Integrated Choice And Latent Variable 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.

Beyond conventional: Analysing the factors affecting the adoption of electric four-wheelers in an Indian metropolis

This study develops an integrated choice and latent variable (ICLV) model to analyse the impact of vehicle attributes, service attributes and supporting government schemes on the likelihood to choose an electric four-wheeler in Bengaluru, India. Further, the influence of socio-demographic variables, travel attributes and latent factors on electric four-wheeler adoption behaviour is also evaluated. The findings indicate purchase price, operating cost, and charging time have a significant negative influence on the intention to adopt electric four-wheelers, while driving range and charging facilities have a significant positive impact. Perceived risks studied in the context of safety and reliability are found to be the biggest barriers to the adoption of electric vehicles followed by lack of infrastructural readiness while environmental benefits are found to be the biggest motivators for electric four-wheeler adoption. These findings are expected to help decision-makers towards widespread adoption of electric four-wheelers in developing economies such as India.

How individual perceptions of transportation systems influence mode choice for mobility-challenged people: A case study in Dhaka using an integrated choice and latent variable model

This study examines how mobility-challenged persons (MCPs) navigate the limitations of transportation systems, using an integrated choice and latent variable (ICLV) model. Building on the experience of MCPs in a South Asian megacity context, that of Dhaka, Bangladesh, the empirical strategy accounts for perceptions of mode-specific challenges, which arguably vary by mobility impairment. These perceptions are posited as latent variables. Drawing on survey data collected from 400 MCPs living in Dhaka, the latent variables were constructed via a factor analysis of 18 statements about the experienced severity (ranked on a scale from 1 to 5) of mode-specific challenges. Holding socio-demographic and travel-related factors constant, we find that perceptions of mode-specific challenges significantly influence mode choices – while the degree of impairment alone, and related mobility aid needed, do not. Perceived limitations of the walking infrastructure shift MCPs' travel demand towards the bus, whereas bus fare-related issues encourage the use of non-motorized and powered three-wheelers. We recommend that mode choice models include latent variables related to MCPs’ perceptions of various modes to more accurately inform universal access policies.

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.

On-demand transit user preference analysis using hybrid choice models

In light of the increasing interest to transform the fixed-route public transit (FRT) services into on-demand transit (ODT) services, there exists a strong need for a comprehensive evaluation of the effects of this shift on the users. Such an analysis can help municipalities and service providers to design and operate more convenient, attractive, and sustainable transit solutions. To understand the user preferences, we developed three hybrid choice models: integrated choice and latent variable (ICLV), latent class (LC), and latent class integrated choice and latent variable (LC-ICLV) models. We used these models to analyze the public transit user's preferences in Belleville, Ontario, Canada. Hybrid choice models were estimated using a rich dataset that combined the actual level of service attributes obtained from Belleville's ODT service and self-reported usage behaviour obtained from a revealed preference survey of the ODT users. The latent class models divided the users into two groups with different travel behaviour and preferences. The results showed that the captive user's preference for ODT service was significantly affected by the number of unassigned trips, in-vehicle time, and main travel mode before the ODT service started. On the other hand, the non-captive user's service preference was significantly affected by the Time Sensitivity and the Online Service Satisfaction latent variables, as well as the performance of the ODT service and trip purpose. This study attaches importance to improving the reliability and performance of the ODT services and outlines directions for reducing operational costs by updating the required fleet size and assigning more vehicles for work-related trips.

