Random Regret Minimization Model Research Articles

Understanding the route choice behavior of metro passenger using the smartphone applications

Understanding the distribution of passenger flow is one of the crucial prerequisites for improving operational efficiency and implementing travel demand management strategies. This paper proposes an improved Random Regret Minimization (i-RRM) model based on the regret theory, where the passenger’s regret on selecting routes is minimized. Considering the growing trend of passengers relying on smartphone apps for route planning and navigation, three factors are investigated, namely, travel time, the number of transfer stations, and the total number of stations traveled. Automatic Fare Collection (AFC) is used to get the proportion of each route, and mobile phone signaling data is used to obtain the ground truth of passengers’ routes proportions for parameter calibration and model validation. The results show that, compared to the ground truth data, the average errors of the proposed i-RRM model, the classic RRM model, and the direct path matching algorithm are 6.03%, 8.96%, and 10.45%, respectively.

The impacts of COVID-19 on route choice with guidance information in urban rail transit of megacities

The outbreak of COVID-19 has caused unprecedented decline of ridership in urban rail transit and changed passenger travel habits, which greatly challenges subway operations. Therefore, it is necessary to better understand and quantify the impact of COVID-19 on passenger travel behavior, specifically route choice. Thus, we collected automatic fare collection data and 2060 random samples through a web-based survey in Beijing on passengers’ route choice behavior during the COVID-19 pandemic. This study utilizes an initial dataset to conduct an analysis and introduces an improved Generalized Random Regret Minimization model (GRRM) aimed at understanding passengers' route choice adjustments in response to COVID-19 guidance information. This improved GRRM accounts for two decision-making criteria, namely, maximum utility and minimum regret, and considers passenger heterogeneity. This marks the first instance of capturing the heterogeneity shift effect in route choice perception during the COVID-19 pandemic. The results show that the improved model has the best fitting result with adjusted Rho square of 0.536, demonstrating that the attributes related to guidance information (i.e., information push/time to receive traffic information/perceived route COVID-19 risk) indeed enhance the model’s fit. Furthermore, the research employs Value of Information Time to quantify the preference of passengers for information in various groups. Compared with the usual scenario, women, young and non-commuter passengers are more likely to receive an early information update to plan their trips in advance. Finally, the perceived risk of COVID-19 on routes is examined in relation to passengers’ personal attributes. It is observed that the elderly and students exhibit heightened sensitivity to the epidemic at all stages, while young passengers and commuters are particularly sensitive only during the small-scale epidemic. These findings offer valuable insights for managers to implement targeted strategies, thereby enhancing passenger flow control and encouraging increased subway ridership.

Comparative Analysis of Discrete Choice Approaches for Modeling Destination Choices of Urban Home-Based Trips to Work

Discrete choice models based on the Random Utility Maximization (RUM) approach are alternatives to traditional aggregated trip distribution models. However, the Random Regret Minimization (RRM) approach has emerged as an alternative to RUM for analyzing behavioral phenomena. This study compares the RUM and RRM approaches to model the destination choice of urban home-based trips to work, with a case study in the city of São Paulo, Brazil. The attributes of distance between traffic analysis zones (TAZs), total number of jobs, and number of jobs per type (industry and services) were considered, and the attributes referring to TAZs in commercial areas were tested in different models. We combined a method to estimate the optimal choice set size and a stratified sampling method to choose alternatives from available choices for a destination-choice problem with many alternatives. The RUM and RRM models produced similar results with respect to the final log-likelihood, [Formula: see text], Akaike information criterion, and Bayesian information criterion for the studied case. However, the RRM models required 52 min to be estimated on average, whereas estimation of the RUM models took only 31 s on average. Models derived from the RUM approach including the total number of jobs and distance produced higher accuracy, requiring less computational time to estimate the parameters.

