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On the flexibility of using marginal distribution choice models in traffic equilibrium

On the flexibility of using marginal distribution choice models in traffic equilibrium
On the flexibility of using marginal distribution choice models in traffic equilibrium
Traffic equilibrium models are fundamental to the analysis of transportation systems. The stochastic user equilibrium (SUE) model which relaxes the perfect information assumption of the deterministic user equilibrium is one such model. The aim of this paper is to develop a new user equilibrium model, namely the MDM-SUE model, that uses the marginal distribution model (MDM) as the underlying route choice model. In this choice model, the marginal distributions of the path utilities are specified but the joint distribution is not. By focusing on the joint distribution that maximizes expected utility, we show that MDM-SUE exists and is unique under mild assumptions on the marginal distributions. We develop a convex optimization formulation for the MDM-SUE. For specific choices of marginal distributions, the MDM-SUE model recreates the optimization formulation of logit SUE and weibit SUE. Moreover, the model is flexible since it can capture perception variance scaling at the route level and allows for modeling different user preferences by allowing for skewed distributions and heavy tailed distributions. The model can also be generalized to incorporate bounded support distributions and discrete distributions which allows to distinguish between used and unused routes within the SUE framework. We adapt the method of successive averages to develop an efficient approach to compute MDM-SUE traffic flows. In our numerical experiments, we test the ability of MDM-SUE to relax the assumption that the error terms are independently and identically distributed random variables as in the logit models and study the additional modeling flexibility that MDM-SUE provides on small-sized networks as well as on the large network of the city of Winnipeg. The results indicate that the model provides both modeling flexibility and computational tractability in traffic equilibrium.
Multinomial logit, Weibit, Route choice model, Stochastic user equilibrium, Marginal distribution model
0191-2615
130-158
Ahipasaoglu, Selin Damla
d69f1b80-5c05-4d50-82df-c13b87b02687
Arikan, Ugur
a067331d-3875-4755-bb18-acab5e7db6bb
Natarajan, Karthik
55eeb4e6-5e43-4d43-a836-635694689487
Ahipasaoglu, Selin Damla
d69f1b80-5c05-4d50-82df-c13b87b02687
Arikan, Ugur
a067331d-3875-4755-bb18-acab5e7db6bb
Natarajan, Karthik
55eeb4e6-5e43-4d43-a836-635694689487

Ahipasaoglu, Selin Damla, Arikan, Ugur and Natarajan, Karthik (2016) On the flexibility of using marginal distribution choice models in traffic equilibrium. Transportation Research Part B: Methodological, 91, 130-158. (doi:10.1016/j.trb.2016.05.002).

Record type: Article

Abstract

Traffic equilibrium models are fundamental to the analysis of transportation systems. The stochastic user equilibrium (SUE) model which relaxes the perfect information assumption of the deterministic user equilibrium is one such model. The aim of this paper is to develop a new user equilibrium model, namely the MDM-SUE model, that uses the marginal distribution model (MDM) as the underlying route choice model. In this choice model, the marginal distributions of the path utilities are specified but the joint distribution is not. By focusing on the joint distribution that maximizes expected utility, we show that MDM-SUE exists and is unique under mild assumptions on the marginal distributions. We develop a convex optimization formulation for the MDM-SUE. For specific choices of marginal distributions, the MDM-SUE model recreates the optimization formulation of logit SUE and weibit SUE. Moreover, the model is flexible since it can capture perception variance scaling at the route level and allows for modeling different user preferences by allowing for skewed distributions and heavy tailed distributions. The model can also be generalized to incorporate bounded support distributions and discrete distributions which allows to distinguish between used and unused routes within the SUE framework. We adapt the method of successive averages to develop an efficient approach to compute MDM-SUE traffic flows. In our numerical experiments, we test the ability of MDM-SUE to relax the assumption that the error terms are independently and identically distributed random variables as in the logit models and study the additional modeling flexibility that MDM-SUE provides on small-sized networks as well as on the large network of the city of Winnipeg. The results indicate that the model provides both modeling flexibility and computational tractability in traffic equilibrium.

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More information

Accepted/In Press date: 1 April 2016
e-pub ahead of print date: 21 May 2016
Published date: September 2016
Keywords: Multinomial logit, Weibit, Route choice model, Stochastic user equilibrium, Marginal distribution model

Identifiers

Local EPrints ID: 443177
URI: http://eprints.soton.ac.uk/id/eprint/443177
ISSN: 0191-2615
PURE UUID: 85cadb5c-589d-4dfc-a33e-b5b4067e4316
ORCID for Selin Damla Ahipasaoglu: ORCID iD orcid.org/0000-0003-1371-315X

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Date deposited: 13 Aug 2020 16:38
Last modified: 17 Mar 2024 04:03

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Contributors

Author: Ugur Arikan
Author: Karthik Natarajan

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