Research on the suitability of telework in the context of COVID-19

PurposeCoronavirus disease 2019 (COVID-19) has made telecommuting widely valued, but different individuals have different degrees of acceptance of telecommuting. This article aims to identify suitable individuals for telework and to clarify which types of workers are suitable for what level of telework, set scientific, reasonable hybrid work ratios and processes and measure their suitability.Design/methodology/approachFirst, two working scenarios of different risk levels were established, and the theory of planned behavior (TPB) was used to introduce latent variables, constructing a multi-indicator multi-causal model (MIMIC) to identify suitable individuals, and second, constructing an integrated choice and latent variable (ICLV) model of the working method to determine the suitability of different types of people for telework by calculating their selection probabilities.FindingsIt is possible to clearly distinguish between two types of suitable individuals for telework or traditional work. Their behavior is significantly influenced by the work environment, which is influenced by variables such as age, income, attitude, perceived behavioral control, work–family balance and personnel exposure level. In low-risk scenarios, the influencing factors of the behavioral model for both types of people are relatively consistent, while in high-risk scenarios, significant differences arise. Furthermore, the suitability of telework for the telework-suitable group is less affected by the pandemic, while the suitability for the non-suitable group is greatly affected.Originality/valueThis study contributes to previous literature by: (1) determining the suitability of different population types for telework by calculating the probability of selection, (2) dividing telework and traditional populations into two categories, identifying the differences in factors that affect telework under different epidemic risks and (3) considering the impact of changes in the work scenario on the suitability of telework for employees and classifying the population based on the suitability of telework in order to avoid the potential negative impact of telework.

What motivates the use of shared mobility systems and their integration with public transit? Evidence from a choice experiment study

Shared mobility, including bike-sharing, shared e-scooter, and ride-hailing, could improve transportation sustainability when substituting private car use and integrating with public transit. However, if shared mobility competes with other green modes, it cannot guarantee sustainability benefits. The competing and synergistic relationships between conventional modes and shared mobility are complex and not well-studied to date. Understanding users’ preferences in mode choice decisions among shared mobility, conventional modes, and multimodal systems can help better evaluate the impact of shared mobility adoption and support related policies. This paper presents the design and results of a stated-preference choice experiment study conducted in Indianapolis, Indiana. An integrated choice and latent variable (ICLV) model was estimated to identify attributes that affect travelers’ mode choices for both non-commuting and commuting purposes. Results show that (1) travel cost and travel time are significant variables and their impact on mode choice for system integration and competition is elastic; and (2) user heterogeneity can be observed through three latent variables (perceptions of shared mobility, travel attributes importance, and social values) to identify traveler’s preferences and concerns for mode choice. The contributions from this study include: (1) developing quantitative models to estimate mode choice behavior with regard to shared mobility use, and (2) identifying a set of policy guidelines for system development to encourage multimodal usage and decrease car dependency.

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.

An exploration of the preferences and mode choice behavior between autonomous demand-responsive transit and traditional buses

With the advancement in autonomous driving techniques, autonomous demand-responsive transit (ADRT) is a newly emerging sustainable transport mode for the future, which will provide more flexible services to public users. ADRT offers benefits such as flexible stops and routes and comfortable seats, but it also involves risks due to the vehicles being driverless. This paper particularly investigates users’ preferences and attitudes towards ADRT, and mode choice behavior between ADRT buses and traditional buses. A survey with Likert scale statements and stated preference (SP) choice scenarios is designed and conducted to explore users’ attitudes towards the safety risks of autonomous vehicles (AVs), social concerns, service flexibility concerns when using AVs, interest in new things, and shuttle mode choices. An integrated choice and latent variable (ICLV) model is adopted to explore users’ psychological factors through latent variables and to integrate them into mode choice behavioral modeling. Estimated results indicate that users’ attitudes towards AV safety risks, their social concerns, and their flexibility concerns with ADRT strongly influence their mode choices and are strongly related to sociodemographic and travel-related factors such as age, gender, income, education, number of family members. In general, a young age, a high education level, a higher income, private car ownership, and better knowledge of AVs are positively related to attitudes towards ADRT. Females, users from large families, and users with driving licenses or long commuting times are less willing to adopt ADRT. The study's outcomes highlight significant heterogeneities among users and can be highly valuable for policymakers, such as government authorities, in providing social support and designing policies targeting specific population groups. This will be beneficial in attracting more users to this emerging mobility service and contributing to sustainable urban development.

