Specification Of Choice Models Research Articles

Household preferences for energy saving measures: Approach of discrete choice models

The aim of this paper is crystallized around the research of the energy saving measures adoption's determinants in the residential sector. Using data from the latest survey of the Tunisian Company of Electricity and Gas, we study the specifications of discrete choice models to the characteristics of three different electrical purposes: the Solar Water Heaters, the Low-Energy Bulbs and the classes of energy efficient refrigerators. The results suggest the strong heterogeneity of households, making the effect of energy policies a bit obvious. However, by adopting a perfect rationality, these households give importance to the enrichment of electricity prices and gains in energy savings, as far as the importance of the changes in equipments’ price. Subsequently, we urge policy makers to create mechanisms of balancing the grants of conventional energies by increasing the grants on energy efficient equipments’ purchasing prices.

Combining RP and SP data while accounting for large choice sets and travel mode – an application to forest recreation

This paper analyses the use of forests for recreational purposes in Lorraine, France, a region with many forests and easy access for recreational users. This implies that residents in Lorraine can choose between a large set of forests if they decide to visit a forest. The abundance of forests in Lorraine makes identification of the visited forests difficult. To facilitate identification of forests actually visited, we have incorporated an interactive map in a Web-based survey intended to include both revealed and stated preference data. We compare different sampling schemes to define the choice set used for site selection modelling when the actual choice set considered is potentially large and unknown to the analyst. Easy access to forests also implies that around half of the visitors walk or bike to the forest. We apply an error-component mixed-logit model to simultaneously model the travel mode decision and the site selection decision and to combine revealed and stated preference data. Finally, the effect on the willingness-to-pay of changes in forest quality and access is evaluated based on alternative choice set specifications, model specifications and data sources (revealed and stated preference data).

Sensitivity of Vehicle Market Share Predictions to Discrete Choice Model Specification

When design decisions are informed by consumer choice models, uncertainty in choice model predictions creates uncertainty for the designer. We investigate the variation and accuracy of market share predictions by characterizing fit and forecast accuracy of discrete choice models for the US light duty new vehicle market. Specifically, we estimate multinomial logit models for 9000 utility functions representative of a large literature in vehicle choice modeling using sales data for years 2004–2006. Each model predicts shares for the 2007 and 2010 markets, and we compare several quantitative measures of model fit and predictive accuracy. We find that (1) our accuracy measures are concordant: model specifications that perform well on one measure tend to also perform well on other measures for both fit and prediction. (2) Even the best discrete choice models exhibit substantial prediction error, stemming largely from limited model fit due to unobserved attributes. A naïve “static” model, assuming share for each vehicle design in the forecast year = share in the last available year, outperforms all 9000 attribute-based models when predicting the full market one year forward, but attribute-based models can predict better for four year forward forecasts or new vehicle designs. (3) Share predictions are sensitive to the presence of utility covariates but less sensitive to covariate form (e.g., miles per gallons versus gallons per mile), and nested and mixed logit specifications do not produce significantly more accurate forecasts. This suggests ambiguity in identifying a unique model form best for design. Furthermore, the models with best predictions do not necessarily have expected coefficient signs, and biased coefficients could misguide design efforts even when overall prediction accuracy for existing markets is maximized.

Revealing the Value of “Green” and the Small Group with a Big Heart in Transportation Mode Choice

To address issues of climate change, people are more and more being presented with the greenhouse gas emissions associated with their alternatives. Statements of pounds or kilograms of CO2 are showing up in trip planners, car advertisements, and even restaurant menus under the assumption that this information influences behavior. This research contributes to the literature that investigates how travelers respond to such information. Our objective is to better understand the “value of green” or how much travelers are willing to pay in money in order to reduce the CO2 associated with their travel. As with previous work, we designed and conducted a mode choice experiment using methods that have long been used to study value of time. The contributions of this paper are twofold. First, we employ revealed preference data, whereas previous studies have been based on stated preferences. Second, we provide new insight on how the value of green is distributed in the population. Whereas previous work has specified heterogeneity either systematically or with a continuous distribution, we find that a latent class choice model specification better fits the data and also is attractive behaviorally. The best fitting latent class model has two classes: one large class (76% of the sample) who are not willing to spend any time or money to reduce their CO2 and a second class (24% of the sample) who value reducing their CO2 at a very high rate of $2.68 per pound of reduction—our so-called small group with a big heart. We reanalyzed three datasets that we had previously collected and found considerable robustness of this two class result.

Use of Private Probe Data in Route Choice Analysis to Explore Heterogeneity in Drivers' Familiarity with Origin–Destination Pairs

An exploratory analysis about the effect on route choice of heterogeneity in driver familiarity with origin–destination (O-D) pairs was carried out. This analysis was based on probe data collected by private vehicles in Toyota, Japan. The hypothesis test results showed that route choice behavior changed in relation to the level of familiarity with O-D pairs. Two specifications of choice models were proposed to consider the effect of familiarity. The estimation results showed that the models that considered familiarity fit the data better and suggested that trips between more familiar O-D pairs had larger error variances and less sensitivity to explanatory variables. The estimated models were applied to a specific choice situation, and the prediction results showed the potential biases introduced by not considering the heterogeneity in the familiarity with O-D pairs. In addition, when traveling between more familiar O-D pairs, drivers were less sensitive to the intersection count than to the free travel time.

