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Journal articles on the topic 'Random Regret Minimization'

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Author: Grafiati

Published: 10 March 2023

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1

Li, Dewei, Yufang Gao, Ruoyi Li, and Weiteng Zhou. "Hybrid Random Regret Minimization and Random Utility Maximization in the Context of Schedule-Based Urban Rail Transit Assignment." Journal of Advanced Transportation 2018 (December 18, 2018): 1–28. http://dx.doi.org/10.1155/2018/9789316.

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Route choice is one of the most critical passenger behaviors in public transit research. The utility maximization theory is generally used to model passengers’ route choice behavior in a public transit network in previous research. However, researchers have found that passenger behavior is far more complicated than a single utility maximization assumption. Some passengers tend to maximize their utility while others would minimize their regrets. In this paper, a schedule-based transit assignment model based on the hybrid of utility maximization and regret minimization is proposed to study the passenger route choice behavior in an urban rail transit network. Firstly, based on the smart card data, the space-time expanded network in an urban rail transit was constructed. Then, it adapts the utility maximization (RUM) and the regret minimization theory (RRM) to analyze and model the passenger route choice behavior independently. The utility values and the regret values are calculated with the utility and the regret functions. A transit assignment model is established based on a hybrid of the random utility maximization and the random regret minimization (RURM) with two kinds of hybrid rules, namely, attribute level hybrid and decision level hybrid. The models are solved by the method of successive algorithm. Finally, the hybrid assignment models are applied to Beijing urban rail transit network for validation. The result shows that RRM and RUM make no significant difference for OD pairs with only two alternative routes. For those with more than two alternative routes, the performance of RRM and RUM is different. RRM is slightly better than RUM in some of the OD pairs, while for the other OD pairs, the results are opposite. Moreover, it shows that the crowd would only influence the regret value of OD pair with more commuters. We conclude that compared with RUM and RRM, the hybrid model RURM is more general.

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2

Chorus, Caspar G. "A Generalized Random Regret Minimization model." Transportation Research Part B: Methodological 68 (October 2014): 224–38. http://dx.doi.org/10.1016/j.trb.2014.06.009.

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3

van Cranenburgh, Sander, Cristian Angelo Guevara, and Caspar G. Chorus. "New insights on random regret minimization models." Transportation Research Part A: Policy and Practice 74 (April 2015): 91–109. http://dx.doi.org/10.1016/j.tra.2015.01.008.

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4

Guevara, C. Angelo, Caspar G. Chorus, and Moshe E. Ben-Akiva. "Sampling of Alternatives in Random Regret Minimization Models." Transportation Science 50, no. 1 (February 2016): 306–21. http://dx.doi.org/10.1287/trsc.2014.0573.

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Chorus, Caspar G., Theo A. Arentze, and Harry J. P. Timmermans. "A Random Regret-Minimization model of travel choice." Transportation Research Part B: Methodological 42, no. 1 (January 2008): 1–18. http://dx.doi.org/10.1016/j.trb.2007.05.004.

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6

Zhao, Lei, Hongzhi Guan, Xinjie Zhang, and Xiongbin Wu. "A regret-based route choice model with asymmetric preference in a stochastic network." Advances in Mechanical Engineering 10, no. 8 (August 2018): 168781401879323. http://dx.doi.org/10.1177/1687814018793238.

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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.

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Li, Mengjie, Fujian Chen, and Qinze Lin. "Random Regret Minimization Model for Variable Destination-Oriented Path Planning." IEEE Access 8 (2020): 163646–59. http://dx.doi.org/10.1109/access.2020.3021524.

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CONTRERAS SERRANO, CARLOS GABRIEL. "Modelos econométricos de elección desde la economía del comportamiento: Modelamiento de elección discreta basada en costo emocional aleatorio - Aplicación a la industria agroquímica Colombiana." Comunicaciones en Estadística 13, no. 2 (November 1, 2020): 33–50. http://dx.doi.org/10.15332/2422474x.6279.

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Los modelos económicos ortodoxos, proponen que el ser humano es racional, egoísta y maximizador para hacer sus elecciones de consumo. Evidencia desde la economía del comportamiento reta estos supuestos planteando nuevos modelos para estudiar la elección humana. Estudiando el proceso de elección de productos de cuidado de cultivo en productores de tomate en Colombia, la presente investigación busco comparar estadística y conceptualmente los modelos RUM (Random Utility Maximization) y RRM (Random Regret Minimization) construidos vía modelamiento de elección discreta concluyendo que los modelos RRM logran mejor bondad de ajuste para describir el comportamiento de elección y compra de nematicidas en muestras de productores de tomate colombianos por lo que constituyen una alternativa viable para diseñar nuevos productos, estimar su participación potencial en el mercado y fijarles precio. Palabras clave: Modelamiento de elección discreta, RUM (Random Utility Maximization), RRM (Random Regret Minimization), Economía del comportamiento, Comportamiento de elección.

