Integrated Choice And Latent Variable Model Research Articles

Accounting for BEV Users’ Risk Attitudes and Charging Inertia in En Route Charging Choice Behavior

This paper innovatively explores BEV (battery electric vehicle) users’ risk attitudes and charging inertia, examining their effects on en route charging and charging route choice behavior. An attitudinal survey was conducted to explore the two latent variables of risk attitudes and charging inertia in relation to socioeconomic and travel-related characteristics. ICLV (Integrated choice and latent variable) models are adopted to estimate the latent variables and the charging choice behavior simultaneously. Specifically, uncertainty in energy consumption is first considered in the ICLV model, which is represented by the available range (AR) uncertainty. Multinomial logit (MNL) models directly incorporating socioeconomic attributes are employed as a reference for comparison with ICLV models. Results illustrate that risk attitudes and charging inertia both play significant roles in modeling en route charging choice behavior. Risk-averse users and users having charging inertia value AR uncertainty more. Battery range, charging frequency, and income emerge as the most crucial factors influencing users’ intention to charge en route. The results show significant heterogeneity of BEV users in attitudes and charging choice behavior, underscoring the importance of accounting for the heterogeneity in en route charging demand estimation and deployment optimization of public charging stations, particularly for medium-to long-distance trips.

Impact of risk perception and trust in autonomous vehicles on pedestrian crossing decision: Navigating the social-technological intersection with the ICLV model

In the rapidly evolving realm of transportation technology, the dynamic relationship between pedestrians and technological innovations has attained unprecedented importance. The complex social-technological intersection surrounding pedestrian road crossings has emerged as an attention for traffic safety. What distinguishes the contemporary urban environment is the rapid assimilation of intelligent transportation systems (ITS) into the transportation infrastructure, including technological elements such as autonomous vehicles, advanced surveillance systems, and smart infrastructure. To investigate how pedestrians perceive risks, trust technology, and make decisions in this era of technological progress, we designed a video-based questionnaire utilizing the stated preference (SP) methodology. We collected SP data from 589 Chinese pedestrians and employed an integrated choice and latent variable (ICLV) model to quantify the influence of risk perception and trust in autonomous vehicle (trust in AV), treated as latent variables, on their crossing decisions. Our findings indicate that the presence of autonomous vehicles significantly affects pedestrian crossing decisions. Specifically, an increase in the approaching vehicle speed and a decrease in the approaching vehicle distance increase the pedestrians’ tendency to choose not to cross the road, and the latent variables of risk perception and trust in AV strongly predict this phenomenon. The results of the scenario analysis show that, compared with overall pedestrians, middle-aged pedestrians and high-risk perception-level pedestrians are more conservative in their crossing decisions, but high levels of trust in AV improve pedestrians’ willingness to cross the street. Additionally, the pedestrian-related findings of this study at the social-technological intersection provide better understanding of the decision process and contribute to the planning and development of urban intelligent transportation systems.

Daily Travel Mode Choice Considering Carbon Credit Incentive (CCI)—An Application of the Integrated Choice and Latent Variable (ICLV) Model

There have been many implementations of carbon credit incentives (CCIs) for promoting green travel, but research on quantifying the effectiveness remains limited. To fill this gap, this study focuses on residents’ daily transportation mode choices under the incentive of carbon credits by employing an integrated choice and latent variable (ICLV) model to adequately address the role of attitudinal variables related to carbon credits, such as perceived usefulness, perceived ease of use, and behavioral intentions. Data from a questionnaire survey show that the ICLV model provides a richer and more nuanced understanding of the green mode choice than a traditional multinomial logit (MNL) model, where the AIC value of the ICLV model (3901.17) is smaller than that of the MNL model (3910.09). Carbon credits exhibit diverse impacts across various modes of eco-friendly transportation and specific demographic groups. Commuting trips reveal noteworthy positive associations between carbon credits and the use of bicycles as well as metro/bus services. Moreover, carbon credits exert a more pronounced influence on individuals with higher education levels, older age groups, and owners of new energy vehicles, whereas their impact on high-income individuals is relatively constrained. Furthermore, perceptions of carbon credits are pivotal, with perceived utility emerging as the most influential factor. The results provide a scientific basis for formulating more effective policies regarding carbon credit and incentive measures in the future.

