The High Occupancy Vehicle (HOV) lanes on Atlanta, Georgia’s radial I-85 had long been providing sub-optimal throughput in the peak traffic hours, as the two-person occupancy requirement allowed the lanes to become heavily congested. The Georgia Department of Transportation converted 15.5 miles of HOV 2+ lanes to High Occupancy Toll (HOT) lanes, one in each direction on I-85. The lanes use dynamic value pricing to set toll levels based on the volume and average speed of traffic in the lanes. The goal of this research was to investigate the responses to toll lane pricing and the factors that appear to inform lane choice decisions, as well as examining values of travel time savings and toll price elasticity for users of the Express Lanes. This study of the metropolitan Atlanta I-85 Express Lanes operates at the microscopic level to examine the impact of demographic characteristics, congestion levels, and pricing on users’ decisions to use or not use the I-85 Express Lanes. The dissertation examined the value of travel time savings distributions across income segments. The differences in these distributions among lower, medium, and higher income households were marginal at best. The results did not indicate that higher income households had the highest value of travel time savings results, as may have been expected. The modeling work performed here provided a number of insights into toll lane use. The determinants of lane choice decision-making in the morning peak had notable differences from the determinants of the afternoon peak. The initial analysis involved models which were estimated across three different income segments to examine differences in decision making between low, medium, and higher income households. The results indicated that the parameters were largely consistent across the three segments. Further segmenting the households showed that lane choice determinants varied more within the ‘Higher’ income segment than across the original three-segment structure. In particular, the five-segment models illustrated lower elasticities with regard to corridor segment counts and toll levels for the highest-income households in the sample, as well as higher household income level elasticities for afternoon trips by that same cohort. The research was among the first in the available literature to use revealed preference lane use data for both the toll lane users and the unpriced general purpose lane users. The use of household level marketing data, rather than census or survey data, was another unique characteristic of this research. The analysis of value of travel time savings with a demographic component that looks at household income has not yet been seen in the literature; similarly, the findings regarding differing behavior among very high income households appear to be unseen in the existing literature. The results from this analysis, such as willingness-to-pay values for different population segments, will be useful inputs to the decisions surrounding future HOT implementations in the Atlanta region. The use of new data sources, the evaluation of those types of data sources, and the application of methods that have previously been unused in this field make up the primary contributions of this dissertation.

The thesis investigates the development of short-sea shipping (SSS) in the Southern African Development Community (SADC) region by studying the determinants of SSS, the stated choice preference of shippers and freight forwarders and the stated intentions of maritime carriers for SSS. It is purported the introduction of SSS in SADC could reduce socio-environmental problems currently faced such as road damage, road congestion, pollution and transport related accidents. Discrete choice modeling (DCM) is employed as the main methodology to study shipper and carrier behavior. Discrete choice modeling permits the construction of general utility functions incorporating various decision maker characteristics and choice attributes to elicit preference of respondents. The general postulate in DCM is that utility is derived from the properties of things rather than the actual thing per se. A particular benefit of DCM in this study is the elicitation of preference for services and interventions that have not been introduced by SSS. The first step in the study is a theoretical investigation of the potential of SSS in the SADC region. It highlights the policy initiatives, the barriers and enablers related to the development of SSS. The proposed SSS system would have three main roles: to offer an alternative mode of freight transport service between port cities, to serve as the main leg in an intermodal transport network, and to serve feeder services between hub-and-spoke ports. The findings reveal that, SSS has the theoretical potential to work in the SADC region, given the large geographic region, projected freight volumes and customs and trade policies the SADC region is pursuing. The second step in the study involves an a-priori study conducted to develop a general understanding of freight transport in SADC. For this purpose, a uniquely developed online survey was conducted across the SADC region to ascertain in particular: who the decision maker is in terms of freight mode choice; and what the significant attributes that influence freight mode choice are. The results reveal that both the shipper and the freight forwarder are involved in mode choice decisions, however the shipper being the dominant decision maker. Furthermore, the results of the exploded logit model reveal that the top five modal attributes that shippers consider most important are: reliability, transport cost, risk of damage, frequency of service and transit time. These results were subsequently employed to inform the shipper and carrier behavior studies. The third step entails the assessment of shipper behavior, where trip specific mode choice decisions are studied along five intra-urban origin-destination (O-D) paired routes (which would form the study corridors). Three of these corridors considered unimodal SSS, and the two considered intermodal SSS. Unimodal SSS was studied along the following corridors: Cape Town (South Africa)~ Walvis Bay (Namibia), Walvis Bay (Namibia) ~ Luanda (Angola) and Durban (South Africa) ~Beira (Mozambique); and intermodal SSS was studied along the following corridors: Durban (South Africa) ~ Harare (Zimbabwe) and Cape Town (South Africa) ~ Windhoek (Namibia). To develop the choice scenarios, d-efficient stated choice experiments were uniquely developed for each of the corridors with the following key modal attributes systematically varied and analyzed across respondents: service frequency, reliability in terms of arriving on time, expected delay, transport cost and transport time. Subsequently, the following choice models were developed: Binary Logit, Mixed Logit and Integrated Choice and Latent Variable Structure models for the unimodal corridors; and Multinomial Logit, Nested Logit and Cross Nested Logit models for the intermodal corridors. The results highlight that in addition to the modal attributes, mode choice decisions are driven by shipper characteristics and situational characteristics. Moreover, the unimodal SSS study reveals that underlying latent perceptions also influence freight mode choice decisions; while the intermodal SSS study reveal strong correlations in the intermodal SSS alternatives, which requires improved intermodal capability if SSS is to become competitive. The fourth step in the study entail the assessment of maritime carriers preference for SSS given varying levels of maritime conditions that include: dedicated freight volumes, income from freight, port dues discount, terminal handling fees discount and ship registration requirements. The results of an ordered logit model reveal that ship registration provisions and terminal handling charges are the most important to the development of SSS from a carrier side. Moreover, ship registration and maritime cabotage provisions require visitation to boost the participation of carriers in SSS. The last step of the study revisits the modeling results and considers their implications through the estimation of willingness-to-pay and attribute elasticities. The results were then employed to suggest policy actions and interventions to develop SSS.