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9

Jang, Sunghoon, Soora Rasouli, and Harry Timmermans. "Tolerance and Indifference Bands in Regret–Rejoice Choice Models: Extension to Market Segmentation in the Context of Mode Choice Behavior." Transportation Research Record: Journal of the Transportation Research Board 2672, no. 47 (October 9, 2018): 23–34. http://dx.doi.org/10.1177/0361198118787629.

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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.

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10

Gutiérrez-Vargas, Álvaro A., Michel Meulders, and Martina Vandebroek. "randregret: A command for fitting random regret minimization models using Stata." Stata Journal: Promoting communications on statistics and Stata 21, no. 3 (September 2021): 626–58. http://dx.doi.org/10.1177/1536867x211045538.

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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.

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Gutiérrez-Vargas, Álvaro A., Michel Meulders, and Martina Vandebroek. "randregret: A command for fitting random regret minimization models using Stata." Stata Journal: Promoting communications on statistics and Stata 21, no. 3 (September 2021): 626–58. http://dx.doi.org/10.1177/1536867x211045538.

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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.

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12

Prato, Carlo Giacomo. "Expanding the applicability of random regret minimization for route choice analysis." Transportation 41, no. 2 (July 6, 2013): 351–75. http://dx.doi.org/10.1007/s11116-013-9489-y.

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13

Chorus, Caspar. "Random Regret Minimization: An Overview of Model Properties and Empirical Evidence." Transport Reviews 32, no. 1 (January 2012): 75–92. http://dx.doi.org/10.1080/01441647.2011.609947.

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Chorus, Caspar, Sander van Cranenburgh, and Thijs Dekker. "Random regret minimization for consumer choice modeling: Assessment of empirical evidence." Journal of Business Research 67, no. 11 (November 2014): 2428–36. http://dx.doi.org/10.1016/j.jbusres.2014.02.010.

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15

Sharma, Bibhuti, Mark Hickman, and Neema Nassir. "Park-and-ride lot choice model using random utility maximization and random regret minimization." Transportation 46, no. 1 (July 19, 2017): 217–32. http://dx.doi.org/10.1007/s11116-017-9804-0.

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16

Luan, Siliang, Qingfang Yang, Wei Wang, Zhongtai Jiang, Ruru Xing, and Ruijuan Chu. "Random Regret-Minimization Model for Emergency Resource Preallocation at Freeway Accident Black Spots." Journal of Advanced Transportation 2018 (October 16, 2018): 1–13. http://dx.doi.org/10.1155/2018/3513058.

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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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17

Bekhor, Shlomo, Caspar Chorus, and Tomer Toledo. "Stochastic User Equilibrium for Route Choice Model Based on Random Regret Minimization." Transportation Research Record: Journal of the Transportation Research Board 2284, no. 1 (January 2012): 100–108. http://dx.doi.org/10.3141/2284-12.

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18

van Cranenburgh, Sander, and Carlo G. Prato. "On the robustness of random regret minimization modelling outcomes towards omitted attributes." Journal of Choice Modelling 18 (March 2016): 51–70. http://dx.doi.org/10.1016/j.jocm.2016.04.004.

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19

Hensher, David A., William H. Greene, and Chinh Q. Ho. "Random Regret Minimization and Random Utility Maximization in the Presence of Preference Heterogeneity: An Empirical Contrast." Journal of Transportation Engineering 142, no. 4 (April 2016): 04016009. http://dx.doi.org/10.1061/(asce)te.1943-5436.0000827.

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20

Keya, Nowreen, Sabreena Anowar, and Naveen Eluru. "Freight Mode Choice: A Regret Minimization and Utility Maximization Based Hybrid Model." Transportation Research Record: Journal of the Transportation Research Board 2672, no. 9 (June 30, 2018): 107–19. http://dx.doi.org/10.1177/0361198118782256.

