Pub Date : 2025-12-01Epub Date: 2025-10-04DOI: 10.1016/j.jocm.2025.100580
Kun Huang, Xin Ye, Mengyi Wang
Estimating a valid correlation matrix with structural restrictions presents significant challenges, particularly in ensuring positive definiteness and enforcing zero-correlation constraints. Traditional approaches, such as the Cholesky decomposition, often suffer from numerical instability and convergence failures in these settings. This paper introduces a novel Cholesky-based parameterization that effectively addresses these issues by allowing zero constraints while maintaining positive definiteness and unit diagonal elements. Through extensive Monte Carlo simulations, we demonstrate that the proposed method outperforms the existing spherical parameterization approach, achieving superior convergence rates, enhanced estimation accuracy, and robustness under high-correlation scenarios. An empirical application on non-commuters’ activity participation in Shanghai further validates the practical effectiveness of the proposed method, showcasing its ability to capture complex behavioral relationships while ensuring stable estimation. The results suggest that the proposed parameterization provides a reliable and computationally efficient alternative for correlation matrix estimation in multivariate models.
{"title":"A special Cholesky-based parameterization for estimation of restricted correlation matrices","authors":"Kun Huang, Xin Ye, Mengyi Wang","doi":"10.1016/j.jocm.2025.100580","DOIUrl":"10.1016/j.jocm.2025.100580","url":null,"abstract":"<div><div>Estimating a valid correlation matrix with structural restrictions presents significant challenges, particularly in ensuring positive definiteness and enforcing zero-correlation constraints. Traditional approaches, such as the Cholesky decomposition, often suffer from numerical instability and convergence failures in these settings. This paper introduces a novel Cholesky-based parameterization that effectively addresses these issues by allowing zero constraints while maintaining positive definiteness and unit diagonal elements. Through extensive Monte Carlo simulations, we demonstrate that the proposed method outperforms the existing spherical parameterization approach, achieving superior convergence rates, enhanced estimation accuracy, and robustness under high-correlation scenarios. An empirical application on non-commuters’ activity participation in Shanghai further validates the practical effectiveness of the proposed method, showcasing its ability to capture complex behavioral relationships while ensuring stable estimation. The results suggest that the proposed parameterization provides a reliable and computationally efficient alternative for correlation matrix estimation in multivariate models.</div></div>","PeriodicalId":46863,"journal":{"name":"Journal of Choice Modelling","volume":"57 ","pages":"Article 100580"},"PeriodicalIF":2.4,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145267465","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-01Epub Date: 2025-09-23DOI: 10.1016/j.jocm.2025.100575
Wilbur Townsend
This paper presents an algorithm for drawing nested extreme value random variables — i.e., the variable used in the latent variable formulation of the nested logit model. Runtime is linear in both the number of alternatives and the number of nests. An R package, nev, implements the algorithm.
{"title":"A novel algorithm for drawing nested extreme value random variables","authors":"Wilbur Townsend","doi":"10.1016/j.jocm.2025.100575","DOIUrl":"10.1016/j.jocm.2025.100575","url":null,"abstract":"<div><div>This paper presents an algorithm for drawing nested extreme value random variables — <em>i.e.</em>, the variable used in the latent variable formulation of the nested logit model. Runtime is linear in both the number of alternatives and the number of nests. An <em>R</em> package, <span>nev</span>, implements the algorithm.</div></div>","PeriodicalId":46863,"journal":{"name":"Journal of Choice Modelling","volume":"57 ","pages":"Article 100575"},"PeriodicalIF":2.4,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145119295","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-01Epub Date: 2025-11-30DOI: 10.1016/j.jocm.2025.100583
Niousha Bagheri , Milad Ghasri , Michael Barlow
This paper presents RUM-NN, a neural network framework that is fully consistent with the Random Utility Maximisation (RUM) theory and designed to flexibly model discrete choice behaviour under a wide range of error distributions. RUM-NN contributes a flexible estimation approach to accommodate arbitrary error distributions. This enables the modelling of choice probabilities even when closed-form solutions are unavailable, accommodating arbitrary error structures, including correlated and non-conventional distributions. The proposed RUM-NN is introduced in both linear and non-linear structures. The linear version of RUM-NN retains interpretability similar to traditional econometric models, while the nonlinear extension enhances predictive flexibility by capturing complex relationships in the utility function. The performance of RUM-NN in parameter recovery and prediction accuracy is rigorously evaluated using synthetic datasets through Monte Carlo experiments. Additionally, RUM-NN is evaluated on the Swissmetro and the London Passenger Mode Choice (LPMC) datasets with different sets of distribution assumptions for the error component. The results demonstrate that RUM-NN under linear utility structure and IID Gumbel error terms can replicate the performance of Multinomial Logit model, but relaxing those constraints yields to superior performance for both Swissmetro and LMPC datasets. By introducing a novel estimation approach aligned with statistical theories, this study empowers econometricians to harness the advantages of neural network models. To facilitate the implementation of RUM-NN, a Python library has been developed and made publicly available.