An integrated estimation approach to incorporate latent variables through SEM into discrete mode choice models to analyze mode choice attitude of a rider

Mode Choice preference of each individual commuter depends on a number of factors. The classic models for choice estimation have majorly focused on the quantifiable variables such as travel cost, travel time, socio-economic characteristics of riders, trip characteristics, and mode characteristics. This paper aims at analyzing the hidden impact of latent variables integrated with observed variables on mode choice attitude of a rider for an integrated public transport system with environment-friendly feeder mode. The study is in a tier II type metropolitan city of Vadodara, Gujarat. For the purpose a new class of model, Integrated Choice and Latent Variable (ICLV) model is introduced. It integrates the Structural Equation Modelling (SEM) through Analysis of Moment Structures (AMOS) approach for the analysis of latent variable with the classic type choice model analyzed through Biogeme regression. The study precisely captures the attitude of a rider towards the need for comfort and convenience, safety and security, services and facilities, and riding attraction and quality class of latent variables. 26 of initially considered 41 latent variables clubbed into four latent constructs through Confirmatory Factor Analysis (CFA) and SEM approach were incorporated in the ICLV analysis. The results explain the riders’ attitude driven choice for comfort and convenience and service and facilities over the safety and security and riding quality aspects of the mode. The shift demand governed by these latent variables helps to develop a methodological framework, which sets up a base for transport system design oriented policies’ implications as future scope of the research.

Behavioural modelling of automated to manual control transition in conditionally automated driving

Human-vehicle interaction in the presence of automated driving features (ADFs) poses significant challenges in behavioural adaptation. At the current level of automation, the mixed right-of-control of connected and automated vehicle (CAV) with partial human involvement is of interest. This study investigates the factors affecting the successful take over of the control by a human driver in the conditionally automated vehicle upon operational design domain (ODD) exit under different sociodemographic, mental workload, traffic flow, weather, and lighting conditions. Data collected in Virtual and Immersive Reality Environment (VIRE) are used to study the successful taking of control back from automated driving. Apart from estimating the binary and mixed logit models, a latent structure was developed to incorporate the attitudinal indicators in the integrated choice and latent variable (ICLV) model. Results indicate that almost 80 percent of participants were successful in safely regaining control. However, participants with more sensitive attitudes about CAV safety, were more likely to fail. Heavy congestion positively impacts situational awareness in taking back control safely from a CAV. Moreover, multi-tasking based on non-driving related secondary task (NDRT) engagement in a rainy night scenario, mental workload, and reaction time were significantly positive indicators of unsafe control transition. The statistics suggest that the driver's familiarity with the concept of CAVs is not enough for the safe transition of control.

Exploring the Choice of Bicycling and Walking in Rajshahi, Bangladesh: An Application of Integrated Choice and Latent Variable (ICLV) Models

Bangladesh has emphasized active transportation in its transportation policies and has encouraged its population, especially the youth and students, towards bicycling. However, there is a scarcity of studies that have examined the factors important to the choice of active transportation that can be referenced to support the initiative. To address this research gap, in this study, we explore the influence of sociodemographics and latent perceptions of a built environment on the choice to walk and bicycle among students and nonstudents in Rajshahi, Bangladesh. In Rajshahi, we conducted a household survey between July and August, 2017. We used a modeling framework that integrated choice and latent variable (ICLV) models to effectively incorporate the latent perception variables in the choice model, addressing measurement error and endogeneity bias. Our models show that students are influenced by perceptions of safety from crime, while nonstudents are influenced by their perceptions of the walkability of a built environment when choosing a bicycle for commuting trips. For recreational bicycle trips, students are more concerned about the perceptions of road safety, whereas nonstudents are concerned about safety from crime. We find that road safety perception significantly and positively influences walking behavior among nonstudents. Structural equation models of the latent perception variables show that females are more likely to provide lower perceptions of neighborhood walkability, road safety, and safety from crime. Regarding active transportation decisions, overall, we find there is a difference between student and nonstudent groups and also within these groups. The findings of this study can assist in developing a sustainable active transportation system by addressing the needs of different segments of the population. In this study, we also provide recommendations regarding promoting active transportation in Rajshahi.

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.

