Dynamic Programming Approaches for Estimating and Applying Large-scale Discrete Choice Models

People go through their life making all kinds of decisions, and some of these decisions affect their demand for transportation, for example, their choices of where to live and where to work, how and when to travel and which route to take. Transport related choices are typically time dependent and characterized by large number of alternatives that can be spatially correlated. This thesis deals with models that can be used to analyze and predict discrete choices in large-scale networks. The proposed models and methods are highly relevant for, but not limited to, transport applications. We model decisions as sequences of choices within the dynamic discrete choice framework, also known as parametric Markov decision processes. Such models are known to be difficult to estimate and to apply to make predictions because dynamic programming problems need to be solved in order to compute choice probabilities. In this thesis we show that it is possible to explore the network structure and the flexibility of dynamic programming so that the dynamic discrete choice modeling approach is not only useful to model time dependent choices, but also makes it easier to model large-scale static choices. The thesis consists of seven articles containing a number of models and methods for estimating, applying and testing large-scale discrete choice models. In the following we group the contributions under three themes: route choice modeling, large-scale multivariate extreme value (MEV) model estimation and nonlinear optimization algorithms. Five articles are related to route choice modeling. We propose different dynamic discrete choice models that allow paths to be correlated based on the MEV and mixed logit models. The resulting route choice models become expensive to estimate and we deal with this challenge by proposing innovative methods that allow to reduce the estimation cost. For example, we propose a decomposition method that not only opens up for possibility of mixing, but also speeds up the estimation for simple logit models, which has implications also for traffic simulation. Moreover, we compare the utility maximization and regret minimization decision rules, and we propose a misspecification test for logit-based route choice models. The second theme is related to the estimation of static discrete choice models with large choice sets. We establish that a class of MEV models can be reformulated as dynamic discrete choice models on the networks of correlation structures. These dynamic models can then be estimated quickly using dynamic programming techniques and an efficient nonlinear optimization algorithm. Finally, the third theme focuses on structured quasi-Newton techniques for estimating discrete choice models by maximum likelihood. We examine and adapt switching methods that can be easily integrated into usual optimization algorithms (line search and trust region) to accelerate the estimation process. The proposed dynamic discrete choice models and estimation methods can be used in various discrete choice applications. In the area of big data analytics, models that can deal with large choice sets and sequential choices are important. Our research can therefore be of interest in various demand analysis applications (predictive analytics) or can be integrated with optimization models (prescriptive analytics). Furthermore, our studies indicate the potential of dynamic programming techniques in this context, even for static models, which opens up a variety of future research directions.

Street characteristics preferred for transportation walking among older adults: a choice-based conjoint analysis with manipulated photographs

BACKGROUND: Knowledge about the relationships between micro-scale environmental factors and older adults' walking for transport is limited and inconsistent. This is probably due to methodological limitations, such as absence of an accurate neighborhood definition, lack of environmental heterogeneity, environmental co-variation, and recall bias. Furthermore, most previous studies are observational in nature. We aimed to address these limitations by investigating the effects of manipulating photographs on micro-scale environmental factors on the appeal of a street for older adults' transportation walking. Secondly, we used latent class analysis to examine whether subgroups could be identified that have different environmental preferences for transportation walking. Thirdly, we investigated whether these subgroups differed in socio-demographic, functional and psychosocial characteristics, current level of walking and environmental perceptions of their own street.

METHODS: Data were collected among 1131 Flemish older adults through an online (n = 940) or an interview version of the questionnaire (n = 191). This questionnaire included a choice-based conjoint exercise with manipulated photographs of a street. These manipulated photographs originated from one panoramic photograph of an existing street that was manipulated on nine environmental attributes. Participants chose which of two presented streets they would prefer to walk for transport.

RESULTS: In the total sample, sidewalk evenness had by far the greatest appeal for transportation walking. The other environmental attributes were less important. Four subgroups that differed in their environmental preferences for transportation walking were identified. In the two largest subgroups (representing 86% of the sample) sidewalk evenness was the most important environmental attribute. In the two smaller subgroups (each comprising 7% of the sample), traffic volume and speed limit were the most important environmental attributes for one, and the presence of vegetation and a bench were the most important environmental attributes for the other. This latter subgroup included a higher percentage of service flat residents than the other subgroups.