{"title":"A neural estimation framework for discrete choice models with arbitrary error distributions","authors":"Niousha Bagheri , Milad Ghasri , Michael Barlow","doi":"10.1016/j.jocm.2025.100583","DOIUrl":"10.1016/j.jocm.2025.100583","url":null,"abstract":"<div><div>This paper presents RUM-NN, a neural network framework that is fully consistent with the Random Utility Maximisation (RUM) theory and designed to flexibly model discrete choice behaviour under a wide range of error distributions. RUM-NN contributes a flexible estimation approach to accommodate arbitrary error distributions. This enables the modelling of choice probabilities even when closed-form solutions are unavailable, accommodating arbitrary error structures, including correlated and non-conventional distributions. The proposed RUM-NN is introduced in both linear and non-linear structures. The linear version of RUM-NN retains interpretability similar to traditional econometric models, while the nonlinear extension enhances predictive flexibility by capturing complex relationships in the utility function. The performance of RUM-NN in parameter recovery and prediction accuracy is rigorously evaluated using synthetic datasets through Monte Carlo experiments. Additionally, RUM-NN is evaluated on the Swissmetro and the London Passenger Mode Choice (LPMC) datasets with different sets of distribution assumptions for the error component. The results demonstrate that RUM-NN under linear utility structure and IID Gumbel error terms can replicate the performance of Multinomial Logit model, but relaxing those constraints yields to superior performance for both Swissmetro and LMPC datasets. By introducing a novel estimation approach aligned with statistical theories, this study empowers econometricians to harness the advantages of neural network models. To facilitate the implementation of RUM-NN, a Python library has been developed and made publicly available.</div></div>","PeriodicalId":46863,"journal":{"name":"Journal of Choice Modelling","volume":"57 ","pages":"Article 100583"},"PeriodicalIF":2.4,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145684484","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-01Epub Date: 2025-10-21DOI: 10.1016/j.jocm.2025.100581
Ambuj Sriwastava, Peter Reichert
The elicitation and quantification of preferences of individuals or aggregated preferences of stakeholders or samples of the population are crucial for decision support. This can be done by statistically evaluating the results of discrete choice inquiries using a parameterized value function. When doing this with Bayesian inference, the specification of a prior can be challenging as it may be difficult to find similar cases to transfer knowledge. This makes it particularly important to be informed about the sensitivity of the results to the choice of the prior. This can be done by computing posteriors for different plausible priors and analyzing differences between them. This is infeasible for a large number of priors. This paper proposes the application of Density Ratio Classes, which sandwich non-normalized prior densities between specified lower and upper functional bounds. In this study, differences among posteriors resulting from priors in such a class are analyzed by comparing marginal posterior credible intervals. We compute “outer” credible intervals that range from the minimum of all lower bounds to the maximum of all upper bounds of marginal posterior credible intervals with the same quantile bounds resulting from the priors in the density ratio class. The methodology is easy to implement and only requires one Markov chain of the posterior resulting from the upper function. We provide an R package “DRclass” that supports such implementations. Theoretical considerations and three case studies provide illustration and guidance about constructing the prior density ratio class.
