The reference point in dynamic Prospect-based User Equilibrium: a simulation study

ABSTRACT

In this paper, we revisit the concept of Prospect-based User Equilibrium in a dynamic context. We consider a mesoscopic Lighthill-Whitham-Richards (LWR) traffic model to determine time-dependent route costs. We propose a solution algorithm to determine the network equilibria. Monte Carlo simulations are used to account for the travel time distributions. We analyze the dynamic Prospect-based User Equilibrium compared to the benchmarks Deterministic and Stochastic User Equilibrium, on a synthetic Manhattan network. We set four endogenous reference points. We show that the setting of the reference point plays a very important role in the route flow patterns and on the network performance at an aggregated level, i.e. in terms of vehicles mean speed as well as internal and outflow capacities. Our results also enhance that the Prospect-based User Equilibrium is more sensitive to a change in the reference point than in the calibration of the users’ risk-aversion and risk-seeking parameters.

KEYWORDS:

• Risk-seeking and risk-aversion users’ behavior
• Prospect theory
• dynamic traffic assignment
• network performance
• LWR traffic model

Article Highlights

• We enhance the importance of the reference point in the application of Prospect Theory to route choice.

• We investigate the influence of users risk-seeking and risk-aversion behavior in the traffic network performance.

• We propose the first implementation of Prospect Theory in a dynamic context, where travel costs are determined by a LWR traffic model.

• We propose a solution algorithm to calculate the dynamic Prospect-based User Equilibrium.

• We show that the concepts of disutility minimization and prospect maximization give different route flow patterns, depending on the reference point.

Acknowledgments

We thank the anonymous reviewers for their comments and suggestions and have improved this manuscript.

Disclosure statement

No potential conflict of interest was reported by the authors.

Correction Statement

This article has been republished with minor changes. These changes do not impact the academic content of the article.

Additional information

Funding

S. F. A. Batista and L. Leclercq acknowledge funding by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation program (grant agreement No 646592 - MAGnUM project). S. F. A. Batista also acknowledges funding from the Region Auvergne-Rhone-Alpes (France).