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With the introduction of automated vehicles, the performance of the trucking industry is expected to be improved. In fact, this may impact the entire freight transportation system as trucks possess the highest mode share in freight transportation. To investigate this impact, a hybrid utility–regret-based mode choice model accommodating for shipper level unobserved heterogeneity is proposed in this study. It recognizes that not all attributes influencing shipment mode are evaluated following a homogenous decision rule (solely random utility maximization/solely random regret minimization). The proposed model system is developed using 2012 Commodity Flow Survey data. To demonstrate the applicability of the proposed model system, a detailed policy analysis is conducted considering several futuristic scenarios such as implementation of automation and controlled access of truck traffic to an urban region. The results indicate that introduction of automation in the freight industry would be more beneficial for long-haul hire truck mode than short-haul private truck mode. An increase in truck shipping time due to re-routing of truck traffic away from urban regions causes a modal shift from truck to parcel and “other” mode (rail, water or multiple modes).

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Hensher, David A., William H. Greene, and Caspar G. Chorus. "Random regret minimization or random utility maximization: an exploratory analysis in the context of automobile fuel choice." Journal of Advanced Transportation 47, no. 7 (December 30, 2011): 667–78. http://dx.doi.org/10.1002/atr.188.

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22

Jin, Woo-Jeong, and Jang-Ho Lee. "Application of Random Regret Minimization Model in the Context of Intercity Travel Mode Choice." Journal of the Korean society for railway 19, no. 1 (February 29, 2016): 87–96. http://dx.doi.org/10.7782/jksr.2016.19.1.87.

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23

Surbakti, Medis, and Fransiscus Pinem. "Analysis of The Time Value for Public Transport Passenger by Using Random Regret Minimization." MATEC Web of Conferences 138 (2017): 07005. http://dx.doi.org/10.1051/matecconf/201713807005.

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24

Belgiawan, Prawira F., Anugrah Ilahi, and Kay W. Axhausen. "Influence of pricing on mode choice decision in Jakarta: A random regret minimization model." Case Studies on Transport Policy 7, no. 1 (March 2019): 87–95. http://dx.doi.org/10.1016/j.cstp.2018.12.002.

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25

Thiene, Mara, Marco Boeri, and Caspar G. Chorus. "Random Regret Minimization: Exploration of a New Choice Model for Environmental and Resource Economics." Environmental and Resource Economics 51, no. 3 (September 8, 2011): 413–29. http://dx.doi.org/10.1007/s10640-011-9505-7.

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Virsa Leinia, Audinda, Imam Muthohar, and Ibnu Fauzi. "The application of random regret minimization on commuter's mode choice behaviour: Model-Fit comparisons with Rum-Modelling (case: comparison between Matsuyama and Yogyakarta)." MATEC Web of Conferences 181 (2018): 10005. http://dx.doi.org/10.1051/matecconf/201818110005.

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Modelling the mode choice behaviours of travellers is a key to design effective transport management policies, particularly in shifting travellers to public transport. Abundant studies have analysed the impact of level of services on mode choice preferences through its Random Utility Maximization (RUM), but the possibility of minimalize the regret have been overlooked. This paper will discusses the possibility of using generalised Random Regret Minimization (G-RRM) model on choosing transportation modes. The study is performed in two cities for comparison: Jogjakarta in Indonesia and Matsuyama in Japan. A stated preference (SP) survey isconducted, in which respondents choose Bike or Bus under hypothetical situations. As the result of RUM revealed that travellers prefer the transportation mode with more ensuring level of service. While an empirical proof of concept, the G-RRM model is estimated on a stated mode choice dataset, and its outcomes are compared with RUM and RRM counterparts.

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Kaplan, Sigal, and Carlo Giacomo Prato. "The application of the random regret minimization model to drivers’ choice of crash avoidance maneuvers." Transportation Research Part F: Traffic Psychology and Behaviour 15, no. 6 (November 2012): 699–709. http://dx.doi.org/10.1016/j.trf.2012.06.005.

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Biondi, Beatrice, Ivo A. Van der Lans, Mario Mazzocchi, Arnout R. H. Fischer, Hans C. M. Van Trijp, and Luca Camanzi. "Modelling consumer choice through the random regret minimization model: An application in the food domain." Food Quality and Preference 73 (April 2019): 97–109. http://dx.doi.org/10.1016/j.foodqual.2018.12.008.

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Keya, Nowreen, Sabreena Anowar, and Naveen Eluru. "Joint model of freight mode choice and shipment size: A copula-based random regret minimization framework." Transportation Research Part E: Logistics and Transportation Review 125 (May 2019): 97–115. http://dx.doi.org/10.1016/j.tre.2019.03.007.

