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

Mode choice behaviour is often modelled by discrete choice models, in which the utility of each mode is characterized by mode-specific parameters reflecting how strongly the utility of that mode depends on attributes such as travel speed and cost, and a mode-specific constant value. For new modes, the mode-specific parameters and the constant in the utility function of discrete choice models are not known and are difficult to estimate on the basis of stated preferences data/choice experiments and cannot be estimated on the basis of revealed preference data. This paper demonstrates how revealed preference data can be used to estimate a discrete mode choice model without using mode-specific constants and mode-specific parameters. This establishes a method that can be used to analyze any new mode using revealed preference data and discrete choice models and is demonstrated using the OViN 2017 dataset with trips throughout the Netherlands using a multinomial and nested logit model. This results in a utility function without any alternative specific constants or parameters, with a rho-squared of 0.828 and an accuracy of 0.758. The parameters from this model are used to calculate the future modal split of shared autonomous vehicles and electric steps, leading to a potential modal split range of 24–30% and 37–44% when using a multinomial logit model, and 15–20% and 33–40% when using a nested logit model. An overestimation of the future modal split occurs due to the partial similarities between different transport modes when using a multinomial logit model. It can therefore be concluded that a nested logit model is better suited for estimating the potential modal split of a future mode than a multinomial logit model. To the authors’ knowledge, this is the first time that the future modal split of shared autonomous vehicles and electric steps has been calculated using revealed preference data from existing modes using an unlabelled mode modelling approach.

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.

Customer preference modelling has been widely used to aid engineering design decisions on the selection and configuration of design attributes. Recently, network analysis approaches, such as the exponential random graph model (ERGM), have been increasingly used in this field. While the ERGM-based approach has the new capability of modelling the effects of interactions and interdependencies (e.g., social relationships among customers) on customers’ decisions via network structures (e.g., using triangles to model peer influence), existing research can only model customers’ consideration decisions, and it cannot predict individual customer’s choices, as what the traditional utility-based discrete choice models (DCMs) do. However, the ability to make choice predictions is essential to predicting market demand, which forms the basis of decision-based design (DBD). This paper fills this gap by developing a novel ERGM-based approach for choice prediction. This is the first time that a network-based model can explicitly compute the probability of an alternative being chosen from a choice set. Using a large-scale customer-revealed choice database, this research studies the customer preferences estimated from the ERGM-based choice models with and without network structures and evaluates their predictive performance of market demand, benchmarking the multinomial logit (MNL) model, a traditional DCM. The results show that the proposed ERGM-based choice modelling achieves higher accuracy in predicting both individual choice behaviours and market share ranking than the MNL model, which is mathematically equivalent to ERGM when no network structures are included. The insights obtained from this study further extend the DBD framework by allowing explicit modelling of interactions among entities (i.e., customers and products) using network representations.

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

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.

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.

Discrete choice models are a group of models that are used to analyze choice data basically because they accommodate the nature of the process that generates the data. The most common types of discrete models include the logit, probit, multinomial logit, nested logit, mixed logit and most recently the generalized multinomial logit. Discrete choice models have been mostly used in the area economics, transportation, energy, psychology, etc. Prediction in these models isn\\'t uncommon, in contexts such as engineering, marketing, and production, discrete choice models are mostly used to forecast demand. Unfortunately, for out-of-sample prediction at the individual level for complex models such as mixed logit, which involves predicting the random effects/parameters, there isn\\'t any work found in literature. Thus, in this is work we propose a method for this scenario in mixed logit discrete models using the nearest neighbour concept. We carry out various simulations and then apply on two types of real-life data. We find that the prediction accuracy of this new method is better than the rudimentary method of using the population parameters especially when the model fitted isn\\'t the very best.

Choices made by individuals have widespread impacts—for instance, people choose between political candidates to vote for, between social media posts to share, and between brands to purchase—moreover, data on these choices are increasingly abundant. Discrete choice models are a key tool for learning individual preferences from such data. Additionally, social factors like conformity and contagion influence individual choice. Traditional methods for incorporating these factors into choice models do not account for the entire social network and require hand-crafted features. To overcome these limitations, we use graph learning to study choice in networked contexts. We identify three ways in which graph learning techniques can be used for discrete choice: learning chooser representations, regularizing choice model parameters, and directly constructing predictions from a network. We design methods in each category and test them on real-world choice datasets, including county-level 2016 US election results and Android app installation and usage data. We show that incorporating social network structure can improve the predictions of the standard econometric choice model, the multinomial logit. We provide evidence that app installations are influenced by social context, but we find no such effect on app usage among the same participants, which instead is habit-driven. In the election data, we highlight the additional insights a discrete choice framework provides over classification or regression, the typical approaches. On synthetic data, we demonstrate the sample complexity benefit of using social information in choice models.

