No description available.

Our research proposes a new, multi-attribute, parameterisation of Tversky’s Elimination- By-Aspects (EBA) model. The EBA model conceptualises choice as a covert sequential elimination process with choice probabilities formulated over all consideration sets of the choice set. This specification attempts to capture the effect of context on choice behaviour. However, the EBA model has seen limited usage due to the large number of required parameters given the set of items under study. For a set of items T, it has 2|T| - 3 free parameters, which is infeasible for all but the simplest of contexts. To provide a practical operationalisation, we impose a set of a priori constraints on the parameter space. We define a generic multi-attribute structure to the set of aspects. This restricts the cardinality of the set of unknown scale values while retaining the functional (recursive) form of the model. The EBA hypothesis of a population of lexicographic decision-makers can therefore be tested in more market-realistic contexts, and inferences made over a large universal set of items described by the complete factorial. We call this model the Multi-attribute Elimination-By-Aspects (MEBA) model. The MEBA model reduces the set of unknown free parameters to a maximum of |T|-1. We develop a general algebraic expression for the MEBA choice probabilities as a function of the attributes of the options in the choice set. This enables the derivation of a likelihood function, and consequently maximum likelihood estimation. We also consider the form of optimal MEBA paired comparison designs. Using Monte Carlo simulation and a discrete choice experiment with consumers, we conduct an initial empirical test of the model against the special case of the MNL model (that assumes no context effects) and find the MEBA model to be a better approximation of observed choice behaviour. This is achieved on a common set of parameters, and so it is due solely to the difference in functional form of the two models. We conclude with a discussion on future research directions, in particular the introduction of heterogeneity into the model, and the description of optimal choice experiments for larger choice set sizes.

Three weather sensitive multinomial logit models are estimated using the 2001 Transportation Tomorrow Survey in order explore the relationship between weather and home-based work trips within the City of Toronto, focusing on active modes of transportation. The data is restricted to non-captive commuters who have the option of alternating between all five basic modes of auto driver, auto passenger, transit, bike and walk with change in weather. Daily trip rates in various weather conditions are assessed. The combined effect of the daily trip rate and mode choice analysis is applied to several climate change scenarios. A 6oC increase in temperature can increase cycling trips by 17%, and reduce auto-passenger trips by 7%. A 20% increase or decrease in precipitation, however, is found to have much smaller impacts on all modes. Overall, the results confirm that impact of weather on active modes of transportation is significant enough to deserve attention at the research, data collection and planning levels.

Three weather sensitive multinomial logit models are estimated using the 2001 Transportation Tomorrow Survey in order explore the relationship between weather and home-based work trips within the City of Toronto, focusing on active modes of transportation. The data is restricted to non-captive commuters who have the option of alternating between all five basic modes of auto driver, auto passenger, transit, bike and walk with change in weather. Daily trip rates in various weather conditions are assessed. The combined effect of the daily trip rate and mode choice analysis is applied to several climate change scenarios. A 6oC increase in temperature can increase cycling trips by 17%, and reduce auto-passenger trips by 7%. A 20% increase or decrease in precipitation, however, is found to have much smaller impacts on all modes. Overall, the results confirm that impact of weather on active modes of transportation is significant enough to deserve attention at the research, data collection and planning levels.

Our research proposes a new, multi-attribute, parameterisation of Tversky’s Elimination- By-Aspects (EBA) model. The EBA model conceptualises choice as a covert sequential elimination process with choice probabilities formulated over all consideration sets of the choice set. This specification attempts to capture the effect of context on choice behaviour. However, the EBA model has seen limited usage due to the large number of required parameters given the set of items under study. For a set of items T, it has 2|T| - 3 free parameters, which is infeasible for all but the simplest of contexts. To provide a practical operationalisation, we impose a set of a priori constraints on the parameter space. We define a generic multi-attribute structure to the set of aspects. This restricts the cardinality of the set of unknown scale values while retaining the functional (recursive) form of the model. The EBA hypothesis of a population of lexicographic decision-makers can therefore be tested in more market-realistic contexts, and inferences made over a large universal set of items described by the complete factorial. We call this model the Multi-attribute Elimination-By-Aspects (MEBA) model. The MEBA model reduces the set of unknown free parameters to a maximum of |T|-1. We develop a general algebraic expression for the MEBA choice probabilities as a function of the attributes of the options in the choice set. This enables the derivation of a likelihood function, and consequently maximum likelihood estimation. We also consider the form of optimal MEBA paired comparison designs. Using Monte Carlo simulation and a discrete choice experiment with consumers, we conduct an initial empirical test of the model against the special case of the MNL model (that assumes no context effects) and find the MEBA model to be a better approximation of observed choice behaviour. This is achieved on a common set of parameters, and so it is due solely to the difference in functional form of the two models. We conclude with a discussion on future research directions, in particular the introduction of heterogeneity into the model, and the description of optimal choice experiments for larger choice set sizes.