Theory-driven or data-driven? Modelling ride-sourcing mode choices using integrated choice and latent variable model and multi-task learning deep neural networks

Ride-sourcing services have had a disruptive impact on urban mobility. However, the perceived risk of contracting the COVID-19 virus while using these services has negatively affected people's willingness to travel by this mode. Therefore, it is essential to understand the factors influencing ride-sourcing usage during and after the pandemic. This study utilized data collected through stated preference experiments to model mode choice decisions during and after the pandemic. The study applied both theory-driven integrated choice and latent variable (ICLV) models and data-driven multi-task learning (MTL) deep neural network framework. The study found that the MTL models achieved the highest prediction accuracies. Additionally, econometric information was derived from both ICLV and MTL models. The marginal effects of level-of-service (LOS) variables were largely agreed between the ICLV and MTL models. However, only the latent variables from the ICLV models presented meaningful behavioural interpretations. The study found that individuals who believed there was greater risk associated with ride-sourcing during the pandemic were less likely to use these services. The ICLV model interpretations also indicate that the perceived safety of using ride-sourcing services is higher during the post-pandemic period compared to during the pandemic period. This finding provides reassurance regarding the recovery and growth of ride-sourcing usage in the post-pandemic era.

Campus commute mode choice in a college town: An application of the integrated choice and latent variable (ICLV) model

This study examined commute mode choice in a small college town by employing an integrated choice and latent variable (ICLV) model to adequately address the role of attitudinal variables, such as pro-car, pro-bus, and pro-environmental attitudes. The campus commute data (n = 554) came from an individual travel survey of the faculty and staff at the University of Illinois at Urbana-Champaign. The ICLV model provided a richer and more nuanced understanding of the commute mode choice of campus members than a traditional multinomial logit (MNL) model by illustrating how various sociodemographic characteristics influence mode decisions through mode preference and environmental attitudes. Results of a scenario analysis show that policy efforts that aim to shift commuter attitudes toward sustainable modes can be as effective as measures that improve actual transportation systems.

An empirical study on consumer automobile purchase intentions influenced by the COVID-19 outbreak

The sudden onset of the coronavirus disease 2019 (COVID-19) may influence individuals' automobile purchase decisions, thus bringing great uncertainty to the automobile industry. To this end, the current study investigates individuals' behaviors regarding the purchase of automobiles, both before and after the outbreak of COVID-19. An ICLV (integrated choice and latent variable) model that integrates the socio-demographics, epidemic-related variables and psychological latent variables is applied. A survey of 960 respondents was conducted in China during the epidemic. The results suggest that there was an increase in the demand for automobiles after the COVID-19 outbreak. Firstly, demand was especially high in the groups of females, citizens, high-income earners, and people who own a driving license or who live in high epidemic risk areas. Secondly, although the severity of the epidemic for residences has a positive effect on automobile demand, travelers' perceived vulnerability is the key factor motivating purchases. Thirdly, the epidemic's negative income effects reduced the purchase propensity. Several dynamic policies are proposed to automobile consumption of the special time of the COVID-19 pandemic.

Development of Integrated Choice and Latent Variable (ICLV) Models Using Matrix-Based Analytic Approximation and Automatic Differentiation Methods on TensorFlow Platform

