This paper presents a combined method for short-term forecasting of detector counts in urban networks and subsequent traffic demand estimation using the forecasted counts as constraints to estimate origin-destination (OD) flows, route and link volumes. The method is intended to be used in the framework of an adaptive traffic control strategy with consecutive optimization intervals of 15. min. The method continuously estimates the forthcoming traffic demand that can be used as input data for the optimization. The forecasting uses current and reference space-time-patterns of detector counts. The reference patterns are derived from data collected in the past. The current pattern comprises all detector counts of the last four time intervals. A simple but effective pattern matching is used for forecasting. The subsequent demand estimation is based on the information minimization model that has been integrated into an iterative procedure with repeated traffic assignment and matrix estimation until a stable solution is found. Some enhancements including the improvement of constraints, redundancy elimination of these constraints and a travel time estimation based on a macroscopic simulation using the Cell Transmission Model have been implemented. The overall method, its modules and its performance, which has been assessed using artificially created data for a real sub-network in Hannover, Germany, by means of a microsimulation with Aimsun NG, are presented in this paper.

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This study aims to provide an empirical investigation of Postal Services pricing in Turkey by way of computing Ramsey prices and examining the sensitivity of Ramsey prices to changes in demand and cost parameters. In this study, the Ramsey pricing problem is stated as maximizing a welfare function subject to the Post Office attaining a certain degree of profitability. The conditions necessary for the Post Office to be able to price efficiently have implications for Ramsey pricing. We estimate demand functions and cost structure of letters and express mail using data from Turkish Postal Services. The robustness of the Ramsey rule is assessed under alternative estimates of demand and similarly, in the absence of reliable data, under alternative intervals of marginal cost. Ramsey prices for two letter categories and welfare gains of moving from the existing pricing structure to Ramsey are determined and examined. Sensitivity analysis indicates that the existing policy is not Ramsey optimal and that this is a fairly robust result.

Esta tesis es una colección de tres ensayos en el campo de la economía industrial. Los dos primeros ensayos son contribuciones empíricas en el tema de la discriminación de precios. El último ensayo está relacionado con la heterogeneidad en las expectativas futuras de precios. Ese ensayo es una contribución a la literatura que trata el tema de demanda dinámica. El objetivo del primer capítulo es determinar la relevancia de la discriminación de precios en los mercados de alimentos orgánicos (cereales para desayuno). El principal problema al responder a esta pregunta es que los costes no son observables y, por lo tanto, a priori, no puedo saber si el sobreprecio de los alimentos orgánicos se debe a la discriminación precios o simplemente por diferencias de costes. Para evitar este problema, utilizo un modelo de elección discreta para obtener la elasticidad de la demanda y, con un modelo de oferta, obtener el valor de los costes marginales. Lo resultados indican que, aproximadamente, el 6% de la diferencia de precio entre productos orgánicos y productos no orgánicos se debe a la discriminación de precios. Además, yo encuentro que un impuesto en los productos no orgánicos no es suficiente para disminuir la discriminación de precios, y tiene un efecto negativo en el bienestar social: hay reducción en el excedente del consumidor que es superior al aumento de los beneficios de las empresas. Por fin, encuentro que la discriminación de precios resulta de la existencia de consumidores con renta alta en el mercado. En el segundo capítulo yo estudio si hay una relación no monótona entre los descuentos por cantidad y el nivel de competencia en el mercado. Los resultados encontrados sugieren que la relación es sí no-monótona y que tiene formato en U. Eso implica que los descuentos por cantidad son más comunes en los mercados menos concentrados y en los más concentrados. Además, los resultados sugieren que la firma líder es la responsable por esa situación. Por fin, el tercer capítulo analiza la heterogeneidad en la expectativa de precios en el mercado de un bien no duradero (refrescos) y que son comprados con alta frecuencia. Los resultados sugieren que, en media, los consumidores son racionales y forman expectativas con respecto al futuro. Sin embargo, consumidores de baja renta forman expectativas basadas en un modelo de Markov de primer orden (donde solo importa el precio actual y del período anterior para formar sus expectativas), y a los consumidores con renta mediana solo les importa el precio actual al decidir comprar (o sea, son impacientes - miopes).

