dynamic equilibrium in a reasonable number of iterations, outperforming the Method of Successive Averages (MSA).
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