{"title":"Sensitivity analysis of Bayesian estimates of value function parameters to priors using imprecise probabilities","authors":"Ambuj Sriwastava, Peter Reichert","doi":"10.1016/j.jocm.2025.100581","DOIUrl":"10.1016/j.jocm.2025.100581","url":null,"abstract":"<div><div>The elicitation and quantification of preferences of individuals or aggregated preferences of stakeholders or samples of the population are crucial for decision support. This can be done by statistically evaluating the results of discrete choice inquiries using a parameterized value function. When doing this with Bayesian inference, the specification of a prior can be challenging as it may be difficult to find similar cases to transfer knowledge. This makes it particularly important to be informed about the sensitivity of the results to the choice of the prior. This can be done by computing posteriors for different plausible priors and analyzing differences between them. This is infeasible for a large number of priors. This paper proposes the application of Density Ratio Classes, which sandwich non-normalized prior densities between specified lower and upper functional bounds. In this study, differences among posteriors resulting from priors in such a class are analyzed by comparing marginal posterior credible intervals. We compute “outer” credible intervals that range from the minimum of all lower bounds to the maximum of all upper bounds of marginal posterior credible intervals with the same quantile bounds resulting from the priors in the density ratio class. The methodology is easy to implement and only requires one Markov chain of the posterior resulting from the upper function. We provide an R package “DRclass” that supports such implementations. Theoretical considerations and three case studies provide illustration and guidance about constructing the prior density ratio class.</div></div>","PeriodicalId":46863,"journal":{"name":"Journal of Choice Modelling","volume":"57 ","pages":"Article 100581"},"PeriodicalIF":2.4,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145362556","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-01Epub Date: 2025-10-03DOI: 10.1016/j.jocm.2025.100579
Kyungah Kim , Jongsu Lee , Junghun Kim
Setting an appropriate reference point is crucial in reference-dependent choice modeling, as it directly influences the reliability of utility estimates and the interpretation of consumer decision-making. However, many prior studies have relied on generalized or fixed reference points—such as status quo or past experiences—without accounting for individual-level heterogeneity. To address this limitation, this study proposes a reference-dependent artificial neural network (RD-ANN) that integrates the structure of reference-dependent choice models into a neural network framework. RD-ANN is designed to learn individual- and alternative-specific reference points based on consumer and alternative attributes, thereby providing a flexible and data-driven approach to reference point estimation. Empirical validation using smartphone and automobile choice data shows that RD-ANN outperforms benchmark models in various predictive performance metrics including accuracy, recall, precision, and F1 score. The model also captures behavioral patterns such as brand loyalty and status quo bias more effectively. In the empirical contexts considered, RD-ANN was found to better reflect consumer heterogeneity and may help provide more accurate estimates of price sensitivity compared to models using a fixed status quo reference point. These findings suggest that the proposed approach offers a promising direction for integrating behavioral theory and machine learning in discrete choice modeling.
{"title":"Beyond the status quo: Leveraging reference-dependent theory in a neural network for consumer choice analysis","authors":"Kyungah Kim , Jongsu Lee , Junghun Kim","doi":"10.1016/j.jocm.2025.100579","DOIUrl":"10.1016/j.jocm.2025.100579","url":null,"abstract":"<div><div>Setting an appropriate reference point is crucial in reference-dependent choice modeling, as it directly influences the reliability of utility estimates and the interpretation of consumer decision-making. However, many prior studies have relied on generalized or fixed reference points—such as status quo or past experiences—without accounting for individual-level heterogeneity. To address this limitation, this study proposes a reference-dependent artificial neural network (RD-ANN) that integrates the structure of reference-dependent choice models into a neural network framework. RD-ANN is designed to learn individual- and alternative-specific reference points based on consumer and alternative attributes, thereby providing a flexible and data-driven approach to reference point estimation. Empirical validation using smartphone and automobile choice data shows that RD-ANN outperforms benchmark models in various predictive performance metrics including accuracy, recall, precision, and F1 score. The model also captures behavioral patterns such as brand loyalty and status quo bias more effectively. In the empirical contexts considered, RD-ANN was found to better reflect consumer heterogeneity and may help provide more accurate estimates of price sensitivity compared to models using a fixed status quo reference point. These findings suggest that the proposed approach offers a promising direction for integrating behavioral theory and machine learning in discrete choice modeling.</div></div>","PeriodicalId":46863,"journal":{"name":"Journal of Choice Modelling","volume":"57 ","pages":"Article 100579"},"PeriodicalIF":2.4,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145221365","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-01Epub Date: 2025-10-18DOI: 10.1016/j.jocm.2025.100578
Cheng-Yu Hung , Peter Kurz , Roger A. Bailey , Joel Huber , Greg M. Allenby
The validity of using conjoint analysis to conduct an economic evaluation of product characteristics rests on the inclusion of brand names, prices, and an outside “no-choice” option in the choice task. The no-choice option is included in case respondents determine that some other offering, not included in the conjoint choice task, is preferred to those that are included and that it would be better to hold onto their money and not make a purchase at that time. Selecting the no-choice option assumes that respondents have some level of knowledge of the value and prices of goods in the market. In this paper, we show that survey respondents may lack this information and make inferences about market prices from the conjoint exercise itself. This learning effect is especially problematic for new products for which a set of reference prices do not yet exist, but can also be problematic in established markets that are familiar. We discuss results from two sets of conjoint experiments, one in a new product category conducted in three countries in Europe, and another in an established category in the United States involving three experimental conditions that inform respondents about products and prices available in the marketplace. We find that the lack of knowledge of competitive offerings and prices affects estimates of brand values but not the value of other product features. In addition, we discuss aspects of how a well-designed conjoint study mitigates the effects of this type of learning in conjoint analysis.