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30

Mai, Tien, Fabian Bastin, and Emma Frejinger. "On the similarities between random regret minimization and mother logit: The case of recursive route choice models." Journal of Choice Modelling 23 (June 2017): 21–33. http://dx.doi.org/10.1016/j.jocm.2017.03.002.

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31

Wang, Xingchuan, Enjian Yao, and Shasha Liu. "Travel Choice Analysis under Metro Emergency Context: Utility? Regret? Or Both?" Sustainability 10, no. 11 (October 24, 2018): 3852. http://dx.doi.org/10.3390/su10113852.

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With the continuous expansion of the network scale and increasing of passengers, metro emergencies such as operational equipment failure are happening more frequently. Due to the narrow space and crowds of people, metro emergencies always have more of an impact than road traffic emergencies. In order to adopt appropriate measures to ensure passenger safety and avoid risks, we need to get a better understanding of passengers’ travel choice behaviors under emergencies. Most of the existing research studies related to travel choice behaviors took the random utility maximization (RUM) principle for granted, but failed to realize the potential of different decision-making processes and changes to the decision-making environment. In this research, we aim to analyze metro passengers’ travel choice behaviors under metro network emergency contexts. Based on the data collected from a survey about travel choices under metro emergencies in the Guangzhou Metro, we compared the performances of models that follow the RUM and random regret minimization (RRM) principles, and established a hybrid RUM-RRM model as well as a nested logit model following RRM (NL-RRM) to estimate the effects of various factors on passengers’ travel choice behaviors. Comparisons illustrate that the hybrid model and NL-RRM model can improve model fit, and the combination of RUM and RRM outperforms either of them respectively.

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González, Rosa Marina, Concepción Román, and Ángel Simón Marrero. "Values of Travel Time for Recreational Trips under Different Behavioural Rules." Sustainability 13, no. 12 (June 17, 2021): 6831. http://dx.doi.org/10.3390/su13126831.

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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.

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Chaugule, S., J. W. Hay, and G. Young. "Exploring Heterogeneity In Attribute Processing Strategies: Use Of Hybrid Random Utility Maximization-Random Regret Minimization (Rum-Rrm) Models In A Discrete Choice Experiment (Dce)." Value in Health 18, no. 3 (May 2015): A34. http://dx.doi.org/10.1016/j.jval.2015.03.205.

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Zhu, Dianchen, N. N. Sze, and Zhongxiang Feng. "The trade-off between safety and time in the red light running behaviors of pedestrians: A random regret minimization approach." Accident Analysis & Prevention 158 (August 2021): 106214. http://dx.doi.org/10.1016/j.aap.2021.106214.

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Chaugule, S., J. W. Hay, G. Young, O. A. Martin, and E. F. Drabo. "Does differential framing of opt-out alternatives in discrete choice experiments (dces) matter? Comparison of random utility maximization (rum) and random regret minimization (rrm) models." Value in Health 18, no. 3 (May 2015): A24—A25. http://dx.doi.org/10.1016/j.jval.2015.03.151.

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Le, Tho V., and Satish V. Ukkusuri. "Influencing Factors That Determine the Usage of the Crowd-Shipping Services." Transportation Research Record: Journal of the Transportation Research Board 2673, no. 7 (May 8, 2019): 550–66. http://dx.doi.org/10.1177/0361198119843098.

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The objective of this study is to understand how senders choose shipping services for different products, given the availability of both emerging crowd-shipping ( CS) and traditional carriers in a logistics market. Using data collected from a United States (U.S.) survey, Random Utility Maximization (RUM) and Random Regret Minimization (RRM) models have been employed to reveal factors that influence the diversity of decisions made by senders. Shipping costs, along with additional real-time services such as courier reputations, tracking info, e-notifications, and customized delivery time and location, have been found to have remarkable impacts on senders’ choices. Interestingly, potential senders were willing to pay more to ship grocery items such as food, beverages, and medicines by CS services. Moreover, the real-time services have low elasticities, meaning that only a slight change in those services will lead to a change in sender behavior. Finally, data-science techniques were used to assess the performance of the RUM and RRM models and found to have similar accuracies. The findings from this research will help logistics firms address potential market segments, prepare service configurations to fulfill senders’ expectations, and develop effective business operations strategies.

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Koemle, Dieter, and Xiaohua Yu. "Choice experiments in non-market value analysis: some methodological issues." Forestry Economics Review 2, no. 1 (April 20, 2020): 3–31. http://dx.doi.org/10.1108/fer-04-2020-0005.