Long-term impacts of COVID-19 pandemic on travel behaviour

The need to understand the influence of the COVID-19 pandemic on the long-term travel behaviour of people has never been higher as a consequence of the second wave of pandemic. In this context, the current study aims to understand the willingness of people to use sustainable modes of transportation including shared modes of transport, and non-motorized transport, against non– shared modes of transport such as personal 2-wheelers and 4-wheelers in a post-vaccinated scenario. The study further models the willingness to choose public transportation under various COVID-19 preventive measures representing the perception of safety among people. An Integrated Choice and Latent Variable (ICLV) framework a employed in the modelling. The fear of contracting COVID-19 and the belief in remedial measures significantly influenced the mode choice of individuals. This highlighted a significant long-term impact of the pandemic on the travel behaviour of individuals. The study concludes by presenting different strategies that could be adopted to make the existing sustainable modes safer, and hence, more attractive.

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.

Pro-environmental Attitude and Adherence to a Travel Mode in an Integrated Choice and Latent Variable (ICLV) Model: Results from a Revealed Preference Survey

Pro-environmental Attitude and Adherence to a Travel Mode in an Integrated Choice and Latent Variable (ICLV) Model: Results from a Revealed Preference Survey

Estimating mode choice of motorized two-wheeler commuters under the influence of combined travel demand management measures: An ICLV modeling approach

Several studies have reported the need for combining multiple Travel Demand Management (TDM) measures for minimizing the use of private vehicles. However, a detailed empirical analysis is needed to substantiate the theory and understand the factors influencing the resultant mode choice behavior. This paper presents a stated choice experiment study to examine the mode choice behavior under the influence of a combined TDM package of Congestion Pricing (CP) and Public Bike Sharing (PBS). The case study area is the old city of Ahmedabad, which is in the western state of Gujarat, India.A stated choice experiment was conducted, wherein a total of 1,719 data points randomly collected from 573 motorized two-wheeler commuters were used to simultaneously estimate an Integrated Choice and Latent Variable (ICLV) model for work trips in the study area. In addition to the observable variables associated with an individual's socioeconomic and trip characteristics, the model also includes the psychological theory of Value-Attitude-Behavior (VAB) in the choice modeling framework. The results show that there exists a statistically significant influence of values of benevolence and stimulation as well as attitudes towards the flexibility of mode, environment, and health in explaining individual preferences. Furthermore, the modeling results also suggest that the combined TDM measures are likely to significantly influence mode choice decisions as opposed to standalone TDM measures.

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.

Ride acceptance behaviour of ride-sourcing drivers

The performance of ride-sourcing services such as Uber and Lyft is determined by the collective choices of individual drivers who are not only chauffeurs but private fleet providers. In such a context, ride-sourcing drivers are free to decide whether to accept or decline ride requests assigned by the ride-hailing platform. Drivers’ ride acceptance behaviour can significantly influence system performance in terms of riders’ waiting time (associated with the level of service), drivers’ occupation rate and idle time (related to drivers’ income), and platform revenue and reputation. Hence, it is of great importance to identify the underlying determinants of the ride acceptance behaviour of drivers. To this end, we collected a unique dataset from ride-sourcing drivers working in the United States and the Netherlands through a cross-sectional stated preference experiment designed based upon disparate information conveyed to the respondents. Using a choice modelling approach, we estimated the effects of various existing and hypothetical attributes influencing the ride acceptance choice. Employment status, experience level with the platform, and working shift are found to be the key individual-specific determinants. Part-time and beginning drivers who work on midweek days (Monday-Thursday) have a higher tendency to accept ride offers. Results also reveal that pickup time, which is the travel time between the driver’s location and the rider’s waiting spot, has a negative impact on ride acceptance. Moreover, the findings suggest that a guaranteed tip (i.e., the minimum amount of tip that is indicated upfront by the prospective rider, a feature that is currently not available) and an additional income due to surge pricing are valued noticeably higher than trip fare. The provided insights can be used to develop customised matching and pricing strategies to improve system efficiency. Since the study has been conducted during the COVID-19 crisis, the potential implications of the pandemic on ride acceptance behaviour have been examined using an Integrated Choice and Latent Variable (ICLV) model. The results show that drivers with a higher sensitivity to the COVID-19 effects tend to have a lower acceptance rate.