Journal of Choice Modelling最新文献

Route choice models are a vital tool for evaluating the impact of transportation policies and infrastructure improvements, such as the addition of new roads, tolls, or congestion charges. They can also be used to predict traffic flow and congestion levels, which is essential for traffic management and control. The aim of this manuscript is to provide a comprehensive analysis of the effectiveness and limitations of various game theory (GT) based models used in route choice modelling. The manuscript draws upon the theoretical foundations of game theory to explore the complex decision-making processes of travelers in transportation networks, focusing on factors such as travel time, congestion. The manuscript discusses the challenges and opportunities associated with implementing game theory-based models in practice, including the data requirements, model calibration, and computational complexity. These factors are considered in relation to the suitability of different game theory-based models, including cooperative, non-cooperative, and evolutionary games. The comparative critiques presented in this manuscript provide guidance for future research directions in the field of private route choice modelling, aimed at academic researchers, engineers, policymakers, and industrial communities.

Recently, there has been a growing interest in comparing machine learning models and Discrete Choice Models. However, no studies have been conducted on image choice problems. This study aims to fill this gap by conducting a stated preference experiment that involves choosing streets for cycling based on real-world street-view images. The choice data obtained were used to estimate and compare four models: Multinomial Logit, Mixed Logit, Deep Neural Network, and Convolutional Neural Network. Additionally, the study tested the effects of different data formats on the models' performances, including semantic interpretation, semantic segmentation, raw image, semantic map, and enriched image. The comparison focused on the models' explainability and out-of-sample predictability with new but similar choice data. The results show that (1) the Discrete Choice Models exhibit nearly equal predictability to the Deep Neural Network models, but significantly outperform the Convolutional Neural Network models; (2) the Discrete Choice Models are more explainable than the Deep Neural Network models; and (3) models trained on semantic interpretation data demonstrate better predictability than those trained on semantic segmentation data and imagery data.

The prevalence of methamphetamine use disorder (MUD) as a major public health problem has increased dramatically over the last two decades, reaching epidemic levels, which pose high costs to the health care systems worldwide and is commonly associated with experience-based decision-making (EDM) aberrant. However, precise mechanisms underlying such non-optimally in choice patterns still remain poorly understood. In this study, to uncover the latent neurobiological and psychological meaningful processes of such impairment, we apply a reinforcement learning diffusion decision model (RL-DDM) while methamphetamine abuser participants ($n=18$, all men; mean (±SD) age: 27.3±5) and age/sex-matched healthy controls ($n=25$, all men; mean (±SD) age: 26.8.0±3.63) perform choices to resolve uncertainty within a simple probabilistic learning task with rewards and punishments. Preliminary behavior results indicated that addicts made maladaptive patterns of learning that mirrored in both choices and response times (RTs). Furthermore, modeling results revealed that such EDM impairment (maladaptive pattern in optimal selection) in addicts was more imputable to both increased learning rates (more sensitive to outcome fluctuations) and decreased drift rate (less reward sensitivity) compared to healthy. In addition, addicts also showed substantially longer non-decision times (attributed to slower RTs), as well as lower decision boundary criteria (reflection of impulsive choice). Taken together, our findings reveal precise mechanisms associated with EDM impairments in methamphetamine use disorder and confirm the debility of the options values assignment system as the main hub in learning-based decision making.

The emergence of modern tools and technologies gives a unique opportunity to collect large amounts of data for understanding behaviour. However, the generated datasets are often imbalanced, as individuals might contribute to the datasets at different frequencies and periods. Models based on these datasets are challenging to estimate, and the results are not straightforward to interpret without considering the sample structure. This study investigates the issue of handling imbalanced panel datasets for modelling individual behaviour. It first conducts a simulation experiment to study to which degree mixed logit models with and without panel reproduce the population preferences when using imbalanced data. It then investigates how the application of bias reduction strategies, such as subsampling and likelihood weighting, influences model results and finds that combining these techniques helps to find an optimal trade-off between bias and variance of the estimates. Considering the conclusions from the simulation study, a large-scale case study estimates bicycle route choice models with different correction strategies. These strategies are compared in terms of efficiency, weighted fit measures, and computational burden to provide recommendations that fit the modelling purpose. We find that the weighted panel mixed multinomial logit model, estimated on the entire dataset, performs best in terms of minimising the bias-efficiency trade-off in the estimates. Finally, we propose a strategy that ensures equal contribution of each individual to the estimation results, regardless of their representation in the sample, while reducing the computational burden related to estimating models on large datasets.

Best-worst scaling (BWS) is a theory-driven choice experiment used for the prioritization of a finite number of options. Within the context of prioritization, BWS is also known as MaxDiff, BWS object case, and BWS Case 1. Now used in numerous fields, we conducted a transdisciplinary literature review of all published applications of BWS focused on prioritization to compare norms on the development, design, administration, analysis, and quality of BWS applications across fields. We identified 526 publications published before 2023 in the fields of health (n = 195), agriculture (n = 163), environment (n = 50), business (n = 50), linguistics (n = 24), transportation (n = 24), and other fields (n = 24). The application of BWS has been doubling every four years. BWS is applied globally with greatest frequency in North America (27.0%). Most studies had a clearly stated purpose (94.7%) that was empirical in nature (89.9%) with choices elicited in the present tense (90.9%). Apart from linguistics, most studies: applied at least one instrument development method (94.3%), used BWS to assess importance (63.1%), used ‘most/least’ anchors (85.7%), and conducted heterogeneity analysis (69.0%). Studies predominantly administered surveys online (58.0%) and infrequently included formal sample size calculations (2.9%). BWS designs in linguistics differed significantly from other fields regarding the average number of objects (p < 0.01), average number of tasks (p < 0.01), average number of objects per task (p = 0.03), and average number of tasks presented to participants (p < 0.01). On a 5-point scale, the average PREFS score was 3.0. This review reveals the growing application of BWS for prioritization and promises to foster new transdisciplinary avenues of inquiry.