This study further explores the multinomial probit-based integrated choice and latent variable (ICLV) models. The LDLT matrix-based analytic approximation methods, including Mendell and Elston (ME) method, bivariate ME (BME) method, and two-variate bivariate screening (TVBS) method, were adapted to calculate the multivariate cumulative normal distribution (MVNCD) function in the ICLV model because of the better performances in accuracy and computational time. Integrated with the composite marginal likelihood (CML) estimation approach, the ICLV model based on high-dimensional integration can be estimated accurately within a reasonable time. In this study, some three-alternative and four-alternative ICLV models are simulated to examine their abilities to recover model parameters. It is found that the parameter estimates and standard error estimates are acceptable for both models and the computational time is expected to decrease using tensor data structures on the TensorFlow platform. For the four-alternative ICLV models, the TVBS method has the highest level of accuracy. The BME method is also a good alternative to TVBS if computational time is of great concern. The application of the automatic differentiation (AD) technique in the model can free researchers from coding analytical gradients of log-likelihood functions and thereby greatly reduce the workload of researchers.

Airline choice model for international round-trip flights: The role of travelers’ satisfaction and personality traits

Air travelers' satisfaction with current airline options may have different effects on their decisions. We analyzed air passengers' demands for international round-trip flights using an Integrated Choice and Latent Variable (ICLV) model. Most research on airline choice has only included observable factors. To fill this gap, this study hypothesized that the airline choice process depends not only on a set of measurable factors but also on passenger satisfaction which, in turn, is influenced by travelers' personality traits. Structural equation modeling was used to construct the passengers' satisfaction to relate passengers' personality traits, airline characteristics, and passenger flight experiences with travelers' perceptions of air carrier services. The satisfaction was incorporated into the discrete choice (DC) model, which was used to evaluate travelers' airline choices for an international round-trip flight. Results indicated that passenger satisfaction with each airline service was associated with airlines’ utilities. Therefore, by incorporating traveler satisfaction into a DC model, air passenger choice behavior can be modeled more precisely. The average probability of correct predictions in the validation sample reflected the superior fit of the ICLV model. These findings highlighted the complex ways in which perceptions can influence airline choice and provided insight for targeted policy interventions.

To Purchase or Not to Purchase? Drivers of Consumers’ Preferences for Animal Welfare in Their Meat Choice

This study investigates the relevance of psychological constructs in determining consumer intention to buy and Willingness-To-Pay (WTP) for a processed meat product, cured ham, differentiated by the attributes of animal welfare, ham variety, and price. Data obtained from an online survey conducted in Germany was used to estimate an integrated choice and latent variable (ICLV) model, which is based on an extension of the Theory of Planned Behavior (TPB) framework. There are two consumer segments that are identified: one that is highly price sensitive in its product choice and one that gives roughly equal weight to the animal welfare, ham variety, and price attributes. The ICLV model shows consistency across the two groups regarding the importance of psychological constructs—moral norms, attitude, and perceived behavioral control—in explaining respondent intentions to buy cured ham and their stated product choice. Subjective norms, however, are only a significant determinant of consumer intention to buy cured ham for the price sensitive consumer group.

The role of personal norms in the choice of mode for commuting

Research in travel behavioural studies have shown that social forces play a significant role in travel decision making like socio-economic factors. Several studies have tested models with different subjective variables to investigate their effects on travel behaviour. Understanding the influence of social norm and personal norm on travel mode choice preference is essential in promoting sustainable travel, considering the reported importance of these social forces in decision-making. This study draws upon the theories underpinning these sociological constructs and the integrated choice and latent variable (ICLV) framework to develop an ICLV model by incorporating social norm and personal norm as latent variables to investigate their impact on transport mode choice decisions. The results of the ICLV model is consistent with the findings in similar studies and extend the literature in transport mode choice modelling. The results indicate that internalised norms or personal norms have a significant influence on individual behaviour, and positively influence travel behaviour. This strengthens the claim that activated norms and pro-environmental behaviour (PEB) influence overt behaviour by inducing a sense of obligations to act. The results also suggest that individuals with pro-environmental attitude are likely to travel with sustainable travel modes. Thus, reinforcing the importance of sociological and psychological factors in decision-making. Unlike studies involving social norms and personal norms, this research is novel because it applies the ICLV framework to empirically investigate the impact of these sociological constructs on travel mode choice behaviour by incorporating them as latent variables in an ICLV model. The contribution of this study to the literature is that it shows that personal norm has a substantial positive impact on sustainable travel. The impact of this study could be situated in the framework of sustainable transport. The findings are relevant for policymaking, the development of policies meant to create awareness of the consequences of travel behaviour could promote the development of strong personal norms, and consequently influence travel decision making. This can be useful for promoting sustainable travel as the findings shed light on the characteristics of individuals most likely to travel by active modes or PT.

