Using semi-open questions to integrate perceptions in choice models

Hybrid Choice Models: Principles and Recent Progress Incorporating Social Influence and Nonlinear Utility Functions

Measurement of non-random attrition effects on mobility rates using trip diaries data

In the past decade, a new trend in discrete choice modeling has emerged: psychological factors are explicitly incorporated to enhance the behavioral representation of the choice process. In this context, hybrid models expand on standard choice models by including attitudes and perceptions as latent variables. The complete model is composed of a group of structural equations describing the latent variables in terms of observable exogenous variables and a group of measurement relationships linking latent variables to certain observable indicators. Although the estimation of hybrid models requires the evaluation of complex multidimensional integrals, simulated maximum likelihood is implemented to solve the integrated multiple-equation model. This study empirically evaluates the application of hybrid choice modeling to data from a survey conducted by the Energy and Materials Research Group (Simon Fraser University, 2002 and 2003) of the virtual personal vehicle choices made by Canadian consumers when they are faced with technological innovations. The survey also includes a complete list of indicators that allows the application of a hybrid choice model formulation. It is concluded that the hybrid choice model is genuinely capable of adapting to practical situations by including latent variables among the set of explanatory variables. The incorporation of perceptions and attitudes in this way leads to more realistic models and gives a better description of the profile of consumers and their adoption of new automobile technologies.

Transport-related problems, such as automobile dependence, traffic congestion, and greenhouse emissions, lead to a great burden on the environment. In developing countries like China, in order to improve the air quality, promoting sustainable travel modes to reduce the automobile usage is gradually recognized as an emerging national concern. Though there are many studies related to the physically active modes (e.g., walking and cycling), the research on the influence of attitudes to active modes on travel behavior is limited, especially in China. To fill up this gap, this paper focuses on examining the impact of attitudes to walking and cycling on commute mode choice. Using the survey data collected in China cities, an integrated discrete choice model and the structural equation model are proposed. By applying the hybrid choice model, not only the role of the latent attitude played in travel mode choice, but also the indirect effects of social factors on travel mode choice are obtained. The comparison indicates that the hybrid choice model outperforms the traditional model. This study is expected to provide a better understanding for urban planners on the influential factors of green travel modes.

As part of efforts to promote sustainable mobility, many cities are currently experiencing the rapid expansion of their metro network. The consequent growth in ridership motivates a broad range of travel demand management (TDM) policies, both in terms of passenger flow control and dynamic pricing strategies. This work aims to reveal the impact of TDM on metro commuters’ behavioural loyalty using stated-preference data collected in Guangzhou, China. Commuters’ behavioural response to TDM strategies is investigated in terms of the possible shift in departure time and travel mode. A hybrid choice model framework is used to incorporate four latent variables of interest, i.e., service quality, overall impression, external attractiveness and switching cost, into the discrete choice model and thereby capture the relationships between the attitudinal factors and observed variables. The model estimation results indicate that the four latent variables all prove useful in interpreting commuters’ behavioural loyalty. Commuters’ perceived service quality and overall impression both show a positive effect on their willingness to continue travelling by metro and are thus instructive for ridership retention. External attractiveness is found to be significant only in the case of the tendency to shift to a private car. Switching costs reveal commuters’ emotional attachment to their already developed commuting habit. These insights into commuters’ behavioural change intention enable metro operators to enhance commuters’ loyalty to their service and develop more effective TDM strategies in future practice.

In the context of future-oriented urban road infrastructure adaptation, it is crucial to understand the factors that influence cyclists’ route choice behavior to promote cycling effectively. However, the increase in bicycle usage can also lead to crowded cycling infrastructure and increased objective safety risks, which has not been thoroughly investigated in previous studies. This study examine how crowding, safety risks, and route attributes influence cyclists’ route choices, differentiating between e-bike and regular bike users. Using stated preference data from 784 cyclists in Nanjing and a hybrid choice model (HCM), the research integrates latent attitudinal variables and socio-demographic factors. Findings show regular cyclists are more sensitive to crowding and accident risk, while e-bike users prioritize dedicated cycling infrastructure over travel time and road characteristics. The results underscore the importance of including attitudinal variables in route choice models and provide valuable guidance to safely manage rising cycling demand and improve navigation systems.

Prelims

This study investigates the current and potential uptake of ridesourcing services, such as Uber and Careem, by the students of the American University of Beirut, Lebanon. A hybrid choice model is developed to predict the switching choice from traditional modes of transport to ridesourcing services for social/recreational trips made by these students in Lebanon. Data are provided by a web-based survey that includes revealed and stated preferences, besides demographics. It is found that the switching choice is determined by several observed factors, such as door-to-door travel time, waiting time for pick-up, and one-way fares, in addition to a latent variable that captures individual differences in perceptions and attitudes towards ridesourcing services. A base switching probability from traditional modes to ridesourcing services (calculated under a base scenario representing realistic values of the attributes of ridesourcing services if the latter were used to make the most recent social/recreational trip) is estimated to be 0.22. This probability is expected to reach 0.31 under a forecasted policy scenario consisting of 40% reduction in ridesourcing fares. Car users will be more sensitive to switch to ridesourcing services for their social/recreational trips if the ridesourcing fare reduction (40%) is associated with restricted parking conditions consisting of (a) 100% increase of parking fees from actual prices, and (b) 20-minute increase of parking search time and parking time from the actual car travel time. In this case, the resulting switching probability is expected to reach 0.38. By using the estimated choice model to forecast policy scenarios as such, this study can guide planners, policymakers, and service operators to prioritize effective policies in response to the behavioral change caused by the diffusion of innovative transport services and technologies. The study also contributes to a better understanding of the uptake of ridesourcing services in developing country contexts where public transport services are often inadequate.

