Journal of Choice Modelling最新文献

Departure time choice is a key component of travel behavior that directly influences the spatial and temporal distribution of travel demand. This paper tries to develop a modeling framework for choosing the departure time that minimizes travel costs. In this regard, a modeling framework for generating departure time recommendations is proposed and applied to real commuting trips. The methodology is an extension of the departure time choice model with unreliable travel time. Two cases are considered. The first calculates the optimal time of departure when the mean of the travel time varies by time of day but the variance is constant. An exact solution to the departure time choice problem is provided for this case. In the second case, both the mean and variance vary with the time of day. A numerical solution is proposed; it is proved that the sequence of the numerical solution is contractive with a unique fixed point obtainable for any initial guess. We apply both to the departure time planning problem for a transportation operator that offers repetitive mandatory trips on a dense network. The case study offers two insights into departure time choice analyses. First, the assumption that the travel time variance at peak hours is constant induces biases for the optimal departure time. However, this assumption provides plausible results for the off-peak period. Second, travelers relying on personal judgment may have significantly different costs of travel than passengers making their decisions based on the system's recommendations.

With the increased affordability of eye-tracking technology, its applications in discrete choice experiments (DCEs) are rapidly increasing. It is critical to understand the current state of research, challenges, and potential value of this technology for future studies. This article provides an interdisciplinary perspective on three main themes of this literature – (i) utilizing visual attention measures to identify the effect of top-down and bottom-up processing on information search and preferences, (ii) modelling advancements to incorporate visual attention measures into the discrete choice models, (iii) examining the effect of the DCE design on the consumer's information search processes. Then, we highlight four areas of improvement in these themes. First, visual attention measures alone might not be sufficient proxies for representing information processing. We lay out a research agenda to precisely measure information processing by integrating eye-tracking and electroencephalogram (EEG) data. Second, traditional static behaviour models do not effectively leverage the dynamic nature of eye-tracking data. We propose to adapt dynamic behavioural models from cognitive psychology where the mathematical representation of the decision-making process is consistent with the eye-tracking data. Third, existing studies provide descriptive (instead of prescriptive) insights about the effect of DCE design on information search. Thus, instead of DCE design, eye-tracking data can be used ex-post to select behavioural models aligned with observed search patterns. Fourth, convenience sampling protocols in eye-tracking studies raise questions about the internal validity of findings. Future DCEs with eye-tracking should adopt protocols used in randomised control trial studies.

This study investigates the process of stage transition in traveler's adoption of carsharing. The carsharing adoption behaviors are classified into five stages using the transtheoretical model: precontemplation (PC), contemplation (C), preparation (PA), action (A), and maintenance (M). In Beijing, a comprehensive survey combining stated preference and revealed preference methods is conducted to collect data on travelers' adoption attitudes and related attributes, such as socioeconomic characteristics, travel features, and carsharing usage environments. Specifically, ten latent attitudinal variables are derived from the technology acceptance model and theory of planned behavior. An integrated choice and latent variable model is developed to examine the impact of these explanatory factors on the transition processes between the five adoption stages. The findings reveal that the effects of explanatory factors on stage transitions in carsharing adoption behaviors differ across stages. Factors such as education level, income, having children, car ownership, familiarity with carsharing, personal condition, and social pressures play a crucial role in the early stages of understanding before carsharing adoption, namely the PC-C and C-PA stage transitions. Conversely, in the later stages (PA-A and A-M), travelers show a greater emphasis on carsharing infrastructure, including station reachability, vehicle availability, and perception toward rental stations and shared vehicles. Additionally, factors related to travel habits and expectations are influential throughout all stages of the carsharing adoption process. Lastly, three categories of measures, namely soft, neutral, and hard measures, are derived from the results to guide policymakers and carsharing operators in formulating strategies for the operation and management of carsharing systems.

In response to the Covid-19 pandemic, many countries have adopted measures to contain the spread of the virus, including mandatory quarantine for inbound travellers. This research investigates the preferences of residents of New South Wales, Australia, towards the mandatory quarantine protocol adopted in the state. Heterogeneity in individual preferences is explored by advancing the Logit Mixed Logit (LML) model defined by Train (2016). Two approaches are suggested to decompose individual heterogeneity in this framework and are applied to data collected via a stated preference experiment. The empirical findings demonstrate that on average, the community prefers returned travellers be quarantined in dedicated quarantine facilities rather than be quarantined at home or using hotels, but are mostly indifferent to how long travellers are quarantined for, and how many travellers are allowed to return to Australia. The sample do however have a preference, on average for travellers having to pay less to quarantine, meaning they wish to see greater government subsidies. However, the modelling approach demonstrates that the common use of averages potentially masks diverse preferences, and is not representative of community wants and desires, thus possibly leading to incorrect inferences about policy impacts.

Community support for climate change adaptation projects markedly benefits effective protection. A relevant driver of community support is the perceived attribution of responsibility to individuals. If individuals attribute responsibility for adaptation to others, e.g. public authorities, this reduces the adaptation efforts of the individual, might induce preference uncertainty, and can lead to maladaptation. We study individuals' perceptions of personal responsibility and preferences for coastal protection in a setting in which individuals have little formal responsibility. To do so, we collect data from the Maldives, a small island development state with significant risks of seaborne hazards where responsibility for coastal protection formally rests with the central government without significant involvement of local communities. Using survey measures and a Discrete Choice Experiment (DCE), we investigate respondents' sense of personal responsibility and their preferences for climate change adaptation distinguishing between preferences for hard, man-made structures and soft, working-with-nature protection approaches. The results show that responsibility perception plays an important role for stated willingness to support protective measures. However, they further show a mismatch between formally assigned and perceived responsibility for protection with a majority of respondents having a strong sense of personal responsibility for protection. In addition, the DCE results indicate a misalignment of people's preferences and the measures implemented by the government. While the latter belong to the group of hard protection measures, the majority of respondents show a clear preference for soft protection. We discuss the implications of these findings and highlight the importance of a better understanding of drivers of responsibility perceptions.

Using rank-ordered logit regression, researchers typically analyze consumer preference data collected with Best-Worst Scaling (BWS) surveys. We propose a generalized rank-ordered logit (GROL) model that allows flexibility in modeling preference heterogeneity. The GROL and mixed rank-ordered logit model (MROL) accommodate preference heterogeneity. However, the GROL also allows one to model heterogeneity as a function of demographic or environmental variables. A Monte Carlo experiment compares the estimates of accuracy and precision of the proposed GROL estimation with the MROL specification. Simulation results suggest that the GROL model performs comparatively well when the GROL or the MROL is the true data-generating process (dgp). Coefficient and willingness-to-pay estimates of the GROL are more precise and accurate compared to the MROL when the MROL is the true dgp. We surmise that the increased precision of the GROL estimator arises from the added flexibility for modeling different sources of heterogeneity. An empirical application analyzes a BWS survey on consumer preferences for single-use eating-ware (SUEW) products made from biobased materials. Findings suggest that consumers value most product degradability and using non-plastic materials to fabricate SUEW. Consumers also valued the rapidity of product degradability and using non-plastic materials to make SUEW plates. Respondent attentiveness also affected willingness-to-pay (WTP) estimates across attributes. Results suggest attentive respondents were about $3.00 more WTP for biodegradable SUEW than inattentive respondents.

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.