This dissertation comprises multiple studies surrounding demand estimation, feasibility and capacity analysis, and environmental impact of the Urban Air Mobility (UAM) or Advanced Air Mobility (AAM). UAM is a concept aerial transportation mode designed for intracity transport of passengers and cargo utilizing autonomous (or piloted) electric vehicles capable of Vertical Take-Off and Landing (VTOL) from dense and congested areas. While the industry is preparing to introduce this revolutionary mode in urban areas, realizing the scope and understanding the factors affecting the attractiveness of this mode is essential. The success of UAM depends on its operational efficiency and the relative utility it offers to current travelers. The studies presented in this dissertation primarily focus on analyzing urban travelers' current behavior using revealed preference data and estimating the potential UAM demand for different trip purposes in multiple U.S. urban areas. Chapter II presents a methodology to estimate commuter demand for UAM operations in the Northern California region. A mode-choice model is calibrated from the commuter mode-choice behavior observed in the survey data. An integrated demand estimation framework is developed utilizing the calibrated mode-choice model to estimate UAM demand and place vertiports. The feasibility of commuter UAM operations in Northern California is further analyzed through a series of sensitivity analyses. This study was published in Transportation Research Part A: Policy and Practice journal. In an effort to analyze the feasibility of UAM operations in different use cases, demand estimation frameworks are developed to estimate UAM demand in the airport access trips segment. Chapter III and Chapter IV focus on developing the UAM Concept of Operations (ConOps) and demand estimation methodology for airport access trips to Dallas-Fort Worth International Airport (DFW)/Dallas Love Field Airport (DAL) and Los Angeles International Airport (LAX), respectively. Both studies utilize the latest available originating passenger survey data to understand arriving passengers' mode-choice behavior at the airport. Mode-choice conditional logit models are calibrated from the survey data, further used to estimate UAM demand. The former study is published in the AIAA Aviation 2021 Conference proceeding, and the latter is published in ICNS 2021 Conference proceedings. UAM vertiport capacity may be a barrier to the scalability of UAM operations. A heavy concentration of UAM demand is observed in specific areas such as Central Business Districts (CBD) during the spatial analysis of estimated UAM demand. However, vertiport size could be limited due to land availability and high infrastructure costs in CBDs. Therefore, operational efficiency is critical for capturing maximum UAM demand with limited vertiport size. The study included in Chapter V focuses on analyzing factors impacting vertiport capacity. A discrete-event simulation model is developed to simulate a full day of commuter operations at the San Francisco Financial District's busiest vertiport. Besides calculating the capacity of different fundamental vertiport designs, sensitivity analyses are carried to understand the impact of several assumptions such as service time at landing pads, service time at parking stall, charging rate, etc. The study explores the importance of pre-positioning UAM vehicles during the time of imbalance between arrival and departure requests. This study is published in ICNS 2021 Conference proceedings. Community annoyance from aviation noise has often been a reason for limiting commercial operations at several major airports globally. Busy airports are located in urban areas with high population densities where noise levels in nearby communities could govern capacity constraints. Commercial aviation noise is only a concern during landing and take-offs. Hence, the impact is limited to communities close to the airport. However, UAM vehicles would be operated at much lower altitudes and have more frequent taking-off and landing operations. Since the UAM operations would mostly be over dense urban spaces, the noise potential is significantly high. Chapter VI includes a study on preliminary estimation of noise levels from commuter UAM operations in Northern California and the Dallas-Fort Worth region. This study is published in the AIAA Aviation 2021 Conference proceedings. The final chapter in this dissertation explores the impact of airspace restrictions on UAM demand potential in New York City. Integration of UAM operations in the current National Airspace System (NAS) has been recognized as critical in developing the UAM ecosystem. Several pieces of urban airspace are currently controlled by Air Traffic Control (ATC), where commercial operation density is high. Even though the initial operations are expected to be controlled by the current ATC, the extent to which UAM operations would be allowed in the controlled spaces is still unclear. As the UAM system matures and the ecosystem evolves, integrating UAM traffic with other airspace management might relax certain airspace restrictions. Relaxation of airspace restrictions could increase the attractiveness of UAM due to a decrease