Driven by utility or regret? Investigating the influence of decision-rule heterogeneity on public preferences for desertification control policies

In the matter of investigating public preferences and stated value for desertification control policies, the choice experiment (CE) could provide valuable insights. However, traditional CE studies modeled the data based on the random utility maximization (RUM) framework, which ignores the significant impact of decision rules on individual choice behavior, such as the random regret minimization (RRM). To examine the influence of decision-rule heterogeneity on public preferences for desertification control scenarios, this study introduced a generalized random regret minimization (G-RRM) model to analyze public choices and developed a hybrid latent class model incorporating the RUM, RRM, and G-RRM choice paradigms to clarify the formation mechanism of decision-rule heterogeneity. The findings indicate that all RRM models demonstrate a better fit with the data than the RUM model, and 68.77% of respondents express significant preference for the RRM choice paradigm. Regret can serve as an important driving factor in public choices for desertification control policies. The hybrid latent class model containing the G-RRM class with different attribute-specific values achieves the best fit, revealing that decision-rule heterogeneity significantly influences individuals' choice behavior, and such heterogeneity is not only among different respondents, but also in their trade-offs across various attributes. The results also reveal the relationship between respondents’ heterogeneous preferences for choice paradigms and their personality traits. Specifically, respondents with less education, residence years, and perceived usefulness of desertification control tend to prefer the RUM choice paradigm; female and respondents with more income, attention, and perceived usefulness of desertification control tend to prefer the RRM choice paradigm; as well as respondents with higher satisfaction to the current desertification situation show a greater decision-rule heterogeneity across different attributes. These results may provide interesting policy consequences, as they offer a precise comprehension of public choices for desertification control policies, through a combination of diverse decision rules.

Stimulus Perception in Long-Distance Railway Mode Choice

In the context of countries in the so-called Global South, where passenger railway services are either nonexistent or poorly performed, discrete choice models are useful to identify the attributes that affect users’ choices and provide insights on their behaviour in regional long-distance trips. Several theories and models have been proposed to understand travel behaviour for effective strategical decision in the transport field. The well-knownRandom Utility Maximization (RUM) approach has been widely used for such purposes, while the Random Regret Minimization (RRM) approach has been recently explored in the literature. However, the magnitude in the difference of levels of the attributes, or the stimulus perception, may affect the results of such models and biases the estimations. Therefore, this paper aims to assess the stimulus perception in mode choice to compare conventional rail (CR) and high-speed rail (HSR) services for passenger transport in intercity trips in Brazil. Estimations of RUM and RRM models were performed with a dataset from a stated preference survey comparing two railway technologies (CR and HSR) with other modes of transport (car, bus, and airplanes) for long-distance trips in the Southeast region of Brazil. Findings provide useful insights about the impacts of travel costs, travel times, and frequency of services, as well as sociodemographic characteristics of users. From the modelling outputs, it was found that users are affected by the magnitude of travel costs, time, and frequency only in business trips by HSR in the Brazilian context.

Modeling the Traveler’s Route Choice Behavior under Unexpected Accidents

To investigate the route choice behavior of travelers under unexpected accidents, this study designs four different scenarios of travelers’ route choice behavior experiments according to the severity of unexpected accidents. The bounded rationality characteristics of travelers, such as the time perception difference coefficient, psychological threshold effect coefficient, and scale effect, are introduced into the generalized random regret minimization (GRRM) model. An improved generalized random regret minimization (IGRRM) model is constructed based on travelers’ route choice behavior under unexpected accidents. The collected data of travelers’ route choice results under four different congestion-level scenarios are analyzed by the IGRRM model. The study finds that with the increase in congestion level, travelers are more willing to change the route; during the commute, travelers tend to choose the route with less travel time and angular cost; in the moderate and serious congestion scenarios, travelers no longer reject the detour route; attribute perception difference, scale effect, and psychological threshold effect; affect travelers’ route choice behavior. The IGRRM model based on the route choice behavior of travelers can more accurately characterize the route choice behavior of travelers under unexpected accidents, provide a basis for traffic flow distribution under unexpected accidents, and benefit traffic management departments to take traffic control and guidance measures to ease the traffic congestion caused by unexpected accidents.

