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2015
DOI: 10.1016/j.trb.2015.03.008
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Stochastic user equilibrium with equilibrated choice sets: Part I – Model formulations under alternative distributions and restrictions

Abstract: The aim of this paper is to remove the known limitations of Deterministic and Stochastic User Equilibrium (DUE and SUE), namely that only routes with the minimum cost are used in DUE, and that all permitted routes are used in SUE regardless of their costs. We achieve this by combining the advantages of the two principles, namely the definition of unused routes in DUE and mis-perception in SUE, such that the resulting choice sets of used routes are equilibrated. Two model families are formulated to address this… Show more

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Cited by 35 publications
(29 citation statements)
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“…The model for congestion used in this paper is presented in detail in two papers [17] [14] by the inventors. This work presents two different implementations, one in MATLAB and one in ArcGIS (with extra extensions for route choice modelling).…”
Section: Related Workmentioning
confidence: 99%
“…The model for congestion used in this paper is presented in detail in two papers [17] [14] by the inventors. This work presents two different implementations, one in MATLAB and one in ArcGIS (with extra extensions for route choice modelling).…”
Section: Related Workmentioning
confidence: 99%
“…Bringing together these elements, the equilibrium conditions are defined as (for details, see Watling et al, 2014):…”
Section: Restricted Stochastic User Equilibriummentioning
confidence: 99%
“…The RSUE conditions, unlike the DUE and SUE, do not provide uniqueness in link flows under the usual sufficient conditions, nor in path flows, as in general the set of used paths may not be uniquely defined by the RSUE conditions (see Watling et al, 2014). Consequently, a given linkflow allocation cannot be mapped to an RSUE path-flow solution via a one-to-one relation, and in any case, the RSUE conditions may be satisfied by multiple link-flow solutions.…”
Section: Solving In the Space Of Link-or Path Flowsmentioning
confidence: 99%
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