The habit-driven life: Accounting for inertia in departure time choices for commuting trips

This paper aims to explicitly account for the impact of inertia (or habit) on departure time decisions, and explore (1) to what extent departure time is influenced by inertia, (2) what influences individuals’ inertia with respect to departure time decisions, and (3) to what extent it impacts transport policies. We estimate an integrated choice and latent variable (ICLV) model using a stated preference survey for morning car commuters in the Greater Copenhagen Area. We interact the rescheduling components in the Scheduling Model (SM) with the latent variable Inertia. The modelling results show that higher levels of inertia yields higher rescheduling penalties and lower willing to shift departure time. Furthermore, we find that inertia in departure time is influenced by gender, presence of children in the household as well as work type. We test the behavioral responses to demand management policies for segments with different inertia, and find that the least inertial segment showed the highest substitution patterns, while the most inertial segment show the lowest substitution patterns. Finally, we compared the ICLV model to a reference model without inertia, and find that the effects of the demand management strategy is overestimated if inertia is neglected.

Analysis of Choice Behaviors of Railway Shippers for Freight Services Based on a Fuzzy Integrated Choice and Latent Variable Model

The existing schemes show that the application of an Integrated Choice and Latent Variable (ICLV) model in choice behaviors of railway shippers in freight services provided by China Railway Express (CRE) and private logistics enterprises (PLEs) is a challenge. Therefore, this paper focuses on the freight service products of CRE and PLEs and constructs a Fuzzy ICLV model by combining a fuzzy comprehensive evaluation with the ICLV model to analyze choice behaviors in freight services of railway shippers. The Fuzzy ICLV model offered significant advantages because it allowed us to incorporate latent variables that regard perceived convenience and reliability with a traditional discrete choice model (DCM). The results of the Fuzzy ICLV model clearly confirm that shippers’ perceptions of the convenience and reliability of freight service products impact their freight service product choices. The results also show that the freight, time, distance from shippers to the sales department, destination distance, occupation, monthly income level, order quantity, cargo weight, and type are all major factors that affect Chinese railway shippers’ choice behaviors for freight services. The goodness-of-fit measure of the Fuzzy ICLV model is 0.325, which is higher than that of the Nested Logit (NL) model. The results of this study can fill the gaps in choice behaviors in freight services research on Chinese railway shippers and have important implications for operators of CRE to adjust their freight service strategies or products.

Shopping trip mode choice of older adults: an application of activity space and hybrid choice models in understanding the effects of built environment and personal goals

Rapid growth of the older population worldwide, coupled with their overreliance on automobile and its negative consequences for the environment and for their wellbeing, has encouraged research on travel behavior of this age group. This study contributes to the literature by providing an integrated analysis of the effects of sociodemographic, built environmental, psycho-social, trip, and activity space attributes on shopping trip mode choice of older adults in Helsinki Metropolitan Area. Data was collected using an online map-based survey. Two person-based activity space models were developed, in addition to the commonly used 500-m buffer, to measure activity space and built environmental attributes. Integrated Choice and Latent Variable (ICLV) models were utilized to explore modal choice. Although the use of activity space models did not significantly increase the fit of ICLV models, it provided different information. Walkability index showed a positive significant effect on walking trips in individualized residential exposure model. A positive effect on transit use or biking was found in individual home range and 500-m buffer. The shape and dispersion of activity spaces affected mode choice as well. Green space influenced the goal of being physically active which in turn affected mode choice. Three personal goals of being physically active, having cultural and social affairs, and caring for others influenced mode choice. Results indicate the priority of the use of activity space and hybrid choice models in understanding travel behavior. Findings of this study can guide policies aiming to increase the use of more sustainable modes among this age group.