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PurposeThis paper reviews the current literature on theoretical and methodological issues in discrete choice experiments, which have been widely used in non-market value analysis, such as elicitation of residents' attitudes toward recreation or biodiversity conservation of forests.Design/methodology/approachWe review the literature, and attribute the possible biases in choice experiments to theoretical and empirical aspects. Particularly, we introduce regret minimization as an alternative to random utility theory and sheds light on incentive compatibility, status quo, attributes non-attendance, cognitive load, experimental design, survey methods, estimation strategies and other issues.FindingsThe practitioners should pay attention to many issues when carrying out choice experiments in order to avoid possible biases. Many alternatives in theoretical foundations, experimental designs, estimation strategies and even explanations should be taken into account in practice in order to obtain robust results.Originality/valueThe paper summarizes the recent developments in methodological and empirical issues of choice experiments and points out the pitfalls and future directions both theoretically and empirically.

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Zhao, Dan, Fengchun Han, Meng Meng, Jun Ma, and Quantao Yang. "Exploring the influence of traffic enforcement on speeding behavior on low-speed limit roads." Advances in Mechanical Engineering 11, no. 12 (December 2019): 168781401989157. http://dx.doi.org/10.1177/1687814019891572.

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Speeding on low-speed limit roads is a common traffic offense in China, which could be due to the mild traffic safety enforcement. The article aims to explicit the impact of traffic enforcement measures on the speeding behavior on low-speed limit roads. First, field data were collected to demonstrate the severity of speeding by investigating speed distribution; second, a virtual traffic enforcement was designed by considering three factors related to traffic enforcement, and a stated preference survey questionnaire including six scenarios was designed and implemented; finally, a series of generalized regret random minimization models were established to study the relationship of speeding behavior and traffic enforcement as well as drivers’ personal characteristics. From the stated preference survey analysis, the research figures out that other vehicles’ average speed is the most important reference to choose speed rather than traffic penalties, and the model estimation results show that speeding violation grows severe if traffic enforcements are lenient. Therefore, increasing the violation costs is a powerful means of lowering the probability of speeding for individual, thus proceeding the drop of vehicles’ average speed, and the fall of average speed will contribute to decrease speeding subsequently.

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Wang, Yao, and Liu. "Simulation of Metro Congestion Propagation Based on Route Choice Behaviors Under Emergency-Caused Delays." Applied Sciences 9, no. 20 (October 9, 2019): 4210. http://dx.doi.org/10.3390/app9204210.

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Generally, metro emergencies could lead to delays and seriously affect passengers’ trips. The dynamic congestion propagation process under metro emergency-caused delays could be regarded as the aggregation of passengers’ individual travel choices. This paper aims to simulate the congestion propagation process without intervention measures under the metro emergency-caused delays, which is integrated with passengers’ route choice behaviors. First, using a stated preference survey data collected from Guangzhou Metro (GZM) passengers, route choice models are developed based on random regret minimization (RRM) theory under metro emergency conditions. Then, a simulation environment is established using graph cellular automata (graph-CA) with augmented GZM network structure, where an ASEIR (advanced susceptible-exposed-infectious-recovered) model with time delay is proposed as the evolution rule in graph-CA. Furthermore, considering passengers’ routing preferences, a quantified method for the congestion propagation rate is proposed, and the congestion propagation process on a subnetwork of the GZM network is simulated. The simulation results show that metro congestion during peak periods has a secondary increase after the end of the emergency-caused delays, while the congestion during nonpeak hours has a shorter duration and a smaller influence range. The proposed simulation model could clearly reflect the dynamic process of congestion propagation under metro emergencies.

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40

Chorus, Caspar. "A Generalized Random Regret Minimization Model." SSRN Electronic Journal, 2013. http://dx.doi.org/10.2139/ssrn.2358415.

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41

Rezapour, Mahdi, and Khaled Ksaibati. "Random regret minimization for analyzing driver actions, accounting for preference heterogeneity." Frontiers in Built Environment 8 (September 27, 2022). http://dx.doi.org/10.3389/fbuil.2022.1000289.