Our research proposes a new, multi-attribute, parameterisation of Tversky’s Elimination- By-Aspects (EBA) model. The EBA model conceptualises choice as a covert sequential elimination process with choice probabilities formulated over all consideration sets of the choice set. This specification attempts to capture the effect of context on choice behaviour. However, the EBA model has seen limited usage due to the large number of required parameters given the set of items under study. For a set of items T, it has 2|T| - 3 free parameters, which is infeasible for all but the simplest of contexts. To provide a practical operationalisation, we impose a set of a priori constraints on the parameter space. We define a generic multi-attribute structure to the set of aspects. This restricts the cardinality of the set of unknown scale values while retaining the functional (recursive) form of the model. The EBA hypothesis of a population of lexicographic decision-makers can therefore be tested in more market-realistic contexts, and inferences made over a large universal set of items described by the complete factorial. We call this model the Multi-attribute Elimination-By-Aspects (MEBA) model. The MEBA model reduces the set of unknown free parameters to a maximum of |T|-1. We develop a general algebraic expression for the MEBA choice probabilities as a function of the attributes of the options in the choice set. This enables the derivation of a likelihood function, and consequently maximum likelihood estimation. We also consider the form of optimal MEBA paired comparison designs. Using Monte Carlo simulation and a discrete choice experiment with consumers, we conduct an initial empirical test of the model against the special case of the MNL model (that assumes no context effects) and find the MEBA model to be a better approximation of observed choice behaviour. This is achieved on a common set of parameters, and so it is due solely to the difference in functional form of the two models. We conclude with a discussion on future research directions, in particular the introduction of heterogeneity into the model, and the description of optimal choice experiments for larger choice set sizes.

Our research proposes a new, multi-attribute, parameterisation of Tversky’s Elimination- By-Aspects (EBA) model. The EBA model conceptualises choice as a covert sequential elimination process with choice probabilities formulated over all consideration sets of the choice set. This specification attempts to capture the effect of context on choice behaviour. However, the EBA model has seen limited usage due to the large number of required parameters given the set of items under study. For a set of items T, it has 2|T| - 3 free parameters, which is infeasible for all but the simplest of contexts. To provide a practical operationalisation, we impose a set of a priori constraints on the parameter space. We define a generic multi-attribute structure to the set of aspects. This restricts the cardinality of the set of unknown scale values while retaining the functional (recursive) form of the model. The EBA hypothesis of a population of lexicographic decision-makers can therefore be tested in more market-realistic contexts, and inferences made over a large universal set of items described by the complete factorial. We call this model the Multi-attribute Elimination-By-Aspects (MEBA) model. The MEBA model reduces the set of unknown free parameters to a maximum of |T|-1. We develop a general algebraic expression for the MEBA choice probabilities as a function of the attributes of the options in the choice set. This enables the derivation of a likelihood function, and consequently maximum likelihood estimation. We also consider the form of optimal MEBA paired comparison designs. Using Monte Carlo simulation and a discrete choice experiment with consumers, we conduct an initial empirical test of the model against the special case of the MNL model (that assumes no context effects) and find the MEBA model to be a better approximation of observed choice behaviour. This is achieved on a common set of parameters, and so it is due solely to the difference in functional form of the two models. We conclude with a discussion on future research directions, in particular the introduction of heterogeneity into the model, and the description of optimal choice experiments for larger choice set sizes.

The Choice Modelling technique is an emerging technique which appears to have strengths relative to other non-market valuation techniques. The latter group are important in the assessment of the tradeoffs involved in environmental and resource use tradeoffs. However, the use of these techniques is often criticised where ethical and moral frameworks are involved, where it is unclear how difficult choices are being framed against each other, or where it is unclear how values change according to the scope of the amenity under consideration The primary focus of the thesis is on the development of the Choice Modelling technique, which provides a richer information set to researchers than other techniques such as Contingent valuation method. However, the richness of this data is offset to some degree by the complexity of application, where careful attention is needed for design, application and analytical stages. In this thesis, a series of four Choice Modelling experiments are reported, as well as two small Contingent Valuation experiments. The Choice Modelling experiments were centred on the estimation of values that Australians might hold for rainforest conservation in Vanuatu. In order to frame and scope the tradeoffs of interest within the broad pool of substitute goods (such as rainforest conservation opportunities in other countries), the Choice Modelling technique appeared more suitable than the single tradeoff preservation of the Contingent Valuation method. The results of Choice Modelling experiments involved sets of parameters indicating that the probability of choice depended on a number of attributes used to make up the conservation profiles together with the influence of unobserved attributes. As a willingness-to-pay variable was included within the attributes making up choice profiles, estimates of value were available by predicting how changes in particular attributes could be offset by changes in the willingness-to-pay variable. The major conclusions of the research presented in the thesis were that the involvement with ethical and moral frameworks does not invalidate the use of non-market valuation techniques, and that the Choice Modelling technique has strengths in relation to scooping and framing issues. In particular, the research demonstrates that the information generated from an experiment can be used in an iterative approach to develop more accurate models of choice. As well, the research shows that the amenity of interest, rainforest conservation in Vanuatu, can have positive values for Australian residents relative to other conservation opportunities.

