System dynamics can be a very useful tool to expand the boundaries of one's mental models to better understand the underlying behavior of systems. But despite its utility, there remains challenges associated with system dynamics modeling that the current research addresses by expanding the system dynamics modeling toolbox. The first challenge relates to imprecision or vagueness, for example, with respect to human perception and linguistic variables. The most common approach is to use table or graph functions to capture the inherent vagueness in these linguistic (qualitative) variables. Yet, combining two or more table functions may lead to further complexity and, moreover, increased difficulty when analyzing the resulting behavior. As part of this research, we extend the system dynamics toolbox by applying fuzzy logic. Then, we select a problem of congestion pricing in mitigating traffic congestion to verify the effectiveness of our integration of fuzzy logic into system dynamics modeling. Another challenge, in system dynamics modeling, is defining proper equations to predict variables based on numerous studies. In particular, we focus on published equations in models for energy balance and weight change of individuals. For these models there is a need to define a single robust prediction equation for Basal Metabolic Rate (BMR), which is an element of the energy expenditure of the body. In our approach, we perform an extensive literature review to explore the relationship between BMR and different factors including age, body composition, gender, and ethnicity. We find that there are many equations used to estimate BMR, especially for different demographic groups. Further, we find that these equations use different independent variables and, in a few cases, generate inconsistent conclusions. It follows then that selecting a single equation for BMI can be quite difficult for purposes of modeling in a systems dynamics context. Our approach involves conducting a meta-regression to summarize the available prediction equations and identifying the most appropriate model for predicting BMR for different sub-populations. The results of this research potentially could lead to more precise predictions of body weight and enhanced policy interventions to help mitigate serious health issues such as obesity. / Ph. D.

This dissertation provides a comprehensive examination of the role of Transportation Networking Companies (TNC) across four dimensions. First, we examined the factors affecting Transportation Networking Companies (TNC) pricing and destination choice behavior using weekday TNC trip data from Chicago spanning January 2019 through December 2019. Towards achieving this goal, we developed a joint model framework where trip fare is modelled using linear regression model (LR) and destination choice is modelled using a multinomial logit model (MNL). Second, we build a systematic framework to analyze spatial TNC demand patterns (origins) across the urban region at the census tract (CT) level and compare them to overall transportation demand. We propose and compute a novel metric at the census tract level to identify the potential imbalance between overall transportation demand and TNC demand by developing a Generalized Ordered Logit. The model applicability is further illustrated through elasticity analysis. Third, based on earlier studies we identified that current TNC related macroscopic studies do not incorporate attributes at the microscopic resolution. We bridge the macroscopic and microscopic analysis using a bi-level modeling approaching that accommodates for the influence of microscopic attributes within the macroscopic modeling approach. In this proposed framework, the trip level destination choice model (microscopic model) takes the form of a multinomial logit model and the origin-destination flow model (macroscopic model) takes the form of a multinomial logit fractional split model. Finally, in our effort to incorporate TNCs into travel demand tools, we conducted a comprehensive literature review on studies examining the impact of TNCs on various components of travel demand models (TDM). We provide guidelines for potential travel demand model updates using three use case examples including vehicle ownership model, trip generation model, and trip level mode choice components.