Modeling and Analysis of Ticketing Channel Choice for Intercity Bus Passengers: A Case Study in Beijing, China

Buses represent the main mode for intercity passenger transportation in China. In recent years, a multichannel ticketing strategy has been widely employed in the bus passenger transportation industry. However, the mechanisms and key drivers of the channels through which bus passengers purchase tickets are underexplored. Thus, the aim of this study is to empirically apply an integrated choice and latent variable (ICLV) approach to analyze ticketing channel choice behavior and the heterogeneous preferences of bus passengers. The variables incorporated in the model include the socioeconomic characteristics of passengers, trip attributes, and latent attitudes with 12 ordinal indicators. Based on the data of 1800 participants collected from the city of Beijing, China, this study develops a ticketing channel choice ICLV model merging a discrete choice model with a structural equation model. The key factors that affect the channel preference are further discussed through a comparison with a conventional multinomial logit (MNL) model. The results reveal that the three attitudinal variables have a significant influence on ticketing channel choice. Furthermore, this study indicates that perceptual differences exist due to various socioeconomic and trip characteristics. Personal privacy is a major obstacle that prevents passengers from choosing online channels, especially for older passengers and those with lower education.

Facilitating an expectation-maximization (EM) algorithm to solve an integrated choice and latent variable (ICLV) model with fully correlated latent variables

It is well known that estimating the parameters of an integrated choice and latent variable (ICLV) model is not a trivial undertaking. The log-likelihood of an ICLV model cannot be evaluated analytically, and can only be evaluated by a simulation that requires large numbers of sample draws. While conducting simulation-based model estimations, researchers often encounter an estimation failure. Sohn (2017) suggests a novel estimation method to circumvent the problem by using an expectation-maximization algorithm (EM). However, a drawback of this method continues to be the requirement of a huge amount of computer memory to deal with an augmented covariance matrix. In the present study, this problem was overcome by connecting each latent variable in a structural equation to all individual specific variables. This restriction did not hamper the utility of an ICLV model during empirical experimentation. The main contribution of this study is to introduce a simple method devised to solve large-scale ICLV models.

An integrated choice and latent variable model for multiple discrete continuous choice kernels: Application exploring the association between day level moods and discretionary activity engagement choices

In the recent years, multiple discrete continuous (MDC) models have emerged as a popular framework to simultaneously model the choice of multiple goods (that are imperfect substitutes to one another) and the associated consumption quantities. The paper presents a new integrated choice and latent variable (ICLV) model implementation called the Hybrid Multiple Discrete Continuous (HMDC) model that is capable of incorporating the influence of psychological factors (modeled as latent constructs) on MDC choice behaviors. Estimation of ICLV models (with single discrete choice kernels and MDC kernels) has been a challenge owing to the high dimensional integrals involved in the likelihood function. The typically used maximum simulated likelihood estimation (MSLE) approach becomes cumbersome when the dimensionality of integration increases. In this research, a composite marginal likelihood (CML) based estimation approach is proposed for parameter estimation of the HMDC framework. Unlike the ICLV model implementations with single discrete choice kernel, the dimension of the integral to be decomposed in the HMDC varies across observations. This necessitated the use of weights when decomposing the likelihood function using the CML approach. A simulation study was conducted using synthetic datasets to demonstrate the superiority of the weighted CML approach over its unweighted counterpart in the presence of MDC choice kernel. The applicability of the proposed model formulation and associated estimation routine was demonstrated using an empirical case study with data from the 2013 American Time Use Survey (ATUS). The empirical study identifies interesting association between day level moods and discretionary activity participation decisions.