{"title":"Re-examining the no-choice option in conjoint analysis","authors":"Cheng-Yu Hung , Peter Kurz , Roger A. Bailey , Joel Huber , Greg M. Allenby","doi":"10.1016/j.jocm.2025.100578","DOIUrl":"10.1016/j.jocm.2025.100578","url":null,"abstract":"<div><div>The validity of using conjoint analysis to conduct an economic evaluation of product characteristics rests on the inclusion of brand names, prices, and an outside “no-choice” option in the choice task. The no-choice option is included in case respondents determine that some other offering, not included in the conjoint choice task, is preferred to those that are included and that it would be better to hold onto their money and not make a purchase at that time. Selecting the no-choice option assumes that respondents have some level of knowledge of the value and prices of goods in the market. In this paper, we show that survey respondents may lack this information and make inferences about market prices from the conjoint exercise itself. This learning effect is especially problematic for new products for which a set of reference prices do not yet exist, but can also be problematic in established markets that are familiar. We discuss results from two sets of conjoint experiments, one in a new product category conducted in three countries in Europe, and another in an established category in the United States involving three experimental conditions that inform respondents about products and prices available in the marketplace. We find that the lack of knowledge of competitive offerings and prices affects estimates of brand values but not the value of other product features. In addition, we discuss aspects of how a well-designed conjoint study mitigates the effects of this type of learning in conjoint analysis.</div></div>","PeriodicalId":46863,"journal":{"name":"Journal of Choice Modelling","volume":"57 ","pages":"Article 100578"},"PeriodicalIF":2.4,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145321469","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-09-01Epub Date: 2025-08-14DOI: 10.1016/j.jocm.2025.100564
Yu Shuang Gan, Neal Stuart Hinvest
The use of carbon labels as an intervention to increase more sustainable food consumption has seen many mixed results, with some studies showing that consumers do not utilise the carbon labels in their decisions. To address the mixed results in the literature, we present a novel and in-depth evaluation of how carbon labels work by quantifying the importance of carbon label information relative to taste preferences in food decisions via a computational modelling approach. Participants (n = 48) were presented with multiple trials of two sandwiches alongside their carbon labels. Participants' choice and response time were recorded whilst visual attention was tracked with an eye-tracking device. The Multi-attribute Attentional Drift Diffusion Model (maaDDM) was fitted to data through Bayesian STAN modelling in R. The analysis revealed that carbon labels were used to a moderate extent similar to individual taste preference in choosing sandwiches, but the extent of use varied as a function of participant's perception of the negative impact of GHG emissions (the more negative perception, the greater use of carbon labels). We further explore the insights gained from maaDDM on participant's information sampling behaviour, and discuss the implications for policies to identify a critical valuation threshold of carbon labels.