Calibrating the Wildfire Decision Model using hybrid choice modelling

Despite decades of methodological sophistication, research on sustainable food behavior remains critically limited in predicting actual purchases. This study aims to examine how methodological fragmentation across psychometric, econometric, and behavioral approaches affects the predictive validity of sustainable food choice and purchasing behavior. This integrative systematic review of 62 empirical studies across psychometric validation, discrete choice experiments (DCEs), trust and cognitive biases, and objective behavioral measurement diagnoses the structural disarticulation between these traditions as the primary cause of limited predictive validity. Findings reveal a pronounced inversion of the evidence hierarchy: while self-report studies report moderate attitude-behavior correlations (β ≈ 0.40-0.50, self-report), the only long-term study using objective scanner data demonstrates that this relationship collapses to a virtually null effect (β = 0.022), representing a 95.6% decay in predictive capacity. Psychometric instruments demonstrate strong structural validity but lack ecological validation against actual purchases. DCEs have evolved econometrically (from MNL to GMNL models), yet remain isolated from psychological theory and real-world validation. Critically, no reviewed study integrated validated scales, a DCE, and objective behavioral data within a single design. Key moderators-skepticism, halo effects, and affective heuristics-are systematically underoperationalized. To overcome this impasse, we propose Hybrid Choice Models (HCM) as the central tool to formally articulate latent attitudes, stated preferences, and observed behavior, enabling cumulative evidence to inform policy and market strategies with greater predictive accuracy. These findings indicate that predictive advances depend on integrating measurement paradigms to achieve ecologically valid and policy-relevant models of sustainable consumer behavior.

This paper analyzes and synthesizes the fundamentals of discrete choice models. This paper alsodiscusses the basic concept and theory underlying the econometrics of discrete choice, specific choicemodels, estimation method, model building and tests, and applications of discrete choice models. Thiswork highlights the relationship between economic theory and discrete choice models: how economictheory contributes to choice modeling and vice versa.
 Keywords: Discrete choice models; Random utility maximization; Decision makers; Utility function;Model formulation

The formulation of hybrid discrete choice models, including both observable alternative attributes and latent variables associated with attitudes and perceptions, has become a topic of discussion once more. To estimate models integrating both kinds of variables, two methods have been proposed: the sequential approach, in which the latent variables are built before their integration with the traditional explanatory variables in the choice model and the simultaneous approach, in which both processes are done together, albeit with a sophisticated but fairly complex treatment. Here both approaches are applied to estimate hybrid choice models by using two data sets: one from the Santiago Panel (an urban mode choice context with many alternatives) and another consisting of synthetic data. Differences between both approaches were found as well as similarities not found in earlier studies. Even when both approaches result in unbiased estimators, problems arise when valuations are obtained such as the value of time for forecasting and policy evaluation.

Modeling passenger route choices is crucial for analyzing and predicting public transportation demand. One of the most popular methods is to use probabilistic route choice (PRC) models (also known as discrete choice models in general), which have broad applications in transportation, economics, politics, and other fields. However, its performance varies depending on the characteristics of the origin–destination (OD) trip data and should be examined carefully. This paper proposes a framework for validating the PRC model on its application to urban rail transit (URT) networks containing small‐scale OD trip data. The concept of small‐scale data is defined at first for each OD pair considering the desired confidence level and the variance of route choices. Then, a travel time range (TTR)‐based method is put forward to deduce passengers’ actual route choices as a benchmark for verifying PRC models. The difference and regularity analysis between the actual route choices and the model predictions are also performed with a twofold comparison. A case study on the Nanchang metro in China shows that the actual daily passenger volumes on routes of small‐scale OD pairs diverge remarkably from the estimations of the PRC model. The PRC model’s performance is further discussed when the small‐scale OD trip data accumulate to a larger scale over multiple days (e.g., several months). This study reveals the inherent limitation of PRC models in estimating the travel behaviors of passengers in a small‐scale population. Several practical implications are discussed to improve the route choice model and passenger flow analysis.

The COVID-19 emergency forced cities worldwide to adopt measures to restrict travel and implement new urban public transport solutions. The discontinuity and reduction of services made users recognize public transport systems as contamination vectors, and the decrease in the number of passengers can already be seen in several places. Thus, this study assessed the impact of the COVID-19 pandemic on urban public transport. We used hybrid choice models (HCMs) to identify the new barriers and potential solutions to increase users’ perception of safety, considering preexistent perceptions of public transportation quality. We used data from an online survey with users of public transportation in a metropolitan area in southern Brazil. We identified that the main barriers to using public transport during virus transmission are related to the system characteristics that force constant interaction with other passengers. Crowded vehicles and crowded stops/stations were considered the most detrimental factor in feeling safe while riding in the COVID-19 outbreak. Countermeasures that reduce the contact with other passengers—directly (limit the number of passengers in vehicles) or indirectly (operate with large vehicles)—and increase offers are possible solutions to make users feel safe while riding. The results of this research might help reduce passenger evasion and migration to more unsustainable transport modes.

On substitutability and complementarity in discrete choice models