Using semi-open questions to integrate perceptions in choice models

The study addressed the increasing complexity faced by students when selecting a university major at the Universidad de las Fuerzas Armadas – ESPE, considering the expansion of academic offerings and the changing demands of the labor market. Its objective was to identify the key determinants guiding this decision through the application of discrete choice models. The Multinomial Logit and Hierarchical Logit models were employed to examine the influence of personal, institutional, and expectation-related variables, organizing program alternatives into disciplinary categories to capture preference heterogeneity. The results showed that personal factors mainly vocation, individual interests, and long-term professional aspirations exerted the strongest influence on major selection, while institutional and economic considerations played a secondary role. The Hierarchical Logit model exhibited a superior fit compared with the Multinomial Logit model, indicating that disciplinary nesting allowed a more accurate representation of decision patterns. Additionally, expected employability and opportunities for career advancement emerged as critical motivations driving students’ choices. In conclusion, the study demonstrated that discrete choice models provided a robust methodological framework to explain educational decision-making processes, offering valuable evidence to strengthen vocational guidance, align academic programs with market demands, and support institutional planning based on empirical insights.

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.

Discrete choice models and the housing price and travel to work elasticities of location demand

In recent years, planners have started considering Machine Learning (ML) techniques as an alternative to discrete choice models (CM). ML techniques are primarily data-driven and typically achieve better prediction accuracy compared to CM. However, it is hypothesized that since the ML techniques do not have the strong grounding to economic theory as the CMs, they may not perform well in contexts that are radically different from the ‘training’ scenario. It is also hypothesized that the relative prediction performance may be affected by the metrics used for comparing the models. This research aims to test these two hypotheses empirically by modelling vehicle ownership choices using household survey data from Dhaka, Bangladesh collected in 2004, 2010 and 2019. The performances of CM (multinomial logit) and ML techniques (neural networks and gradient boosting trees) have been compared using log-likelihood and mean absolute percentage error of market shares. The results indicate that the multinomial logit model (MNL) with a piecewise linear transformation of the household income, has the best performance in terms of log-likelihood and mean absolute percentage error of market shares. This is followed by Neural Networks (NN) and Gradient Boosting Trees (GBT). The results thus provide empirical evidence that the ML techniques do not consistently outperform CM. Moreover, the difference in the performance of the models further increases if the prediction scenario is substantially different. This reinforces the hypothesis that CMs, with their behavioural underpinning, are better suited for long-term forecasting than data-driven ML approaches, especially if the population and network attributes are expected to change substantially. These findings will be useful for planners and policy makers in the selection of the appropriate tool for forecasting travel demand.

The transport sector has been hit hard by the COVID-19 pandemic disrupting travel behaviors and mobility patterns around the globe. The pandemic has also affected mode choice behavior. This research study modeled the mode choice behavior before and during the COVID-19 pandemic in Pakistan. Data was collected through an online questionnaire survey consisting of questions about socio-economic characteristics, factors affecting mode choice, and mode chosen for shorter as well as longer distances for both before and during COVID-19 pandemic situations. The results indicated that public transport use declined, whereas walking and bicycling slightly increased during the pandemic. The respondents placed more priority on safety and security, comfort, cleanliness, infection concerns, personal social status, availability of hand-sanitizers, waiting, and paying more for less congested vehicles during the pandemic. Factor analysis was performed to explore the underlying factors affecting mode choice before and during the pandemic. Discrete choice models were developed to model the mode choice behavior. Monthly household income and pandemic-related underlying factor were significant predictors of mode choice for shorter distances (i.e., < 5 km) during the pandemic. Whereas, gender, car ownership and monthly household income were significant predictors of mode choice for longer distances (i.e., > 5 km) during the pandemic. Understanding the modal shift during a pandemic will surely help urban and transport planners to prepare better for effective transport management in the future. Policy implications are also presented to help policymakers in developing policies for post-pandemic mobility needs, particularly in developing countries.