The purpose of this thesis is to investigate how the differences between the study and policy sites impact the performance of benefit function transfer. For this purpose, simulated data are created where all information necessary to conduct the benefit function transfer is available. We consider the six cases of difference between the study and policy sites- scale parameter, substitution possibilities, observable characteristics, population preferences, measurement error in variables, and a case of preference heterogeneity at the study site and fixed preferences at the policy site. These cases of difference were considered one at time and their impact on quality of transfer is investigated. RUM model based on reveled preference was used for this analysis. Function estimated at the study site is transferred to the policy site and willingness to pay for five different cases of policy changes are calculated at the study site. The willingness to pay so calculated is compared with true willingness to pay to evaluate the performance of benefit function transfer. When the study and policy site are different only in terms of scale parameter, equality of estimated and true expected WTP is not rejected for 89.7% or more when the sample size is 1000. Similarly, equality of estimated preference coefficients and true preference coefficients is not rejected for 88.8% or more. In this study, we find that benefit transfer performs better only in one direction. When the function is estimated at lower scale and transferred to the policy site with higher scale, the transfer error is less in magnitude than those which are estimated at higher scale and transferred to the policy site with lower scale. This study also finds that transfer error is less when the function from the study site having more site substitutes is transferred to the policy site having less site substitutes whenever there is difference in site substitution possibilities. Transfer error is magnified when measurement error is involved in any of the variables. This study do not suggest function transfer whenever the study site's model is missing one of the important variable at the policy site or whenever the data on variables included in study site's model is not available at the policy site for benefit transfer application. This study also suggests the use of large representative sample with sufficient variation to minimize transfer error in benefit transfer. / Master of Science

Spatial and social dependence shape human activity-travel pattern decisions and their antecedent choices. Although the transportation literature has long recognized the importance of considering spatial and social dependencies in modeling individuals’ choice behavior, there has been less research on techniques to accommodate these dependencies in discrete choice models, mainly because of the modeling complexities introduced by such interdependencies. The main goal of this dissertation, therefore, is to propose new modeling approaches for accommodating flexible spatial and social dependency structures in discrete choice models within the broader context of activity-based travel demand modeling. The primary objectives of this dissertation research are three-fold. The first objective is to develop a discrete choice modeling methodology that explicitly incorporates spatial dependency (or correlation) across location choice alternatives (whether the choice alternatives are contiguous or non-contiguous). This is achieved by incorporating flexible spatial correlations and patterns using a closed-form Generalized Extreme Value (GEV) structure. The second objective is to propose new approaches to accommodate spatial dependency (or correlation) across observational units for different aspatial discrete choice models, including binary choice and ordered-response choice models. This is achieved by adopting different copula-based methodologies, which offer flexible dependency structures to test for different forms of dependencies. Further, simple and practical approaches are proposed, obviating the need for any kind of simulation machinery and methods for estimation. Finally, the third objective is to formulate an enhanced methodology to capture the social dependency (or correlation) across observational units. In particular, a clustered copula-based approach is formulated to recognize the potential dependence due to cluster effects (such as family-related effects) in an ordered-response context. The proposed approaches are empirically applied in the context of both spatial and aspatial choice situations, including residential location and activity participation choices. In particular, the results show that ignoring spatial and social dependencies, when present, can lead to inconsistent and inefficient parameter estimates that, in turn, can result in misinformed policy actions and recommendations. The approaches proposed in this research are simple, flexible and easy-to-implement, applicable to data sets of any size, do not require any simulation machinery, and do not impose any restrictive assumptions on the dependency structure. / text