<p>  </p> <p>Transportation access is an important indicator of the quality of life and if it is inequitable, it will limit the work, leisure, and other essential opportunities for people and worsen the access for the disadvantaged groups. In the U.S., increased auto-dependency and the lack of other feasible alternative transportation modes exacerbate the negative impacts of this inequity, especially for the people without automobiles. The transportation equity in terms of the number of feasible transportation mode alternatives to serve a trip (i.e. mobility option equity) has not been extensively evaluated in the literature. Existing studies mainly analyzed the access to transportation infrastructures (e.g., bus stops, bike lanes, shared bike stations) based on the proximity at the zonal level. However, having access to a certain trip mode based on proximity does not necessarily add to the mobility option equity. First, mismatch may exist between the infrastructure and an individual’s travel demand. For example, if someone lives closely to a bus station but the bus route that can be accessed does not align with this person’s trip destination, they will not be able to use bus as a feasible mode for this trip. Second, existing accessibility-based studies often lack consideration of the trip feasibility (in terms of cost, quality, and safety) of using transportation infrastructures at the route level. For example, if a walking trip route is generated without considering the existence of sidewalks, the individual might have to walk on a unsafe busy road. In this case they will not be able to walk to satisfy their travel demand. Therefore, better transportation equity metrics concerning the feasibility of using transportation infrastructures to serve individuals’ travel demands are needed. </p> <p>To address this gap, this thesis defined the “travel-demand-relevant access” (mobility-need-relevant access) metric to evaluate transportation access in the context of individual travel demands and route-level infrastructure constraints and developed a framework to use GPS data to quantify the proposed metric for transportation equity analysis. Assessing which transportation modes are feasible alternatives to serve a trip, requires trip-level disaggregated travel demand data and detailed transportation infrastructure information. The recent development of information and communication technologies and open data efforts provide unprecedented opportunities for such trip-level analysis. With these developments it is now possible to evaluate the feasibility of a mode both the cost- and quality-based measures. The cost-based method estimates the monetary and time cost of using each mobility option and compares it with prominent trip mode (car) to examine “forced car use” concerning the travel demand. The quality-based method comprises accessibility and mobility-based performance measures to evaluate the feasibility of a certain trip mode regarding the ease of use and safety with relation to the infrastructure characteristics. The mobility options/alternatives deemed feasible with these two methods were used in the equity analysis, where the travel-demand-relevant access on the spatial and sociodemographic level was evaluated. </p> <p>The proposed framework was applied to the Indianapolis Metropolitan Planning Area (MPA) as a case study. The key insights of this study can be listed as (1) it is important to consider travel-demand-relevant access to evaluate transportation equity because we found that 40% of the trips that were identified as accessible by public transit are not feasible when travel-demand-relevant access is considered; (2) suburban areas on average have 12% less mobility options available compared with  the urban core which forces high car ownership in these areas; amd (3) people with non-college educational attainment, households with more crowded rooms, and larger families are the negatively impacted disadvantaged groups while census block groups with high composition of white middle-class suburban families have the lowest number of options (1.5 on average) available. </p> <p>The suburban populations with a low number of mobility options (with a vehicle) are not necessarily at a disadvantage in terms of mobility option equity, since suburban areas are by design made to be car dependent. However, the lower number of feasible mobility options in these areas possesses a risk for the future if the consequences are not evaluated carefully. In terms of urban migration, if out-migration from the urban core to suburban areas keeps increasing as the pandemic trend suggests, the forced car ownership in suburban areas could increase and create/worsen transport deserts. This increase in vehicle ownership contradicts equity and environmental goals regarding transportation. If we observe an increase in the suburban to urban core migration trend, it can force disadvantaged groups to move into suburban areas because of gentrification and increasing prices. These disadvantaged groups could suffer from the limited amount of mobility options in suburban areas, since their access to opportunities would decrease. </p>

Regional growth caused the emergence of traffic congestion and pollution in the past few decades, which have started to affect small urban areas. These problems are not only related to transportation system design but also to land use planning. There has been growing recognition that the relationship between land use and transportation needs to be understood and analyzed in a consistent and systematic way. Integrated urban models have recently been introduced and implemented in several metropolitan areas to systematically examine the relationship between land use and transportation. The general consensus in the field of integrated urban models is that each model has its own limitations and assumptions because they are each designed for different application purposes. This dissertation proposes a new type of methodology to integrate the regression-based land use model and the combined trip distribution-assignment transportation model that can be applied to both metropolitan areas and small urban areas. The proposed integrated land use and transportation model framework has three components: the regression-based land use model, the combined trip distributionassignment transportation model, and the interaction between these two models. The combined trip distribution-assignment model framework provides the platform to simultaneously integrate the transportation model with the land use model. The land use model is developed using an easy-to-implement method in terms of correlation and regression analysis. The interaction between the land use model and the transportation model is examined by two model frameworks: feedback model framework and simultaneous model framework. The feedback model framework solves the land use model and the transportation model iteratively. The simultaneous model framework brings the land use model and the transportation models into one optimization program after introducing the used path set. Both the feedback model and the simultaneous model can be solved to estimate link flow, origin-destination (OD) trips, and household distribution with the results satisfying network equilibrium conditions. The proposed integrated model framework has an “affordable and easy-toimplement” land use model; it can be performed in small urban areas with limited resources. The model applications show that using the proposed integrated model framework can help decision-makers and planners in preparing for the future of their communities.