Heterogeneous consumer preferences for alternative fuel cars in China

This paper models consumer preferences for alternative fuel cars and conventional, petrol fueled cars in China. It compares discrete choice model specifications where each specification reflects how consumers choose between alternative fuel cars and conventional types of cars. We find that, in China, consumers differentiate between the types of alternative fuel cars and are more likely to consider switching from petrol fuel vehicles to hybrid than to electric cars. Segmentation analysis is used to explore groups of consumers and their likelihood of adopting alternative fuel cars.

Influence of Race on Household Residential Utility

Residential location choice models are an important tool employed by urban geographers, planners, and transportation engineers for understanding household residential location behavior and for predicting future residential location activity. Racial segregation and residential racial preferences have been studied extensively using a variety of analysis techniques in social science research, but racial preferences have generally not been adequately incorporated into residential location choice models. This research develops residential location choice model specifications with a variety of alternative methods of addressing racial preferences in residential location decisions. The research tests whether social class, family structure, and in-group racial preferences are sufficient to explain household sensitivity to neighborhood racial composition. The importance of the interaction between the proportion of in-group race neighbors and other-race neighbors is also evaluated. Models for the San Francisco Bay metropolitan area are estimated and evidence of significant avoidance behavior by households of all races is found. The results suggest that social class differences, family structure differences, and in-group racial preferences alone are not sufficient to explain household residential racial preference and that households of all races practice racial avoidance behavior. Particularly pronounced avoidance of black neighbors by Asian households, Hispanic neighbors by black households, and Asian neighbors by white households are found. Evidence of a decrease in household racial avoidance intensity in neighborhoods with large numbers of own-race neighbors is also found.

Aggregation of common-metric attributes in preference revelation and implications for willingness to pay

There is a growing interest in process heterogeneity in the way that individuals evaluate packages of attributes in real or hypothetical markets and make choices. We consider the role of the relative magnitude of pairs of attributes that are defined on a common metric (e.g., minutes or dollars), to look at the extent to which attributes might be added in preference revelation, in contrast to the commonly adopted single rule of compensatory behaviour. The focus is on a choice model specification that allows for different treatments of pairs of attributes across a sample, in contrast to studies that impose a single rule on all observations, and that does not require supplementary information on whether specific individuals claimed to have added up attributes; rather we structure a non-linear utility function that permits a probabilistic aggregation of each attribute. We translate this into a willingness to pay for travel time-savings for car commuters, in the context of tolling roads in Sydney, and contrast it with the results from the additive model, and a model where self-stated attribute processing information is taken into account. The empirical evidence suggests that mean willingness to pay increases when the addition rule is accounted for. This is a potentially important message for environmental applications where two or more attributes have a common metric.

Behavioral frontiers in choice modeling

We review the discussion at a workshop whose goal was to achieve a better integration among behavioral, economic, and statistical approaches to choice modeling. The workshop explored how current approaches to the specification, estimation, and application of choice models might be improved to better capture the diversity of processes that are postulated to explain how consumers make choices. Some specific challenges include how to capture and parsimoniously describe heterogeneous mixes of heuristic choice rules, methods for building realistic models of choice, and nontraditional methods for estimating models. An agenda for important future work in these areas is also proposed.

Dissecting the Random Component of Utility

We illustrate and discuss several general issues associated with the random component of utility, or more generally “unobserved variability”. We posit a general conceptual framework that suggests a variance components view as an appropriate structure for unobserved variability. This framework suggests that “unobserved heterogeneity” is only one component of unobserved variability; hence, a more general view is required. We review a considerable amount of empirical research that suggests that random components are unlikely to be independent of systematic components, and random component variances are unlikely to be constant between or within individuals, time periods, locations, etc. We also review evidence that random components are functions of (elements of) systematic components. The latter suggests considerable caution in the use and interpretation of complex choice model specifications, in particular recently introduced forms of random parameter models that purport to estimate distributions of preference parameters. Several areas for future research are identified and discussed.

THE SCHEDULE-BASED DYNAMIC MODELLING FOR PUBLIC TRANSPORT NETWORKS: A NEW APPROACH IN PATH CHOICE AND ASSIGNMENT

This paper presents a general overview of the schedule-based approach in dynamic path choice and assignment models for transit nehvorks. The first part deseribes different specification of path choice models in relation to different user and seMce characteristics. The latter deals with a classification of assignment models.

The Influence of Task Complexity on Consumer Choice: A Latent Class Model of Decision Strategy Switching

The literature indicating that person‐, context‐, and task‐specific factors cause consumers to utilize different decision strategies has generally failed to affect the specification of choice models used by practitioners and academics alike, who still tend to assume an utility maximizing, omniscient, indefatigable consumer. This article (1) introduces decision strategy selection, within a maintained compensatory framework, into aggregate choice models via latent classes, which arise because of task complexity; (2) it demonstrates that within an experimental choice task, the model reflects changing aggregate preferences as choice complexity changes and as the task progresses. The import of these findings for current practice, model interpretation, and future research needs is examined.

