A prospect-based user equilibrium model with endogenous reference points and its application in congestion pricing

2011 ◽  
Vol 45 (2) ◽  
pp. 311-328 ◽  
Author(s):  
Hongli Xu ◽  
Yingyan Lou ◽  
Yafeng Yin ◽  
Jing Zhou
Keyword(s):  
Equilibrium Model ◽  
Congestion Pricing ◽  
User Equilibrium ◽  
Reference Points ◽  
Endogenous Reference

Expectations-Based Reference-Dependence and Choice Under Risk

2019 ◽  
Vol 129 (622) ◽  
pp. 2424-2458
Author(s):  
David J Freeman
Keyword(s):  
Equilibrium Model ◽  
Revealed Preference ◽  
Reference Points ◽  
Independence Axiom ◽  
Reference Dependence ◽  
Endogenous Reference ◽  
Personal Equilibrium ◽  
Weak Axiom ◽  
Reference Dependent Preferences

Abstract This article characterises the behavioural content of a model of choice under risk with reference-dependent preferences and endogenous expectations-based reference points based on the preferred personal equilibrium model of Kőszegi and Rabin (2006). The combination of reference-dependent preferences and endogenous reference points leads to violations of the Independence Axiom and can also lead to violations of the Weak Axiom of Revealed Preference. An axiomatic characterisation shows that the model places testable restrictions on choice under risk.

scholarly journals A regret-based route choice model with asymmetric preference in a stochastic network

2018 ◽  
Vol 10 (8) ◽  
pp. 168781401879323
Author(s):  
Lei Zhao ◽  
Hongzhi Guan ◽  
Xinjie Zhang ◽  
Xiongbin Wu
Keyword(s):  
Loss Aversion ◽  
Equilibrium Model ◽  
Choice Model ◽  
User Equilibrium ◽  
Stochastic User Equilibrium ◽  
Regret Minimization ◽  
Route Network ◽  
Travel Decision ◽  
Gains And Losses ◽  
Random Regret Minimization

In this study, a stochastic user equilibrium model on the modified random regret minimization is proposed by incorporating the asymmetric preference for gains and losses to describe its effects on the regret degree of travelers. Travelers are considered to be capable of perceiving the gains and losses of attributes separately when comparing between the alternatives. Compared to the stochastic user equilibrium model on the random regret minimization model, the potential difference of emotion experienced induced by the loss and gain in the equal size is jointly caused by the taste parameter and loss aversion of travelers in the proposed model. And travelers always tend to use the routes with the minimum perceived regret in the travel decision processes. In addition, the variational inequality problem of the stochastic user equilibrium model on the modified random regret minimization model is given, and the characteristics of its solution are discussed. A route-based solution algorithm is used to resolve the problem. Numerical results given by a three-route network show that the loss aversion produces a great impact on travelers’ choice decisions and the model can more flexibly capture the choice behavior than the existing models.

scholarly journals A Two-Class Stochastic Network Equilibrium Model under Adverse Weather Conditions

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Chenming Jiang ◽  
Linjun Lu ◽  
Junliang He ◽  
Caimao Tan
Keyword(s):  
Travel Time ◽  
Equilibrium Model ◽  
Route Choice ◽  
Weather Condition ◽  
Weather Conditions ◽  
User Equilibrium ◽  
Network Equilibrium ◽  
Adverse Weather Conditions ◽  
Adverse Weather Condition ◽  
Adverse Weather

Adverse weather condition is one of the inducements that lead to supply uncertainty of an urban transportation system, while travelers’ multiple route choice criteria are the nonignorable reason resulting in demand uncertainty. This paper proposes a novel stochastic traffic network equilibrium model considering impacts of adverse weather conditions on roadway capacity and route choice criteria of two-class mixed roadway travellers on demand modes, in which the two-class route choice criteria root in travelers’ different network information levels (NILs). The actual route travel time (ARTT) and perceived route travel time (PRTT) are considered as the route choice criteria of travelers with perfect information (TPI) and travelers with bounded information (TBI) under adverse weather conditions, respectively. We then formulate the user equilibrium (UE) traffic assignment model in a variational inequality problem and propose a solution algorithm. Numerical examples including a small triangle network and the Sioux Falls network are presented to testify the validity of the model and to clarify the inner mechanism of the two-class UE model under adverse weather conditions. Managerial implications and applications are also proposed based on our findings to improve the operation efficiency of urban roadway network under adverse weather conditions.

Optimal congestion pricing toll design for revenue maximization: comprehensive numerical results and implications

2015 ◽  
Vol 42 (8) ◽  
pp. 544-551 ◽  
Author(s):  
Wei Fan
Keyword(s):  
Operating Cost ◽  
Congestion Pricing ◽  
User Equilibrium ◽  
Numerical Results ◽  
Revenue Maximization ◽  
Value Of Time ◽  
Elastic Demand ◽  
Upper Level ◽  
Demand Sensitivity ◽  
The Impact

The purpose of this paper is to present bi-level optimization models and develop a genetic algorithm (GA) based method to solve the optimal congestion pricing toll design problem and to determine the second-best link-based optimal toll locations and toll levels simultaneously. The upper-level subprogram is to maximize the toll revenue collected while explicitly accounting for the toll booth setting up and operating cost, given certain toll level constraints. The lower-level subprogram is a traditional user equilibrium problem with elastic demand. The proposed GA model is applied to the Sioux Falls network, which has 76 links and 24 origin–destination pairs, assuming homogeneous users. Comprehensive numerical results including solutions achieved under continuous tolling and discrete tolling schemes, tolling on optimized links and tolling on heuristically selected most congested links are carefully presented and compared. The impact of value of time and the elastic demand sensitivity are also comprehensively investigated.

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