Random Regret Minimization Model of Carpool Travel Choice for Urban Residents Considering Perceived Heterogeneity and Psychological Distance.

Carpooling is a sustainable, economical, and environmentally friendly solution to reduce air pollution and ease traffic congestion in urban areas. However, existing regret theories lack consideration of the heterogeneity of attribute perception in different ways and the psychological factors that affect regret, so they cannot accurately portray urban residents' carpool travel decisions and cannot provide a correct explanation of the actual carpool choice behavior. In this paper, based on the analysis of classical random regret minimization models and random regret minimization models considering heterogeneity, the concept of psychological distance is introduced to address shortcomings of the existing models and construct an improved random regret minimization model considering heterogeneity and psychological distance. The results show that the fit and explanatory effect of the improved model proposed in this paper is better than that of the other two models. The psychological distance of travel residents during the Corona Virus Disease 2019 (COVID-19) affects the anticipated regret value and the willingness to carpool. The model can better describe the carpool travel choice mechanism of travelers and effectively explain the carpool travel choice behavior of travelers.

Random regret minimization for analyzing driver actions, accounting for preference heterogeneity

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.

Hybrid random utility-random regret model in the presence of preference heterogeneity, modeling drivers’ actions

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.

Randregret: A command for fitting random regret minimization models using Stata

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.

Analysis of driver’s choice behavior based on the combined model of utility-regret

In the context of increasing urban traffic trajectory data, based on large-scale urban traffic trajectory data, in the field of intelligent transportation, it has become an urgent need to analyze travelers’ routing behaviors and establish effective routing models to provide travelers with efficient and reasonable driving route recommendations. It is often the case that urban taxi groups has better route choice behavior than other travel group under the cost-benefit constraints. Therefore, providing route recommendations for other travelers according to the route choice habits of urban taxi group is a feasible solution to the above need. This paper starts with the utility maximization model and the stochastic regret minimization model, for the problem that the traveler cannot satisfy the premise of the complete rationality of the utility maximization model when choosing routes and the random regret minimization model ignores the traveler’s sensitivity to the original value of the alternative attribute, and improve the calculation formula of the regret value in the random regret minimization model. A route choice model under the utility-regret combination rule is proposed based on the combination of the utility model and the improved regret model, and then, based on the historical trajectory data of taxis in Xi’an, the RP data required by the model is extracted, and the parameters of the model are estimated and verified. The experimental results show that the route choice probability calculated by the model is closer to the route choice probability in the real scene than the simple utility maximization model or the simple random regret minimization model.

A multi-stage emergency supplies pre-allocation approach for freeway black spots: A Chinese case study.

This study presents a multi-stage random regret minimization (RRM) model as an emergency rescue decision support system to determine the emergency resource pre-allocation schedule for the freeway network. The proposed methodology consists of three steps: (1) improved accident frequency approach to identify the black spots on the freeway network, (2) stochastic programming (SP) model to determine the initial allocation plan sets, and (3) regret-based model in the logarithmical specification to select the most minimal regret one considering the factors of the response time, total cost and demand. The model is applied to the case study of 2014–2016 freeway network in Shandong, China. The results show that the random regret minimization (RRM) model can improve the full-compensation of SP model to a certain degree. RRM in logarithmical specification performs lightly better than random utility maximization (RUM) and RRM in the linear-additive specification in this case. This approach emerges as a valuable tool to help decision makers to allocate resources before traffic accident occurs, with the aim of minimizing the total regret of their decisions.