The valuation of commuter travel time savings for car drivers: evaluating alternative model specifications

The empirical valuation of travel time savings is a derivative of the ratio of parameter estimates in a discrete choice model. The most common formulation (multinomial logit) imposes strong restrictions on the profile of the unobserved influences on choice as represented by the random component of a preference function. As we progress our ability to relax these restrictions we open up opportunities to benchmark the values derived from simple (albeit relatively restrictive) models. In this paper we contrast the values of travel time savings derived from multinomial logit and alternative specifications of mixed (or random parameter) logit models. The empirical setting is urban car commuting in six locations in New Zealand. The evidence suggests that less restrictive choice model specifications tend to produce higher estimates of values of time savings compared to the multinomial logit model; however the degree of under-estimation of multinomial logit remains quite variable, depending on the context.

The Multinomial Probit Model Revisited: A Discussion of Parameter Estimability, Identification and Specification Testing

In this paper we examine the multinomial probit model in the light of recent developments in the field of simulation-based inference. We focus upon five broad areas: specification of multinomial choice models; parameter estimability and the use of simulation techniques, parameter identification; specification testing; and practical issues in simulation-based inference. Although the substitution of simulated probabilities for difficult to compute multidimensional integrals represents a significant step, by examining the more tenuous task of identification and in particular the identification of covariance parameters, we show how the specification and estimation of the multinomial probit still represents a formidable task.

A disaggregate Box-Cox Logit mode choice model of intercity passenger travel in Germany and its implications for high-speed rail demand forecasts

We show that enriching logit mode choice model specification by mode attributes, socio economic variables and trip purpose characteristics significantly improves model quality, and that Box-Cox transformations applied to model attributes imply an asymmetry of the reaction curve, as well as more reasonable properties (diminishing marginal values of time savings, elasticities and values of time that differ among the modes) than those of the linear logit model. Moreover, the model yielded very different high speed rail market shares for Germany than results obtained with the usual linear utility functions.

Conjoint models of tourist portfolio choice: Theory and illustration

This article develops and tests a conjoint choice model of portfolio choice behavior. It shows how experimental design principles and discrete choice model specifications can be used to model the choice of travel portfolios. Design strategies, model formulations, and estimation methods are discussed and illustrated through use of Dutch tourists’ choices of destination and transportation for short city breaks as an example. Findings support the suggested methodology and indicate that the nested logit model is most appropriate in the tested application.

A transactions choice model for forecasting demand for alternative-fuel vehicles

The vehicle choice model developed here is one component in a micro-simulation demand forecasting system being designed to produce annual forecasts of new and used vehicle demand by vehicle type and geographic area in California. The system will also forecast annual vehicle miles traveled for all vehicles and recharging demand by time of day for electric vehicles. The choice model specification differs from past studies by directly modeling vehicle transactions rather than vehicle holdings. The model is calibrated using stated preference data from a new study of 4,747 urban California households. These results are potentially useful to public transportation and energy agencies in their evaluation of alternatives to current gasoline-powered vehicles. The findings are also useful to manufacturers faced with designing and marketing alternative-fuel vehicles as well as to utility companies who need to develop long-run demand-side management planning strategies.

A comparative analysis of different specifications of modal choice models in an urban area

This paper reports a comparative analysis of different models proposed in the literature for the simulation of modal choice in an urban area. Three popular models: Multinomial Logit, Multinomial Probit and Box-Cox Logit are specified, calibrated and validated, with reference to a real town. A new model is also tested, the Multinomial Probit Logit model. The results obtained with different specifications are reported. The characteristics of the models are compared and examined.

On discrete choice models of housing demand

This paper explores several specifications of discrete choice models in estimating housing consumption for purposes of projection and policy simulation. We view housing consumption comprehensively as it includes the choice between three types of homeownership, five types of rented housing, and, as a ninth alternative, not heading a household. The familiar multinomial logit specification is rejected by several specification tests. The paper therefore studies a variety of nested multinomial logit models of hierarchical choice that allow a richer substitution pattern among the very disparate housing choices enumerated above. Estimations of different nesting structures reveal strong and plausible differences among nesting structures.

Effects of the Level-of-Service Measured by the Public Transport Network on the Specification of Disaggregate Mode Choice Models

The paper is concerned with the aggregation bias of the LOS provided by network measurements and its effects on the specification of disaggrerate modal choice models. Five LOS calculated by different minimum indicies including travel time, cost, transfer times, and generalised cost in public transport network are compared with the individual LOS given by attitude survey. The LOS coded by each minimum index is significantly different and the coded measurements by the minimum travel time with transfer penalty are superior to other ones in terms of estimation accuracy. The difference of LOS affects the specifi-cation of BL models less significantly, but the aggregation bias of LOS has an important effect on the model accuracy as compared with the ones specified by individual perceived LOS.