The basic design concept of most advanced traveler information systems (ATIS) is to present generic information to travelers, leaving travelers to react to the information in their own way. This passive way of managing traffic by providing generic traffic information makes it difficult to predict the outcome and may even incur an adverse effect, such as overreaction (also referred to as the herding effect). Active traffic and demand management (ATDM) is another approach that has received continual attention from both academic research and real-world practice, aiming to effectively influence people's travel demand, provide more travel options, coordinate between travelers, and reduce the need for travel. The research discussed in this article deals with how to provide users with a travel option that aims to minimize the marginal system impact that results from this routing. The goal of this research is to take better advantage of the available real-time traffic information provided by ATIS, to further improve the system level traffic condition from User Equilibrium (UE), or a real-world traffic system that is worse than UE, toward System Optimal (SO), and avoid passively managing traffic. A behaviorally induced, system optimal travel demand management model is presented to achieve this goal through incremental routing. Both analytical derivation and numerical analysis have been conducted on Tucson network in Arizona, as well as on the Capital Area Metropolitan Planning Organization (CAMPO) network in Austin, TX. The outcomes of both studies show that our proposed modeling framework is promising for improving network traffic conditions toward SO, and results in substantial economic savings.

Geralmente, dados relacionados à demanda por transportes não são independentes no espaço. Por esta razão, o uso de técnicas de estatística espacial torna-se relevante para aprimoramento de estimativas. A geoestatística está entre as técnicas que incorporam a dependência espacial em suas análises e o semivariograma é uma ferramenta indispensável para descrever e ilustrar a estrutura espacial de uma Variável Regionalizada. Muitas vezes, o cálculo e ajuste do semivariograma experimental são realizados visualmente, de acordo a familiaridade com os dados ou experiência do pesquisador, o que exige, sobretudo, tempo de dedicação às análises. A partir destas considerações, técnicas de otimização podem ser uma alternativa viável para cálculo e ajuste de semivariograma experimental. Diante disso, este trabalho objetiva avaliar a adequabilidade do uso de Algoritmos Genéticos (AG) para otimização da modelagem geoestatística aplicada à demanda por transportes. Outro ponto importante é a forma de representação de dados de transportes: quando disponíveis, dados desagregados, por domicílios, comprometem a qualidade dos modelos variográficos, devido à sua alta aleatoriedade espacial. Diante disso, outra importante contribuição deste estudo foi a implementação de um código em software livre para definir a dimensão de uma grade para agregação de dados pontuais. A implementação do AG permitiu a obtenção de inúmeros modelos variográficos de duas variáveis relacionadas à demanda por transportes, para dois diferentes suportes geográficos. Além disso, foi possível obter os intervalos mais frequentes dos parâmetros dos semivariograma com melhor fitness. Finalmente, uma proposta primária de análise da semivariância foi apresentada, a fim de validar os resultados obtidos pelo AG. A análise de mapas de semivariância permitiu verificar o comportamento estrutural das variáveis estudadas. Apesar da abordagem tradicional (mapas de semivariância e ajuste manual) apresentar algumas dissimilaridades quando comparada aos melhores semivariogramas provenientes do AG, as medidas de desempenho, obtidas através da validação cruzada, foram bem similares. Conclui-se que a otimização da modelagem geoestatística, através de AG, pode trazer contribuições importantes, relativas a maior facilidade de cálculo e ajuste, além de distribuição de parâmetros variográficos associados a soluções quase ótimas. Vale ressaltar que o código desenvolvido ao longo desta tese, disponível ao público, pode ser utilizado em qualquer área do conhecimento onde se verifique a existência de dependência espacial entre as observações. / Data related to travel demand are generally not independent in space. Due to this, using spatial statistics techniques is important for improving estimates. Geostatistics is among the techniques that incorporate spatial dependence in its analyses and the semivariogram is an indispensable tool to describe and illustrate the spatial structure of a Regionalized Variable. The calculation and fitting of the experimental semivariogram are often performed visually, according to the familiarity with the data or the researcher\'s experience, which requires, above all, time for the analyses. Based on these considerations, optimization techniques can be a viable alternative to calculate and fitting the experimental semivariogram. Therefore, this study aims to evaluate the adequacy of using Genetic Algorithms (GAs) to optimize geostatistical modeling applied to travel demand. Another important point is the way of representing travel data: when it is available, disaggregated data by households affect the quality of variographic models, due to their high spatial randomness. Therefore, another important contribution of this study was the implementation of a free software code to define the size of a grid for aggregation of point data. Implementing the GA enabled us to obtain numerous variographic models of two variables related to travel demand for two different geographical supports. In addition, the most frequent intervals of the semivariogram parameters could be obtained with better fitness. Finally, a primary proposal for semivariance analysis was presented in order to validate the results obtained by the GA. The semivariance analysis maps verified the structural behavior of the studied variables. In spite of traditional approach (semivariance maps and manual fit) to present some dissimilarities when compared to the best semivariograms from GA, the performance measures obtained through cross validation were very similar. It can be concluded that the geostatistical modeling optimization, through GA, can bring important contributions, related to making calculations and fits easier, as well as distribution of variographic parameters associated with almost optimal solutions. It is worth mentioning that the code developed throughout this thesis, available to the public, can be used in any area of knowledge where there is a spatial dependence between observations.