{"title":"Quantifying the value of carbon label information in food choice using drift diffusion modelling","authors":"Yu Shuang Gan, Neal Stuart Hinvest","doi":"10.1016/j.jocm.2025.100564","DOIUrl":"10.1016/j.jocm.2025.100564","url":null,"abstract":"<div><div>The use of carbon labels as an intervention to increase more sustainable food consumption has seen many mixed results, with some studies showing that consumers do not utilise the carbon labels in their decisions. To address the mixed results in the literature, we present a novel and in-depth evaluation of how carbon labels work by quantifying the importance of carbon label information relative to taste preferences in food decisions via a computational modelling approach. Participants (<em>n</em> = 48) were presented with multiple trials of two sandwiches alongside their carbon labels. Participants' choice and response time were recorded whilst visual attention was tracked with an eye-tracking device. The Multi-attribute Attentional Drift Diffusion Model (maaDDM) was fitted to data through Bayesian STAN modelling in R. The analysis revealed that carbon labels were used to a moderate extent similar to individual taste preference in choosing sandwiches, but the extent of use varied as a function of participant's perception of the negative impact of GHG emissions (the more negative perception, the greater use of carbon labels). We further explore the insights gained from maaDDM on participant's information sampling behaviour, and discuss the implications for policies to identify a critical valuation threshold of carbon labels.</div></div>","PeriodicalId":46863,"journal":{"name":"Journal of Choice Modelling","volume":"56 ","pages":"Article 100564"},"PeriodicalIF":2.4,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144829766","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-09-01Epub Date: 2025-06-13DOI: 10.1016/j.jocm.2025.100561
Vilma Xhakollari , Daniele Asioli , Rodolfo M. Nayga
Cheap Talk is one of the most popular techniques used to mitigate hypothetical bias in choice experiments, but there is uncertainty about how it is used by researchers, and its effectiveness. We reviewed and explored in-depth how cheap talk is used and how effective it is in mitigating hypothetical bias by examining 172 articles in the literature using a systematic review. The results show that cheap talk is largely used in choice experiment studies, but only a minority of articles make the cheap talk scripts available to the readers. Furthermore, we found that there is a large heterogeneity on how the cheap talk script is used by researchers in terms of length, words used, structure, and its effectiveness. This review provides useful insights about the implementation of cheap talk in choice experiments as well as outline several future research avenues that could be useful in improving the validity and reliability of data collected using hypothetical choice experiments.
{"title":"Mitigating hypothetical bias in choice Experiments: An in-depth review on the use of cheap talk","authors":"Vilma Xhakollari , Daniele Asioli , Rodolfo M. Nayga","doi":"10.1016/j.jocm.2025.100561","DOIUrl":"10.1016/j.jocm.2025.100561","url":null,"abstract":"<div><div>Cheap Talk is one of the most popular techniques used to mitigate hypothetical bias in choice experiments, but there is uncertainty about how it is used by researchers, and its effectiveness. We reviewed and explored in-depth how cheap talk is used and how effective it is in mitigating hypothetical bias by examining 172 articles in the literature using a systematic review. The results show that cheap talk is largely used in choice experiment studies, but only a minority of articles make the cheap talk scripts available to the readers. Furthermore, we found that there is a large heterogeneity on how the cheap talk script is used by researchers in terms of length, words used, structure, and its effectiveness. This review provides useful insights about the implementation of cheap talk in choice experiments as well as outline several future research avenues that could be useful in improving the validity and reliability of data collected using hypothetical choice experiments.</div></div>","PeriodicalId":46863,"journal":{"name":"Journal of Choice Modelling","volume":"56 ","pages":"Article 100561"},"PeriodicalIF":2.8,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144272076","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This study introduces a tri-reference-point framework to analyze air-rail passengers' airport ground access behaviour, using advisory, earliest, and mandatory latest airport arrival times as key reference points. Leveraging revealed preference data from Dalian Airport in China, this model examines how deviations from instructive arrival timings, rather than total ground access time, influence passenger choices among available high-speed rail (HSR) options. Compared to the traditional multinomial logit (MNL) model, the proposed approach better captures these behaviours, showing that passengers prioritize timing relative to advisory intervals. This framework also provides insights into evaluating the suitability of HSR options for air-rail integrated services.
