El distrito de San Borja, es actualmente, uno de los lugares más congestionados en la provincia de Lima. Por ello, es importante realizar estudios que reflejen la situación actual del distrito y, así mismo, poder dar solución a los puntos con mayor tráfico vehicular. El distrito de San Borja, no cuenta con una base de datos actualizada con dicha información, por lo cual esta tesis propone una plataforma de base de datos que identifique los principales orígenes y destinos de los viajes, obtenida mediante encuestas en el distrito de San Borja, el cual será el área de estudio para esta investigación. Esta área de investigación se ha dividido convenientemente en 12 zonas, las cuales se ha hecho coincidir con los 12 sectores en los cuales se encuentra dividido el distrito de San Borja, con el objetivo de aprovechar la disponibilidad de datos referidos a esa zonificación. Además de ello, se ha tomado en cuenta 4 macro zonas externas adicionales (conformadas por los distritos restantes en la provincia de Lima), es decir, en total 16 zonas de estudio. Con los datos procesados, se obtuvo una matriz Origen Destino actual, la cual se calibró mediante dos modelos (Gravitacional y Fratar) de distribución de viajes, los cuales se utilizaron para el pronóstico de un escenario futuro proyectado a 5 años. Previamente, se elaboraron los modelos de generación y atracción de viajes, los cuales son requisitos para los modelos de distribución de viajes. / The district of San Borja is currently one of the most congested places in the province of Lima. For this reason, it is important to carry out studies that reflect the current situation of the district and, likewise, to be able to solve the points with the highest vehicular traffic. The district of San Borja does not have an updated database with this information, so this thesis proposes a database platform that identifies the main origins and destinations of the trips, obtained through surveys in the district of San Borja, which will be the study area for this investigation. This area of research has been conveniently divided into 12 zones, which has been made to coincide with the 12 sectors in which the district of San Borja is divided, in order to take advantage of the availability of data referring to this zoning. In addition, four additional external zones (made up of the remaining districts in the province of Lima) have been taken into account, that is, a total of 16 study areas. With the processed data, a current Origin Destination matrix was obtained, which was calibrated by means of two models (Gravitational and Fratar) of trip distribution, which were used for the forecast of a future scenario projected to 5 years. Previously, travel generation and attraction models were developed, which are requirements for travel distribution models. / Tesis

The Portfolio Analysis Management Office (PAMO) for the Aeronautics Research Mission Directorate (ARMD) at NASA Headquarters tasked the Systems Analysis and Concepts Directorate at NASA Langley to combine efforts with Virginia Tech to develop a global demand model with the capability to predict future demand in the air transportation field. A previous study (Alsalous, 2015) started the development of the Global Demand Mode (GDM) to predict air travel demand based on Gross Domestic Product (GDP) and population trends for 3,974 airports worldwide. The study was done from year 2016 to year 2040. This research project intends to enhance the GDM capabilities. A Fratar model is implemented for the distribution of the forecast demand during each year. The Fratar model uses a 3,974 by 3,974 origin-destination matrix to distribute the demand among 55,612 unique routes in the network. Moreover, the GDM is capable to estimate the aircraft fleet mix per route and the number of flights per aircraft that are needed to satisfy the forecast demand. The model adopts the aircraft fleet mix from the Official Airline Guide data for the year 2015. Once the aircraft types are distributed and flights are assigned, the GDM runs an aircraft retirement and replacement analysis to remove older generation aircraft from the network and replace them with existing or newer aircraft. The GDM continues to evolve worldwide aircraft fleet by introducing 14 new generation aircraft from Airbus, Boeing, Bombardier, and Embraer and 5 Advanced Technology Aircraft from NASA. / Master of Science / The Portfolio Analysis Management Office (PAMO) for the Aeronautics Research Mission Directorate (ARMD) at NASA Headquarters tasked the Systems Analysis and Concepts Directorate at NASA Langley to combine efforts with Virginia Tech to develop a global demand model with the capability to predict future demand in the air transportation field. A previous study (Alsalous, 2015) started the development of the Global Demand Mode (GDM) to predict air travel demand based on Gross Domestic Product (GDP) and population trends for 3,974 airports worldwide. The study was done from year 2016 to year 2040. The previous study done by Alsaous, predicts how many seats will be departing out of the 3,974 airports worldwide. This project intends to use the outputs of the GDM and distribute the seats predicted among the airports. The objective is to predict how many seats will be offered that will be departing from airport “A” and arriving at airport “B”. For this, a Fratar model was implemented. The second objective of this project is to estimate what will the aircraft fleet be in the future and how many flights will be needed to satisfy the predicted air travel demand. If the number of seats going from airport A to airport B is known, then, by analyzing real data it can be estimated what type of aircraft will be flying from airport “A” to airport “B” and how many flights each aircraft will have to perform in order to satisfy the forecasted demand. Besides of estimating the type of aircraft that will be used in the future, the modeled created is capable of introducing new aircraft that are not part of the network yet. Fourteen new generation aircraft from Airbus, Boeing, Bombardier, and Embraer and 5 Advanced Technology Aircraft from NASA.