Em junho de 2013, o reajuste de R$ 0,20 na tarifa de ônibus gerou uma série de manifestações populares no país que acabaram fazendo alguns governos, como o da cidade de São Paulo, voltarem atrás e arcarem com essa diferença com as empresas de ônibus. Visto isso, o prefeito de São Paulo, Fernando Haddad, propôs uma política de municipalização de um tributo imposto sobre a gasolina, a CIDE, para financiar o transporte público urbano. Nesse contexto, foi encomendada uma pesquisa a Fundação Getúlio Vargas para responder a magnitude do impacto desse subsídio cruzado entre usuários do transporte privado e coletivo. Esse trabalho utiliza o resultado encontrado por essa pesquisa para responder qual o efeito sobre o tráfego da Região Metropolitana de São Paulo utilizando dados da Pesquisa de Origem e Destino de 2007. Os resultados encontrados mostram que a política de subsídio cruzado proporciona um baixo deslocamento no fluxo dos modos de transporte. Além disso, a análise de bem estar da política mostra que os mais favorecidos são os indivíduos de baixa renda. A estimação é feita com base em dois modelos de escolha discreta (Multinomial e Mixed Logit), separada por dois motivos de viagem: trabalho e estudo. E, as simulações de deslocamento de demanda utilizam dois valores de tributos, R$ 0,10 e R$ 0,50. / In June 2013, the increase of 0.20 BRL in bus fare has emerged a series of popular demonstrations in the country that ended up making some governments, such as the city of São Paulo, backtrack and pay out this difference with the buses company. So, the mayor of São Paulo, Fernando Haddad, proposed a policy of decentralization of a tax imposed on gasoline, CIDE, to finance urban public transport. In this context, a report was commissioned to Fundação Getúlio Vargas to respond the magnitude of the impact of cross-subsidy between users of private and collective transport. This work uses the results found in this report to answer the effect on traffic in the Metropolitan Region of São Paulo using data from the Source and Destination Survey of 2007. Results show that the cross-subsidy policy provides a low offset in the flow modes of transport. Moreover, the analysis of welfare policy shows that the most favored are the low-income individuals. The estimation is based on two discrete choice models (Multinomial and Mixed Logit), separate for two reasons of trips: work and study. And the simulations of displacement demand use two values of taxes, 0.10 and 0.50 BRL.