in travel time/cost and relatively more optimal placement of vertiports. Quantifying the impact of different levels of airspace restrictions requires an integrated framework that can capture utility changes for UAM under different operational ConOps. This analysis uses a calibrated mode-choice model, restriction-sensitive vertiport placement methodology, and demand estimation process. This study has been submitted for ICNS 2022 Conference. / Doctor of Philosophy / Urban Air Mobility (UAM) or Advanced Air Mobility (AAM) are concept transportation modes currently in development. It proposes transporting passengers and cargo in urban areas using all-electric Vertical Take-Off and Landing (eVTOL) vehicles. UAM is a multi-modal concept involving low-altitude aerial transport. The high capital costs involved in developing vehicles and infrastructure suggests the need for meticulous planning and strong strategy development in the rolling out of UAM. Moreover, urban travelers are relatively more sensitive to travel time savings and travel time reliability; therefore, the efficiency of UAM is critical for its success. This dissertation comprises multiple studies surrounding demand estimation, feasibility and capacity analysis, and the environmental impact of UAM. To estimate the potential for UAM, we need first to understand the mode-choice making behavior of urban travelers and then estimate the relative utility UAM could possibly offer. The studies presented in this dissertation primarily focus on analyzing urban travelers' current behavior and estimating the potential UAM demand for different trip purposes in multiple U.S. urban areas. The system planners would need to know the individual or combined effect of various parameters in the system, such as cost of UAM, network size of UAM, etc., on UAM potential. Therefore, sensitivity analyses with respect to UAM demand are performed against various framework parameters. Capacity constraints are not initially considered for potential demand estimation. However, like any other transportation mode, UAM could suffer from capacity issues that can cause operational delays. A simulation study is dedicated to model UAM operations at a vertiport and estimating factors affecting vertiport capacity. After observing the demand potential for certain optimistic scenarios, we realized the possibility of a large number of low-flying vehicles, which could cause annoyance and environmental impacts. Therefore, the following study focuses on developing a noise estimation framework from a full-day of UAM operations and estimating a highly annoyed population in the Bay Area and Dallas-Fort Worth Region. In our studies, modeling restricted airspaces (due to commercial operations at large airports) was always a critical part of the analysis. The urban airspaces are already quite congested in some urban areas, and we assumed that UAM would not operate in the restricted airspaces. The last study in this dissertation focuses on quantifying the impact of different levels of airspace restrictions on UAM demand potential in New York. It would help system planners gauge the level of integration required between the UAM and National Airspace System (NAS).

<div>The purpose of this dissertation is to study the effectiveness of public policies in generating fuel savings and emissions reductions. I focus on applying various empirical methods to analyze consumer responses to policy changes on both extensive and intensive margins. This dissertation consists of two chapters.</div><div><br></div><div>In the first chapter, I compare the effectiveness of fuel taxes and product taxes on reducing gasoline consumption of new car buyers. I employ a unified data source for vehicle choice and subsequent vehicle use to estimate a random effects logit demand model that explicitly accounts for vehicle use heterogeneity. My demand estimation suggests that new car buyers fully value the fuel-saving benefits from improved vehicle fuel efficiency when they initially purchase their cars. My policy simulations indicate that high-mileage drivers are more responsive to a change in fuel taxes than to a change in product taxes, even as low-mileage drivers are more responsive to product taxes. By capturing such heterogeneous consumer response to policies, I show that a counterfactual increase of the fuel tax is more effective than a revenue-equivalent product tax in reducing the total gasoline consumption of new car buyers. Further, when accounting for its effects on consumer response on both extensive and intensive margins, a change in fuel taxes has a clear advantage over a change in product taxes in reducing the consumption of gasoline even when the magnitude of tax increase is small. More importantly, a model not accounting for vehicle use heterogeneity understates the fuel saving effects of both policies and misleads us about the relative effectiveness when comparing different policies. </div><div><br></div><div>The second chapter explores how changes in the marginal cost of driving affect consumers decisions about passenger vehicle utilization, as measured by average daily miles traveled per vehicle. This intensive margin of consumer response has important implications for the