<p>QC 20130524</p>

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. / Les gens consacrent une importante part de leur existence à prendre diverses décisions, pouvant affecter leur demande en transport, par exemple les choix de lieux d'habitation et de travail, les modes de transport, les heures de départ, le nombre et type de voitures dans le ménage, les itinéraires ... Les choix liés au transport sont généralement fonction du temps et caractérisés par un grand nombre de solutions alternatives qui peuvent être spatialement corrélées. Cette thèse traite de modèles pouvant être utilisés pour analyser et prédire les choix discrets dans les applications liées aux réseaux de grandes tailles. Les modèles et méthodes proposées sont particulièrement pertinents pour les applications en transport, sans toutefois s'y limiter. Nous modélisons les décisions comme des séquences de choix, dans le cadre des choix discrets dynamiques, aussi connus comme processus de décision de Markov paramétriques. Ces modèles sont réputés difficiles à estimer et à appliquer en prédiction, puisque le calcul des probabilités de choix requiert la résolution de problèmes de programmation dynamique. Nous montrons dans cette thèse qu'il est possible d'exploiter la structure du réseau et la flexibilité de la programmation dynamique afin de rendre l'approche de modélisation dynamique en choix discrets non seulement utile pour représenter les choix dépendant du temps, mais également pour modéliser plus facilement des choix statiques au sein d'ensembles de choix de très grande taille. La thèse se compose de sept articles, présentant divers modèles et méthodes d'estimation, leur application ainsi que des expériences numériques sur des modèles de choix discrets de grande taille. Nous regroupons les contributions en trois principales thématiques: modélisation du choix de route, estimation de modèles en valeur extrême multivariée (MEV) de grande taille et algorithmes d'optimisation non-linéaire. Cinq articles sont associés à la modélisation de choix de route. Nous proposons différents modèles de choix discrets dynamiques permettant aux utilités des chemins d'être corrélées, sur base de formulations MEV et logit mixte. Les modèles résultants devenant coûteux à estimer, nous présentons de nouvelles approches permettant de diminuer les efforts de calcul. Nous proposons par exemple une méthode de décomposition qui non seulement ouvre la possibilité d'estimer efficacement des modèles logit mixte, mais également d'accélérer l'estimation de modèles simples comme les modèles logit multinomiaux, ce qui a également des implications en simulation de trafic. De plus, nous comparons les règles de décision basées sur le principe de maximisation d'utilité de celles sur la minimisation du regret pour ce type de modèles. Nous proposons finalement un test statistique sur les erreurs de spécification pour les modèles de choix de route basés sur le logit multinomial. Le second thème porte sur l'estimation de modèles de choix discrets statiques avec de grands ensembles de choix. Nous établissons que certains types de modèles MEV peuvent être reformulés comme des modèles de choix discrets dynamiques, construits sur des réseaux de structure de corrélation. Ces modèles peuvent alors être estimées rapidement en utilisant des techniques de programmation dynamique en combinaison avec un algorithme efficace d'optimisation non-linéaire. La troisième et dernière thématique concerne les algorithmes d'optimisation non-linéaires dans le cadre de l'estimation de modèles complexes de choix discrets par maximum de vraisemblance. Nous examinons et adaptons des méthodes quasi-Newton structurées qui peuvent être facilement intégrées dans des algorithmes d'optimisation usuels (recherche linéaire et région de confiance) afin d'accélérer le processus d'estimation. Les modèles de choix discrets dynamiques et les méthodes d'optimisation proposés peuvent être employés dans diverses applications de choix discrets. Dans le domaine des sciences de données, des modèles qui peuvent traiter de grands ensembles de choix et des ensembles de choix séquentiels sont importants. Nos recherches peuvent dès lors être d'intérêt dans diverses applications d'analyse de la demande (analyse prédictive) ou peuvent être intégrées à des modèles d'optimisation (analyse prescriptive). De plus, nos études mettent en évidence le potentiel des techniques de programmation dynamique dans ce contexte, y compris pour des modèles statiques, ouvrant la voie à de multiples directions de recherche future.