Effect of perception and attitudinal variables on mode choice behavior: A case study of Indian city, Agartala

Attitudes and perceptions play a significant role in the individual’s selection of a travel mode. In the developing cities of India, socioeconomic characteristics vary significantly among the trip makers and subsequently the perception towards various travel modes. In this study, variables such as the comfort and flexibility offered by different travel modes were used to estimate an Integrated Choice and Latent Variable (ICLV) model for understanding the effect of latent variables on mode choice behavior. With a model estimated using 561 work trip data collected from Agartala city, located in the North East part of India, we found that the comfort and flexibility influence the individual’s choice of mode. The results obtained from the ICLV model support the hypothesis that the effect of attitude and perception is important in mode choice behavior and useful for transportation planners and policy makers.

How, when and why integrated choice and latent variable models are latently useful

Integrated Choice and Latent Variable (ICLV) models are an increasingly popular extension to discrete choice models that attempt explicitly to model the cognitive process underlying the formation of any choice. This study was born from the discovery that an ICLV model can in many cases be reduced to a choice model without latent variables that fits the choice data at least as well as the original ICLV model from which it was obtained. The failure of past studies to recognize this fact raised concerns about other benefits that have been claimed with regards to the framework. With the objective of addressing these concerns, this study undertakes a systematic comparison between the ICLV model and an appropriately specified reduced form choice model. We derive analytical proofs regarding the benefits of the framework and use synthetic datasets to corroborate any conclusions drawn from the analytical proofs. We find that the ICLV model can under certain conditions lead to an improvement in the analyst's ability to predict outcomes to the choice data, allow for the identification of structural relationships between observable and latent variables, correct for bias arising from omitted variables and measurement error, reduce the variance of parameter estimates, and abet practice and policy, all in ways that would not be possible using the reduced form choice model. We synthesize these findings into a general process of evaluation that can be used to assess what gains, if any, might be had from developing an ICLV model in a particular empirical context.

Estimation of multinomial probit-kernel integrated choice and latent variable model: comparison on one sequential and two simultaneous approaches

Integrated choice and latent variable (ICLV) model incorporates latent factors into standard discrete choice model with aim to provide greater explanatory power. Using simulated datasets, this study makes a comparison among three estimation approaches corresponding to the sequential approach and two simultaneous approaches including the maximum simulated likelihood with GHK estimator and maximum approximate composite marginal likelihood (MACML) approach, to evaluate their abilities to recover the underlying parameters of multinomial probit-kernel ICLV model. The results show that both simultaneous approaches outperform the sequential approach in terms of estimates accuracy and efficiency irrespective of the sample sizes, and the MACML approach is the most preferable due to its best performance on recovering true values of parameters with relatively small standard errors, especially when the sample size is large enough.

Introducing non-normality of latent psychological constructs in choice modeling with an application to bicyclist route choice

In the current paper, we propose the use of a multivariate skew-normal (MSN) distribution function for the latent psychological constructs within the context of an integrated choice and latent variable (ICLV) model system. The multivariate skew-normal (MSN) distribution that we use is tractable, parsimonious in parameters that regulate the distribution and its skewness, and includes the normal distribution as a special interior point case (this allows for testing with the traditional ICLV model). Our procedure to accommodate non-normality in the psychological constructs exploits the latent factor structure of the ICLV model, and is a flexible, yet very efficient approach (through dimension-reduction) to accommodate a multivariate non-normal structure across all indicator and outcome variables in a multivariate system through the specification of a much lower-dimensional multivariate skew-normal distribution for the structural errors. Taste variations (i.e., heterogeneity in sensitivity to response variables) can also be introduced efficiently and in a non-normal fashion through interactions of explanatory variables with the latent variables. The resulting model we develop is suitable for estimation using Bhat’s (2011) maximum approximate composite marginal likelihood (MACML) inference approach. The proposed model is applied to model bicyclists’ route choice behavior using a web-based survey of Texas bicyclists. The results reveal evidence for non-normality in the latent constructs. From a substantive point of view, the results suggest that the most unattractive features of a bicycle route are long travel times (for commuters), heavy motorized traffic volume, absence of a continuous bicycle facility, and high parking occupancy rates and long lengths of parking zones along the route.