scholarly journals randregret: A command for fitting random regret minimization models using Stata

2021 ◽  
Vol 21 (3) ◽  
pp. 626-658
Author(s):  
Álvaro A. Gutiérrez-Vargas ◽  
Michel Meulders ◽  
Martina Vandebroek
Keyword(s):  
Likelihood Ratio Tests ◽  
Model Specification ◽  
Stated Choice ◽  
Choice Data ◽  
Score Functions ◽  
Regret Minimization ◽  
Transportation Research ◽  
Analytical Expressions ◽  
Random Regret Minimization ◽  
Transportation Research Part

In this article, we describe the randregret command, which implements a variety of random regret minimization (RRM) models. The command allows the user to apply the classic RRM model introduced in Chorus (2010, European Journal of Transport and Infrastructure Research 10: 181–196), the generalized RRM model introduced in Chorus (2014, Transportation Research, Part B 68: 224–238), and also the µRRM and pure RRM models, both introduced in van Cranenburgh, Guevara, and Chorus (2015, Transportation Research, Part A 74: 91–109). We illustrate the use of the randregret command by using stated choice data on route preferences. The command offers robust and cluster standarderror correction using analytical expressions of the score functions. It also offers likelihood-ratio tests that can be used to assess the relevance of a given model specification. Finally, users can obtain the predicted probabilities from each model by using the randregretpred command.

scholarly journals Values of Travel Time for Recreational Trips under Different Behavioural Rules

2021 ◽  
Vol 13 (12) ◽  
pp. 6831
Author(s):  
Rosa Marina González ◽  
Concepción Román ◽  
Ángel Simón Marrero
Keyword(s):  
Travel Time ◽  
National Parks ◽  
Traffic Congestion ◽  
National Park ◽  
Revealed Preference ◽  
Discrete Choice Models ◽  
Model Specification ◽  
Regret Minimization ◽  
Random Regret Minimization

In this study, discrete choice models that combine different behavioural rules are estimated to study the visitors’ preferences in relation to their travel mode choices to access a national park. Using a revealed preference survey conducted on visitors of Teide National Park (Tenerife, Spain), we present a hybrid model specification—with random parameters—in which we assume that some attributes are evaluated by the individuals under conventional random utility maximization (RUM) rules, whereas others are evaluated under random regret minimization (RRM) rules. We then compare the results obtained using exclusively a conventional RUM approach to those obtained using both RUM and RRM approaches, derive monetary valuations of the different components of travel time and calculate direct elasticity measures. Our results provide useful instruments to evaluate policies that promote the use of more sustainable modes of transport in natural sites. Such policies should be considered as priorities in many national parks, where negative transport externalities such as traffic congestion, pollution, noise and accidents are causing problems that jeopardize not only the sustainability of the sites, but also the quality of the visit.

scholarly journals Tolerance and Indifference Bands in Regret–Rejoice Choice Models: Extension to Market Segmentation in the Context of Mode Choice Behavior

2018 ◽  
Vol 2672 (47) ◽  
pp. 23-34 ◽  
Author(s):  
Sunghoon Jang ◽  
Soora Rasouli ◽  
Harry Timmermans
Keyword(s):  
Market Segmentation ◽  
Choice Behavior ◽  
Choice Models ◽  
Random Utility ◽  
Regret Theory ◽  
Regret Minimization ◽  
Trip Purpose ◽  
Utility Models ◽  
Transportation Research ◽  
Random Regret Minimization

Random regret minimization models (RRMs), based on seminal work in regret theory, have been introduced into transportation research as an alternative to expected/random utility models. With ample applications in diverse choice contexts, the RRMs have been extended to include the effect of “rejoice,” the counterpart of the emotion of regret. The fundamental assumption of regret–rejoice models is that when the chosen alternative is inferior to non-chosen alternatives with respect to an attribute, individuals feel regret; otherwise, if the chosen alternative is superior to non-chosen alternatives, individuals rejoice. The regret and rejoice functions are assumed to be continuous in attribute differences. However, individuals may tolerate small attribute differences when judging regret and be indifferent to small differences when assessing rejoice. This paper therefore introduces tolerance and indifference bands in random regret–rejoice choice models, and compares the performance of these models against the performance of the original models. Furthermore, it is assumed that tolerance and indifference bands differ by trip purpose. Empirical results testify to the better performance of the models with the tolerance and indifference bands, and show that trip purpose is an important factor affecting tolerance and indifference bands.

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 Random Regret-Minimization Model for Emergency Resource Preallocation at Freeway Accident Black Spots

2018 ◽  
Vol 2018 ◽  
pp. 1-13
Author(s):  
Siliang Luan ◽  
Qingfang Yang ◽  
Wei Wang ◽  
Zhongtai Jiang ◽  
Ruru Xing ◽  
...  
Keyword(s):  
Large Scale ◽  
Traffic Accidents ◽  
Weather Conditions ◽  
Time Cost ◽  
Frequency Method ◽  
Regret Minimization ◽  
Accident Probability ◽  
Black Spots ◽  
Random Regret Minimization

The preallocation of emergency resources is a mechanism increasing preparedness for uncertain traffic accidents under different weather conditions. This paper introduces the concept of accident probability of black spots and an improved accident frequency method to identify accident black spots and obtain the accident probability. At the same time, we propose a three-stage random regret-minimization (RRM) model to minimize the regret value of the attribute of overall response time, cost, and demand, which allocates limited emergency resources to more likely to happen accident spots. Due to the computational complexity of our model, a genetic algorithm is developed to solve a large-scale instance of the problem. A case study focuses on three-year rainy accidents’ data in Weifang, Linyi, and Rizhao of China to test the correctness and validity of the application of the model.

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