Recent developments in microsimulation modeling of activity and travel demand have called for the explicit recognition of time-space constraints under which individuals perform their activity and travel patterns. The estimation of time-space prism vertex locations, i.e., the perceived time constraints, is an important development in this context. Stochastic frontier modeling methodology offers a suitable framework for modeling and identifying the expected vertex locations of time space prisms within which people execute activity-travel patterns. In this work, stochastic frontier models of time space prism vertex locations are estimated for samples drawn from a household travel survey conducted in 2001 in the city of Thane on the west coast of India and National Household Travel Survey 2001, United States. This offers an opportunity to study time constraints governing activity travel patterns of individuals in a developing as well as developed country context. The work also includes comparisons between males and females, workers and non-workers, and developed and developing country contexts to better understand how socio-economic and socio-cultural norms and characteristics affect time space prism constraints. It is found that time space prism constraints in developing country data set can be modeled using the stochastic frontier modeling methodology. It is also found that significant differences exist between workers and non-workers and between males and females,possibly due to the more traditional gender and working status roles in the Indian context. Finally, both differences and similarities were noticed when comparisons were made between results obtained from the data set of India and United States. Many of these differences can be explained by the presence of other constraints including institutional, household, income, and transportation accessibility constraints that are generally significantly greater in the developing country context.

In recent years, various researches are under progress to enhance the quality of the travel survey. These researches were mainly performed with the aid of GPS technology. Initially the researches were mainly focused on the vehicle travel mode due to the availability of GPS technology in vehicle. But, nowadays due to the accessible of GPS devices for personal uses, researchers have diverted their focus on personal mobility in all travel modes. This master’s thesis aimed at developing a mechanism to extract one type of travel survey information particularly travel mode from collected GPS dataset. The available GPS dataset is collected for travel modes of walk, bike, car, and public transport travel modes such as bus, train and subway. The developed procedure consists of two stages where the first is the dividing the track trips into trips and further the trips into segments by means of a segmentation process. The segmentation process is based on an assumption that a traveler switches from one transportation mode to the other. Thus, the trips are divided into walking and non walking segments. The second phase comprises a procedure to develop a classification model to infer the separated segments with travel modes of walk, bike, bus, car, train and subway. In order to develop the classification model, a supervised classification method has been used where decision tree algorithm is adopted. The highest obtained prediction accuracy of the classification system is walk travel mode with 75.86%. In addition, the travel modes of bike and bus have shown the lowest prediction accuracy. Moreover, the developed system has showed remarkable results that could be used as baseline for further similar researches.

The overall goal of the dissertation is to contribute to the growing literature on the activity-based framework by focusing on the modeling of choices that are influenced by land-use and travel environment attributes. An accurate characterization of activity-travel patterns requires explicit consideration of the land-use and travel environment (referred to as travel environment from here on). There are two important categories of travel environment influences: direct (or causal) and indirect (or self-selection) effects. The direct effect of travel environment refers to how travel environment attributes causally influence travel choices. This direct effect may be captured by including travel environment variables as exogenous variables in travel models. Of course, determining if a travel environment variable has a direct effect on an activity/travel choice of interest is anything but straightforward. This is because of a potential indirect effect of the influence of the travel environment, which is not related to a causal effect. That is, the very travel environment attributes experienced by a decision maker (individual or household) is a function of a suite of a priori travel related choices made by the decision maker. The specific emphasis of the current dissertation is on moving away from considering travel environment choices as purely exogenous determinants of activity-travel models, and instead explicitly modeling travel environment decisions jointly along with activity-travel decisions in an integrated framework. Towards this end, the current dissertation formulates econometric models to analyze multidimensional choices. The multidimensional choice situations examined (and the corresponding model developed) in the research effort include: (1) reason for residential relocation and associated duration of stay (joint multinomial logit model and a grouped logit model), (2) household residential location and daily vehicle miles travelled (Copula based joint binary logit and log-linear regression model), (3) household residential location, vehicle type and usage choices (copula based Generalized Extreme Value and log-linear regression model) and (4) activity type, travel mode, time period of day, activity duration and activity location (joint multiple discrete continuous extreme value (MDCEV) model and multinomial logit model (MNL) with sampling of alternatives). The models developed in the current dissertation are estimated using actual field data from Zurich and San Francisco. A variety of policy exercises are conducted to illustrate the advantages of the econometric models developed. The results from these exercises clearly underline the importance of incorporating the direct and indirect effects of travel environment on these choice scenarios. / text