CONCLUSIONS: Our results suggest that the provision of even sidewalks should be considered a priority when developing environmental interventions aiming to stimulate older adults' transportation walking. Natural experiments are needed to confirm whether our findings can be translated to real environments and actual transportation walking behavior.

Neural choice by elimination via highway networks

We introduce Neural Choice by Elimination, a new framework that integrates deep neural networks into probabilistic sequential choice models for learning to rank. Given a set of items to chose from, the elimination strategy starts with the whole item set and iteratively eliminates the least worthy item in the remaining subset. We prove that the choice by elimination is equivalent to marginalizing out the random Gompertz latent utilities. Coupled with the choice model is the recently introduced Neural Highway Networks for approximating arbitrarily complex rank functions. We evaluate the proposed framework on a large-scale public dataset with over 425K items, drawn from the Yahoo! learning to rank challenge. It is demonstrated that the proposed method is competitive against state-of-the-art learning to rank methods.

Male courtship decisions are influenced by light environment and female receptivity.

The appearance of animal colour signals depends jointly upon the ambient light spectrum and the signal's reflectance spectra. Light environment heterogeneity might, therefore, allow individuals to enhance their signal by signalling in an environment that increases signal efficacy. We tested this hypothesis by providing male guppies (Poecilia reticulata), a choice of three light environments in which to display their colour signal to females: green, lilac, and clear. We paired males with both receptive and non-receptive females to test whether female response might affect male behavioural decisions. Males preferred the clear environment in all trials and this environment also resulted in males having the highest average visual contrast. Sexual behaviour was influenced by complex interactions between female receptivity, light environment, and male colour pattern contrast. Males spent significantly more time in the environment in which their colour signal had the highest contrast, but only when paired with receptive females. Significant interactions between light environment and individual male colour components were also seen only in receptive trials. Our results suggest that males use light environment to enhance their colour pattern, but only in the presence of receptive females.

Condensed phase combustion travelling waves with sequential exothermic or endothermic reactions

The one-dimensional propagation of a combustion wave through a premixed solid fuel for two-stage kinetics is studied. We re-examine the analysis of a single reaction travelling-wave and extend it to the case of two-stage reactions. We derive an expression for the travelling wave speed in the limit of large activation energy for both reactions. The analysis shows that when both reactions are exothermic, the wave structure is similar to the single reaction case. However, when the second reaction is endothermic, the wave structure can be significantly different from single reaction case. In particular, as might be expected, a travelling wave does not necessarily exist in this case. We establish conditions in the limiting large activation energy limit for the non-existence, and for monotonicity of the temperature profile in the travelling wave.

Gendered perceptions of experiential value in using web-based retail channels

Purpose – This paper compares the experiential consumption values that motivate consumer choice to purchase online for both male and female purchasers and non-purchasers. Design/methodology/approach – Using the theory of consumption value the study examines gendered perceptions of the functional, social and conditional value of using a virtual consumption setting for purchasing. Data was collected through an online survey and analysed using multiple discriminant analysis to determine meaningful differences between male and female purchasers and non-purchasers. Findings – The findings show that male online purchasers are discriminated from female purchasers by social value and from male non-purchasers by conditional value. Female purchasers are discriminated from male purchasers by functional value and from female non-purchasers by social value. Female non-purchasers are discriminated from female purchasers by conditional value. Male non-purchasers are discriminated from male purchasers by functional and social value. Research limitations/implications – Limitations include using an Internet survey and an Australian sample which may impact the generalisability of the findings to a wider population of Internet users. Future research should involve replication of the study in a country more or less developed in terms of gender composition of internet users to extend the generalisability of the findings. Additionally, researchers should examine whether other dimensions of consumption value,such as social influence through on- and off-line communication networks, may influence consumer choice to purchase online. Practical implications – The study provides practical implications for marketers to leverage consumption values that influence male and female consumers’ choice to purchase online and then drive their behaviour online through integrated marketing campaigns that involve both on- and offline strategies. Originality/value – The research makes an original contribution to the consumer behaviour literature as to date, no research has been found that undertakes such a comprehensive gender-based comparison of the perceived value of using a virtual consumption setting for purchasing.