{"title":"Tri-reference-point framework for analyzing air-rail passenger airport access behaviour","authors":"Wenqian Zou , Yiming Zheng , Shengguo Gao , Yonglei Jiang","doi":"10.1016/j.jocm.2025.100565","DOIUrl":"10.1016/j.jocm.2025.100565","url":null,"abstract":"<div><div>This study introduces a tri-reference-point framework to analyze air-rail passengers' airport ground access behaviour, using advisory, earliest, and mandatory latest airport arrival times as key reference points. Leveraging revealed preference data from Dalian Airport in China, this model examines how deviations from instructive arrival timings, rather than total ground access time, influence passenger choices among available high-speed rail (HSR) options. Compared to the traditional multinomial logit (MNL) model, the proposed approach better captures these behaviours, showing that passengers prioritize timing relative to advisory intervals. This framework also provides insights into evaluating the suitability of HSR options for air-rail integrated services.</div></div>","PeriodicalId":46863,"journal":{"name":"Journal of Choice Modelling","volume":"56 ","pages":"Article 100565"},"PeriodicalIF":2.4,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144885786","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-09-01Epub Date: 2025-08-05DOI: 10.1016/j.jocm.2025.100563
Monique Stinson , Abolfazl (Kouros) Mohammadian
Companies use high-level strategies to guide their decision-making and maintain strategic alignment in their actions. For example, companies may adopt a strategy of providing excellent customer service and own a private truck fleet, giving the company complete control over delivery. Despite its relevance, the concept of strategic alignment is a major omission in existing freight transportation models. In this study, we develop a methodology to integrate strategic alignment into agent-based, freight transportation models. We first identify a suitable modification to the typical agent-based structure, then outline a conceptual model relating strategy to strategic decisions. We develop a mathematical formulation to operationalize the conceptual model by introducing latent variables, which represent strategies, into the Seemingly Unrelated Regression (SUR) formulation, permitting a mix of continuous and Tobit equations. The new method is named SURTLV (Seemingly Unrelated Regression of Tobit Equations with Latent Variables). Our methodology offers many powerful features for forecasting. Binary, continuous, and contingent decisions are modeled. Choice set generation parameters are modeled as strategic decisions. Strategic decisions are modeled jointly, which acknowledges their interrelationships. Bayesian estimation with Gibbs sampling supports rich model specifications. In an empirical demonstration, we apply SURTLV to simulate a nationwide network of distribution centers and private fleets using real-world data of Fortune 500 companies. Our latent strategy measurement data come from parallel work, featuring the first real-world implementation of a novel, Natural Language Processing-based measurement generation method.
{"title":"A method to integrate strategic alignment in freight transportation behavioral models","authors":"Monique Stinson , Abolfazl (Kouros) Mohammadian","doi":"10.1016/j.jocm.2025.100563","DOIUrl":"10.1016/j.jocm.2025.100563","url":null,"abstract":"<div><div>Companies use high-level strategies to guide their decision-making and maintain strategic alignment in their actions. For example, companies may adopt a strategy of providing excellent customer service and own a private truck fleet, giving the company complete control over delivery. Despite its relevance, the concept of strategic alignment is a major omission in existing freight transportation models. In this study, we develop a methodology to integrate strategic alignment into agent-based, freight transportation models. We first identify a suitable modification to the typical agent-based structure, then outline a conceptual model relating strategy to strategic decisions. We develop a mathematical formulation to operationalize the conceptual model by introducing latent variables, which represent strategies, into the Seemingly Unrelated Regression (SUR) formulation, permitting a mix of continuous and Tobit equations. The new method is named SURTLV (Seemingly Unrelated Regression of Tobit Equations with Latent Variables). Our methodology offers many powerful features for forecasting. Binary, continuous, and contingent decisions are modeled. Choice set generation parameters are modeled as strategic decisions. Strategic decisions are modeled jointly, which acknowledges their interrelationships. Bayesian estimation with Gibbs sampling supports rich model specifications. In an empirical demonstration, we apply SURTLV to simulate a nationwide network of distribution centers and private fleets using real-world data of Fortune 500 companies. Our latent strategy measurement data come from parallel work, featuring the first real-world implementation of a novel, Natural Language Processing-based measurement generation method.</div></div>","PeriodicalId":46863,"journal":{"name":"Journal of Choice Modelling","volume":"56 ","pages":"Article 100563"},"PeriodicalIF":2.4,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144771725","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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