A Revealed Preference Methodology to Evaluate Regret Minimization with Challenging Choice Sets: A Wildfire Evacuation Case Study

Regret is often experienced for difficult, important, and accountable choices. Consequently, we hypothesize that random regret minimization (RRM) may better describe evacuation behavior than traditional random utility maximization (RUM). However, in many travel related contexts, such as evacuation departure timing, specifying choice sets can be challenging due to unknown attribute levels and near-endless alternatives, for example. This has implications especially for estimating RRM models, which calculates attribute-level regret via pairwise comparison of attributes across all alternatives in the set. While stated preference (SP) surveys solve such choice set problems, revealed preference (RP) surveys collect actual behavior and incorporate situational and personal constraints, which impact rare choice contexts (e.g., evacuations). Consequently, we designed an RP survey for RRM (and RUM) in an evacuation context, which we distributed from March to July 2018 to individuals impacted by the 2017 December Southern California Wildfires (n = 226). While we hypothesized that RRM would outperform RUM for evacuation choices, this hypothesis was not supported by our data. We explain how this is partly the result of insufficient attribute-level variation across alternatives, which leads to difficulties in distinguishing non-linear regret from linear utility. We found weak regret aversion for some attributes, and we identified weak class-specific regret for route and mode choice through a mixed-decision rule latent class choice model, suggesting that RRM for evacuations may yet prove fruitful. We derive methodological implications beyond the present context toward other RP studies involving challenging choice sets and/or limited attribute variability.

Does regret matter in public choices for air quality improvement policies? A comparison of regret-based and utility-based discrete choice modelling

The choice experiment used to elicit public preferences and willingness to pay is increasingly popular in the studies of air quality improvement policies. However, the traditional random utility maximization (RUM) models underlying the choice experiment approach ignores the important influence of anticipated regret on individual choice behaviour. To explore the importance of regret in public preferences for air quality improvement policies, this study introduces random regret minimization (RRM) models in the analysing of public choices, and further compares the performance of utility-based and regret-based discrete choice modelling based on the multinomial logit and hybrid latent class models. The results suggest that the RRM models outperform the RUM model regarding goodness of fit and prediction, indicating that the RRM models can be used as an alternative and supplement to traditional RUM models. Particularly, 55.9% of the choices from respondents are mainly driven by regret. Our results also reveal that respondents driven by regret-based choice paradigm have a stronger preference for increased clean air days, reduced haze days and lower mortality, whereas utility-based respondents prefer to shorten the years of policy delay. These findings are useful for the design of socially optimal policies on the sustainable management of air pollution and providing theoretical reference for the development of environment-related policies.

Regret-based multi-objective route choice models and stochastic user equilibrium: a non-compensatory approach

ABSTRACT Since regret theory has been introduced to model travellers’ route choices, various random regret minimization (RRM) models have been developed for the choice situation of multiple alternatives and multiple attributes. There are two approaches dealing with multi-objective optimisations: one is to combine different attributes into a single additive one and the other is to consider each attribute separately. The existing RRM models adopt the first approach to measure regret. However, travellers might not always trade off attributes in such a compensatory way. In this paper, under the assumption that travellers might consider attributes separately, we develop two new regret-based stochastic user equilibrium (SUE) models by incorporating the RRM model and the hybrid RUM-RRM model into a non-compensatory multi-objective framework. The majority of the second approaches dealing with multiple objectives generally provide a feasible solution set caused by conflicts among objectives. Different from that, the two new models provide probabilistic choice to each route, based on which a single SUE path flow pattern would be attained. Meanwhile, the compromise effect which is widely seen in consumer behaviour can be explained by the two new models. The equivalent variational inequality problems for the proposed models and a path-based algorithm using the method of successive averages have been given. Numerical examples are further conducted to illustrate the properties of the proposed models and the effectiveness of the algorithm.