In the context of discrete choice modeling, the extraction of potential behavioral insights from large datasets is often limited by the poor scalability of maximum likelihood estimation. This paper proposes a simple and fast dataset-reduction method that is specifically designed to preserve the richness of observations originally present in a dataset, while reducing the computational complexity of the estimation process. Our approach, called LSH-DR, leverages locality-sensitive hashing to create homogeneous clusters, from which representative observations are then sampled and weighted. We demonstrate the efficacy of our approach by applying it on a real-world mode choice dataset: the obtained results show that the samples generated by LSH-DR allow for substantial savings in estimation time while preserving estimation efficiency at little cost.

Efficient experimental designs aim to maximise the information obtained from stated choice data to estimate discrete choice models' parameters statistically efficiently. Almost without exception efficient experimental designs assume that decision-makers use a Random Utility Maximisation (RUM) decision rule. When using such designs, researchers (implicitly) assume that the decision rule used to generate the design has no impact on respondents' choice behaviour. This study investigates whether the decision rule assumption underlying an experimental design affects respondents' choice behaviour. We use four stated choice experiments on coastal adaptation to climate change: Two are based on experimental designs optimised for utility maximisation and two are based on experimental designs optimised for a mixture of RUM and Random Regret Minimisation (RRM). Generally, we find that respondents place value on adaptation measures (e.g., dykes and beach nourishments). We evaluate the models' fits and investigate whether some choice tasks particularly invoke RUM or RRM decision rules. For the latter, we develop a new sampling-based approach that avoids the confounding between preference and decision rule heterogeneity. We find no evidence that RUM-optimised designs invoke RUM-consistent choice behaviour. However, we find a relationship between some of the attributes and decision rules, and compelling evidence that some choice tasks invoke RUM consistent behaviour while others invoke RRM consistent behaviour. This implies that respondents’ choice behaviour and choice modelling outcomes are not exogenous to the choice tasks, which can be particularly critical when information on preferences is used to inform actual decision-making on a sensitive issue of common interest as climate change.

Japan is amongst those countries known for long hours and an inflexible working culture that makes it difficult to pursue work-life balance. The question is what aspects of job market flexibility are most valuable to Japanese women and men and to what extent are these values are driven by feelings of guilt. Using a nationwide sample of 1046 working-age adults, we conduct a choice experiment that examines willingness to trade wages against changes in job characteristics such as the extent of overtime, job security, the possibility of work transfer and relocation. Our results suggest that: i) workers have high WTP (willingness to pay) to avoid extreme overtime and internal transfers but not to safeguard job security or to avoid relocation, ii) women have higher WTP than men, and iii) the gap is driven only in part by feelings of guilt. Perhaps surprisingly, women's preferences are generally not affected by the presence or absence of children in the household while men's WTP for work-life balance is generally lower in the presence of children, but less influenced by guilt.

This study presents an Ordinal version of Residual Logit (Ordinal-ResLogit) model to investigate the ordinal responses. We integrate the standard ResLogit model into COnsistent RAnk Logits (CORAL) framework, classified as a binary classification algorithm, to develop a fully interpretable deep learning-based ordinal regression model. As the formulation of the Ordinal-ResLogit model enjoys the Residual Neural Networks concept, our proposed model addresses the main constraint of machine learning algorithms, known as black-box. Moreover, the Ordinal-ResLogit model, as a binary classification framework for ordinal data, guarantees consistency among binary classifiers. We showed that the resulting formulation is able to capture underlying unobserved heterogeneity from the data as well as being an interpretable deep learning-based model. Formulations for market share, substitution patterns, and elasticities are derived. We compare the performance of the Ordinal-ResLogit model with an Ordered Logit Model using a stated preference (SP) dataset on pedestrian wait time and a revealed preference (RP) dataset on travel distance. Our results show that Ordinal-ResLogit outperforms the traditional ordinal regression model. Furthermore, the results obtained from the Ordinal-ResLogit RP model show that travel attributes such as driving and transit cost have significant effects on choosing the location of non-mandatory trips. In terms of the Ordinal-ResLogit SP model, our results highlight that the road-related variables and traffic condition are contributing factors in the prediction of pedestrian waiting time such that the mixed traffic condition significantly increases the probability of choosing longer waiting times.

To study the mechanism of pedestrians' route choice behaviors under non-emergency conditions, we conducted a series of route choice experiments. Participants were required to choose between two routes. Possible controls, including bottleneck, social distancing, extra reward, were tested in the experiments. Results shows that the bottleneck effect can dramatically influence the route-choice behaviors, whereas the impact of social distancing and reward were much weaker. Five typical logit models, including Binary Logit (BL) model, Mixed Logit (ML) model, Panel Logit (PL) model, Latent Class Logit (LCL) model and Latent Class Logit including Panel effect (LCL-P) model were employed. PL and LCL models performed better in this study, while the results of LCL-P model were the best. This suggests the existence and importance of heterogeneity in route choice behavior. Two classes of pedestrians were identified, with one being comfort-seeking and the other being speed-seeking. ML model did not perform well in this study, which is contrary to some previous studies. All these results could be helpful for understanding the essence of pedestrians’ route choice behaviors.