effectiveness of usage-based policies, such as the fuel tax and the mileage tax, that designed to address externalities of driving. I estimate the elasticity of driving with respect to fuel cost per mile using a large panel data that covers 351 towns and cities in Massachusetts over 24 quarters. While most researchers in this literature apply fixed effects estimators to examine the elasticity of driving, I use a factor model econometric setup to account for unobserved common factors and regional heterogeneity. Residual diagnostics confirm that the factor model setup does a better job of removing the cross-section dependence than fixed effects estimators do. Given low consumer responsiveness to changes in the marginal cost of driving engendered by current usage-based policies, rights-based approaches like congestion charges might be better alternatives to influence vehicle utilization and vehicle ownership.</div>

<div> <p>Public transportation is vital for many people who do not have the means to use other forms of transportation. In small communities, transit service is often limited, due to funding constraints of the transit agency. In order to maximize the use of available funding resources, agencies strive to provide effective and efficient service that meets the needs of as many people as possible. To do this, effective service planning is critical.</p> <p> </p> <p>Unlike traditional road-based transportation projects, transit service modifications can be implemented over the span of just a few weeks. In planning for these short-term changes, the traditional four-step transportation planning process is often inadequate. Yet, the characteristics of small communities and the resources available to them limit the applicability of existing transit demand models, which are generally intended for larger cities.</p> <p> </p> <p>This research proposes a methodology for using population and demographic data from the Census Bureau, combined with stop-level ridership data from the transit agency, to develop models for forecasting transit ridership generated by a given geographic area with known population and socioeconomic characteristics. The product of this research is a methodology that can be applied to develop ridership models for transit agencies in small cities. To demonstrate the methodology, the thesis built ridership models using data from Lafayette, Indiana.</p> <p> </p> <p>A total of four (4) ridership models are developed, giving a transit agency the choice to select a model, based on available data and desired predictive power. More complex models are expected to provide greater predictive power, but also require more time and data to implement. Simpler models may be adequate where data availability is a challenge. Finally, examples are provided to aid in applying the models to various situations. Aggregation levels of the American Community Survey (ACS) data provided some challenge in developing accurate models, however, the developed models are still expected to provide useful information, particularly in situations where local knowledge is limited, or where additional information is unavailable.</p> </div> <br>

This dissertation aimed to improve travel time estimation for the purpose of transportation planning by developing a travel time estimation method that incorporates the effects of signal timing plans, which were difficult to consider in planning models. For this purpose, an analytical model has been developed. The model parameters were calibrated based on data from CORSIM microscopic simulation, with signal timing plans optimized using the TRANSYT-7F software. Independent variables in the model are link length, free-flow speed, and traffic volumes from the competing turning movements. The developed model has three advantages compared to traditional link-based or node-based models. First, the model considers the influence of signal timing plans for a variety of traffic volume combinations without requiring signal timing information as input. Second, the model describes the non-uniform spatial distribution of delay along a link, this being able to estimate the impacts of queues at different upstream locations of an intersection and attribute delays to a subject link and upstream link. Third, the model shows promise of improving the accuracy of travel time prediction. The mean absolute percentage error (MAPE) of the model is 13% for a set of field data from Minnesota Department of Transportation (MDOT); this is close to the MAPE of uniform delay in the HCM 2000 method (11%). The HCM is the industrial accepted analytical model in the existing literature, but it requires signal timing information as input for calculating delays. The developed model also outperforms the HCM 2000 method for a set of Miami-Dade County data that represent congested traffic conditions, with a MAPE of 29%, compared to 31% of the HCM 2000 method. The advantages of the proposed model make it feasible for application to a large network without the burden of signal timing input, while improving the accuracy of travel time estimation. An assignment model with the developed travel time estimation method has been implemented in a South Florida planning model, which improved assignment results.