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Increasingly more studies have implemented random regret minimization (RRM) as an alternative to random utility maximization (RUM) for modeling travelers’ choice-making behaviors. While for RUM, the focus is on utility maximization, for RRM the emphasis is on the regret of not selecting the best alternative. This study presented RRM and RUM for modeling actions made by drivers that resulted in crashes. The RRM method was considered in this study as the actions made before crashes might be the resultants of avoidance of regrets across the alternatives rather than the maximization of the utility related to the considered attributes. In addition, we extended the considered models to account for the unobserved heterogeneity in the datasets. Finally, we gave more flexibility to our model by changing the means of random parameters based on some observed attributes. This is one of the earliest studies, which considered the technique in the context of traffic safety for modeling drivers’ action while accounting for heterogeneity in the dataset by means of the random parameter. In addition, we considered the impact of inclusion of various predictors in the model fit of RRM and RUM. The results showed that while the standard RUM model outperforms the RRM model, the standard mixed models and the mixed models accounting for observed heterogeneity outperform the other techniques. As expected from the methodological structure of RRM, we found that the RRM performance is very sensitive to the included attributes. For instance, we found that by excluding the attributes of drivers’ condition and drivers under influence (DUI), the RRM model significantly outperforms the RUM model. The impact might be linked to the fact that when drivers are under abnormal conditions or influenced by drugs or alcohol, based on the sum of pairwise regret comparison, the inclusion of those attributes deteriorates the goodness-of-fit of the RRM model. It is possible that those parameters do not make a difference on regret pairwise comparison related to alternatives. The discussions at the end of this article examined possible reasons behind this performance.

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Rezapour, Mahdi, and Khaled Ksaibati. "Hybrid random utility-random regret model in the presence of preference heterogeneity, modeling drivers’ actions." Frontiers in Built Environment 8 (August 8, 2022). http://dx.doi.org/10.3389/fbuil.2022.972253.

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Despite the importance of drivers’ actions and behaviors, the underlying factors to those actions have not received adequate attention. Understanding the factors contributing to various drivers’ actions before crashes could help policy makers to take appropriate actions to tackle those behaviors before crashes occur. One of the first steps could be to identify contributing factors to drivers’ actions by using a reliable statistical technique. It is reasonable to assume that drivers vary in their decision-making processes. Thus, in this study, in addition to the random utility maximization (RUM), the random regret minimization (RRM), as a psychological representation of the choice-making process, was considered. While most of the past studies, in the context of traffic safety, focused on either the RRM or RUM, both models’ frameworks as hybrid models might be needed to account for the heterogeneity of drivers’ decision-making behaviors. In addition, we accounted for the additional dimensions of preference heterogeneity in the latent class (LC) that the model might not capture. The results showed a significant improvement in the model fit of the mixed hybrid LC model compared with the standard hybrid and simple mixed RRM and RUM models. The emotional conditions of drivers, distraction, environmental conditions, and gender are some of the factors found to impact drivers’ choices. The results suggest that while the majority of attributes are processed according to the RUM, a significant portion of attributes are processed by the RRM. The hybrid model provides a richer understanding regarding factors to drivers’ actions before crashes based on different paradigms.

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"Travel Mode and Travel Route Choice Behavior Based on Random Regret Minimization: A Systematic Review." Sustainability 10, no. 4 (April 14, 2018): 1185. http://dx.doi.org/10.3390/su10041185.

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Xiao, Qiang, Ruichun He, and Ziyi Wang. "Random Regret Minimization Model of Carpool Travel Choice for Urban Residents Considering Perceived Heterogeneity and Psychological Distance." Journal of Shanghai Jiaotong University (Science), March 2, 2023. http://dx.doi.org/10.1007/s12204-023-2588-9.

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Oren, Joel, and Brendan Lucier. "Online (Budgeted) Social Choice." Proceedings of the AAAI Conference on Artificial Intelligence 28, no. 1 (June 21, 2014). http://dx.doi.org/10.1609/aaai.v28i1.8891.

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We consider a classic social choice problem in an online setting. In each round, a decision maker observes a single agent's preferences overa set of $m$ candidates, and must choose whether to irrevocably add a candidate to a selection set of limited cardinality $k$. Each agent's (positional) score depends on the candidates in the set when he arrives, and the decision-maker's goal is to maximize average (over all agents) score. We prove that no algorithm (even randomized) can achieve an approximationfactor better than $O(\frac{\log\log m}{\log m})$. In contrast, if the agents arrive in random order, we present a $(1 - \frac{1}{e} - o(1))$-approximatealgorithm, matching a lower bound for the off-line problem.We show that improved performance is possible for natural input distributionsor scoring rules. Finally, if the algorithm is permitted to revoke decisions at a fixedcost, we apply regret-minimization techniques to achieve approximation $1 - \frac{1}{e} - o(1)$ even for arbitrary inputs.

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