Error by omitted variables in regret-based choice models: formal and empirical comparison with utility-based models using orthogonal design data

Random regret minimization models have mostly relied on the assumption of identically and independently distributed error terms. Professionally designed stated choice experiments use various design and implementation principles to ensure that any errors meet this assumption. Based on the behavioral theory that individuals arrive at a decision by first processing the attribute values of a choice alternative according to an integration rule to derive the stochastic utility associated with the choice alternatives and then apply the deterministic utility maximizing rule, this assumption can be reasonable met for utility maximizing models. The question is whether this assumption is equally defendable for random regret minimization models in light of the difference in model specification, rooted in fundamental behavioral differences between the underlying decision theories. This study focuses on the effect of omitted variables which is one of main sources of error. Based on a formal analysis and empirical comparison, we argue and show that omitted variables cause correlation between unobserved regrets. Consequently, the independence assumption is difficult to defend because the omitted variables simultaneously affect the comparison of choice alternatives. To capture this effect, we propose an error components structure for regret-based choice models. Empirical results obtained for classic random regret-minimization models for two different data sets support our theoretical arguments. If these results can be generalized to other data sets, it is advisable to adopt an error components structure when estimating these classic random regret-minimization models unless one has reason to believe the effect of the omitted variables is small and therefore can be ignored.

Random Regret Minimization Model for Variable Destination-Oriented Path Planning

How should travelers be guided to change destinations and choose routes when they change their initial destination after being informed that the reception capacity is saturated and that the road ahead is congested? Using quasi-experimental methods, this paper explores this problem from the perspective of regret theory. We propose the regret index to classify the regret level and develop a random regret minimization model for variable destination-oriented path planning. Then, an improved ant colony algorithm based on the destination-path regret value is designed to estimate the model and to recommend alternative destinations and new paths to travelers. Finally, the results show the following: (1) The regret index can measure the regret level of travelers' decision-making, determine the minimum attribute difference tolerate threshold and regret threshold, and has a strong correlation with destination selection behavior. (2) In the case of the uncertain destination and paths, path planning depends not only on the minimum distance between Origin-Destination (OD), but also on destination's regret value. The research results provide reference for designing anticipated regret information to improve travelers' intentions to change destinations, which will reasonably guide travelers to change their decision and rationally arrange their travel plan. On the macro level, traffic volume is guided to different destinations, so as to balance destination reception capacity and reduce traffic jam.

Route Choice Considering Hybrid Regret and Utility Modeling Under Big Data Context

There are two ways to predict and evaluate decision-makers' route choice behavior: random utility maximization (RUM) and random regret minimization (RRM). In this paper, the main purpose is to use the characteristics of regret weight in GRRM to get a hybrid RUM-RRM model. To illustrate the asymmetry of RRM model, this paper uses a route choice case to interpret three main properties of RRM-based model: independence of irrelevant alternatives, semi-compensatory and compromise effect. Then the same scenario is used to interpret why and how the hybrid model can be obtained from the regret weight. What's more, the current empirical studies only used a stated preference survey to test and estimate the model. So GPS-based big data in Guangzhou is used to test the aforementioned models, which can get rid of the weakness of using the stated survey data. The result shows that although the RUM model outperforms most of the RRM models, using regret weight to get the hybrid model, it can also find better model fitness and coefficients consistent with our understanding of attributes. Finally, a value is used to help traffic designers choose a better position of U-turn on the road.

Context-dependent models (CRRM, MuRRM, PRRM, RAM) versus a context-free model (MNL) in transportation studies: a comprehensive comparisons for Swiss and German SP and RP data sets

ABSTRACTThe random regret minimization (RRM) model considers the relative performance of the alternatives and is therefore context-dependent. In RRM, an individual, when choosing between alternatives, is assumed to minimize anticipated regret as opposed to maximize his/her utility. There are three variants of RRM, the classical CRRM, the µRRM, and the P-RRM. There is also a further approach called relative advantage maximization (RAM). We compare multinomial logit with the four mentioned alternatives. We use stated choice data sets which include mode choice, location choice, parking choice, carpooling, car-sharing. We compare the performance of those five models by their model fit, values of travel time savings (VTTS), and elasticities. Looking at the model fit, RAM outperforms the other models in five cases, whereas the PRRM does so in two cases and µRRM only for one case. The VTTS and elasticities vary substantially which is relevant for cost–benefit analysis or simplified modeling approaches.

Influencing Factors That Determine the Usage of the Crowd-Shipping Services

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.