Emergence of System Optimum: A Fair and Altruistic Agent-based Route-choice Model

Nadav Levy; Eran Ben-Elia

doi:https://doi.org/10.1016/j.procs.2016.04.187

Emergence of System Optimum: A Fair and Altruistic Agent-based Route-choice Model

Nadav Levy, Eran Ben-Elia

Department of Environmental, Geoinformatics and Urban Planning Sciences

Ben-Gurion University of the Negev

Research output: Contribution to journal › Conference article › peer-review

Abstract

The System Optimum, an optimal traffic assignment that minimizes the total travel costs on the road network is usually only referred to as a comparison to self-emerging user equilibrium. In this paper we investigate how different behavioral aspects of drivers can self-organize towards a system optimum that minimizes travel costs while providing benefits and preserving equity among drivers. We present a simple binary route-choice Agent-Based Model that provides a disaggregated view of driver behavior and a unique understanding of the potential of cognitive reinforcement models to effect a convergence to user equilibrium and a shift in driver behavior toward a system optimum without the need for an enforcing traffic policy such as tolls.

Original language	American English
Pages (from-to)	928-933
Number of pages	6
Journal	Procedia Computer Science
Volume	83
DOIs	https://doi.org/10.1016/j.procs.2016.04.187
State	Published - 1 Jan 2016
Event	7th International Conference on Ambient Systems, Networks and Technologies, ANT 2016 and the 6th International Conference on Sustainable Energy Information Technology, SEIT 2016 - Madrid, Spain Duration: 23 May 2016 → 26 May 2016

Keywords

Agent-Based
Route-Choice
Social Optimum
User Equilibrium

All Science Journal Classification (ASJC) codes

General Computer Science

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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https://doi.org/10.1016/j.procs.2016.04.187

Cite this

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title = "Emergence of System Optimum: A Fair and Altruistic Agent-based Route-choice Model",

abstract = "The System Optimum, an optimal traffic assignment that minimizes the total travel costs on the road network is usually only referred to as a comparison to self-emerging user equilibrium. In this paper we investigate how different behavioral aspects of drivers can self-organize towards a system optimum that minimizes travel costs while providing benefits and preserving equity among drivers. We present a simple binary route-choice Agent-Based Model that provides a disaggregated view of driver behavior and a unique understanding of the potential of cognitive reinforcement models to effect a convergence to user equilibrium and a shift in driver behavior toward a system optimum without the need for an enforcing traffic policy such as tolls.",

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T2 - 7th International Conference on Ambient Systems, Networks and Technologies, ANT 2016 and the 6th International Conference on Sustainable Energy Information Technology, SEIT 2016

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AU - Ben-Elia, Eran

PY - 2016/1/1

Y1 - 2016/1/1

N2 - The System Optimum, an optimal traffic assignment that minimizes the total travel costs on the road network is usually only referred to as a comparison to self-emerging user equilibrium. In this paper we investigate how different behavioral aspects of drivers can self-organize towards a system optimum that minimizes travel costs while providing benefits and preserving equity among drivers. We present a simple binary route-choice Agent-Based Model that provides a disaggregated view of driver behavior and a unique understanding of the potential of cognitive reinforcement models to effect a convergence to user equilibrium and a shift in driver behavior toward a system optimum without the need for an enforcing traffic policy such as tolls.

AB - The System Optimum, an optimal traffic assignment that minimizes the total travel costs on the road network is usually only referred to as a comparison to self-emerging user equilibrium. In this paper we investigate how different behavioral aspects of drivers can self-organize towards a system optimum that minimizes travel costs while providing benefits and preserving equity among drivers. We present a simple binary route-choice Agent-Based Model that provides a disaggregated view of driver behavior and a unique understanding of the potential of cognitive reinforcement models to effect a convergence to user equilibrium and a shift in driver behavior toward a system optimum without the need for an enforcing traffic policy such as tolls.

KW - Agent-Based

KW - Route-Choice

KW - Social Optimum

KW - User Equilibrium

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DO - https://doi.org/10.1016/j.procs.2016.04.187

M3 - Conference article

SN - 1877-0509

VL - 83

SP - 928

EP - 933

JO - Procedia Computer Science

JF - Procedia Computer Science

Y2 - 23 May 2016 through 26 May 2016

ER -

Emergence of System Optimum: A Fair and Altruistic Agent-based Route-choice Model

Abstract

Keywords

All Science Journal Classification (ASJC) codes

UN SDGs

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