As economies develop, novel products are created and markets for these products arise. Genetically modified food (GMF) is an example of such a novel product and provides economists with the opportunity to investigate an infant market. Of particular interest with GMF is the impact of consumer reactions on the market. The response of consumers to GMF and their willingness to pay for it has emerged as an important factor in the development of this technology. This research investigates these consumer responses. Prior research suggests that two aspects of consumer behaviour may be relevant for the GMF market. First, consumers may react differently to different types of GMF, so that some products are potentially more economically viable. Secondly, some consumers appear to prefer not having GMF at all. Consumer behaviour is often framed according to neoclassical economic theory. Consumer preferences over goods and the attributes of those goods are generally held to have certain properties. The aspects of consumers’ reactions to GMF noted above, however, may be in conflict with two properties of preferences in neoclassical theory. First, preferences over food attributes are not separable, but may interact with each other. Secondly, some consumers may have preferences regarding GMF that are not continuous. As a result, aggregate impacts of introducing GMF may be difficult to measure, which raises a third issue for investigation, aggregation. Finally, an alternative model of consumer behaviour is bounded rationality, which theorises that choices may be discontinuous as a result of specific protocols. It also suggests that consumers seek to make good-enough choices, rather than attempting to maximise their satisfaction. Thus, optimisation or maximisation is the fourth issue considered in this thesis. In order to investigate these properties of consumers’ preferences, a choice experiment survey was developed. The strength of a choice experiment for examining these issues is its focus on the impact of each product attribute on a respondent’s choices. Thus, it may be possible to identify potentially discontinuous choice patterns and to identify choices affected by interactions between GM technology and other food attributes. Results from a neoclassical analysis of the survey data suggest that some consumers consider the type of benefit created with GM technology in making their choices. In addition, one-quarter to one-half of respondents may have had discontinuous preferences with respect to GMF. Reactions to GMF appear related to respondents’ attitudes, but not to socio-economic or demographic descriptors. As a result, aggregate measures of the impact of GMF may not fully account for consumers’ responses. A boundedly rational model also has reasonable goodness of fit, and may provide a different perspective on consumer behaviour. It is hoped that the results of this research provide a better understanding of consumer behaviour regarding GMF and, by extension, of the process of consumer adoption of novel products. It is further hoped that this attempt to incorporate choice protocols into discrete choice analysis will provide a useful example for further research.

Redland Shire Council (RSC) has recently completed the preparation of Integrated Local Transport Plan (ILTP) and started its implementation and monitoring program. One of the major thrusts of the ILTP is to reduce the car dependency in the Shire and increase the shares of sustainable environmental-friendly travelling modes, such as walking, cycling and public transport. To achieve these objectives, a mathematical model is needed that is capable of modelling and forecasting the travelling mode choice behaviour in the multi modal environment of Redland Shire. Further, the model can be employed in testing the elasticity of various level-of-service attributes, under a virtual travel environment, as proposed in the ILTP, and estimating the demand for the new travelling alternatives to private car, namely the bus on busway, walking on walkway and cycling on cycleway. The research estimated various nested logit models for different trip lengths and trip purposes, using the data from a stated preference (SP) survey conducted in the Shire. A unique computer assisted personal interviewing (CAPI) instrument was designed, using both the motorised (bus on busway) and non-motorised travelling modes (walking on walkway and cycling on cycleway) in the SP choice set. Additionally, a unique set of access modes for bus on busway was also generated, containing hypothetical modes, such as secure park and ride facilities and kiss and ride drop-off zones at the busway stations, walkway and cycleway facilities to access the busway stations and a frequent and integrated feeder bus network within the Shire. Hence, this study created a totally new virtual travel environment for the population of Redland Shire, in order to record their perceived observations under these scenarios and develop the mode choice models. From the final model estimation results, it was found that the travel behaviour forecasted for regional trip-makers is considerably different from that of local trip-makers. The regional travellers for work, for instance, were found not to perceive the non-motorised modes as valid alternatives to car, possibly due to longer trip lengths. The value of time (VoT) determined for local work trip-makers (16.50 A$/hr) was also found to be higher than that of regional work trip-makers (11.70 A$/hr). From the survey analysis, a big part of the targeted population was found to be car captives, who are not likely to switch from cars to public transport; even if a more efficient transit infrastructure is implemented. In the past, the models have been generally calibrated using the mode choice survey data only, while that of the captive users were ignored. This yields a knowledge gap in capturing the complete travel behaviour of a region, since the question of what particular biases can be involved with each model estimation parameter by the captives remain unresolved. In this research, various statistical analyses were performed on the car captive users' data by categorising them into various trip characteristics and household parameters, in order to infer the relative influence of the car captive population on the travel behaviour of the study area. The outcomes of the research can assist the policy makers in solving the strategic issues of transit planning, including the future development of a busway corridor, with an efficient transit access mode network. The research findings can also be utilised in evaluating the feasibility of developing walkways and cycleways in the Shire, along with appraising the relative influence of car captive users on the travel behaviour forecasts for the study area.