With reliable models to forecast travel demand, traffic planners and decision-makers can be assisted in choosing the best solutions to obtain traffic performance goals. Practitioners have traditionally been relying on infrequent, costly and respondent pressurized travel surveys as their main source of data for these models. The drawbacks of the data collection method highlight a need to search for alternative sources of data used for the purpose. One such source is public transport “tap-in” data. This thesis executed a case study with the target of improving the travel demand model of Norrköping via public transport data. An algorithm that estimates the alighting station of travellers was applied to a data set provided by the public transport operator of the city. By allocating the OD-demand from stations to the traffic analysis zones used in the model a straightforward integration method using the tap-in estimate as a reference matrix could be used. The target with the method was to redistribute the demand in such a way that the public transport demand approached the tap-in estimate but that the total demand for all modes for the OD-pair remained unchanged. The results gave some indication that the integration of tap-in data improved the model performance from the perspective of public transports. In a regression analysis comparing the number of entries per station the integration of tap-in data increased the correlation coefficient from 0,845 to 0,864. Further was the performance for other transport modes seemingly not worsened by the integration of tap-in data. Finding an allocation procedure that was generic but still accurate proved complex. Further were drawbacks with the integration procedure highlighted where the method executed affected the results of the model, not its behaviour. The consequence of this is that, though the model might be an accurate representation of the current state of traffic, it is difficult to execute the same procedure when investigating future states. Still, the thesis stressed some of the potential for public transport data in modelling contexts, where the role of the data, given the procedure executed, still is of complementary character to travel surveys.

Over the last few decades there has been a gradual transformation in both the spatial and temporal patterns of urban activities. The percentage share of non-discretionary travel such as morning rush-hour commuting has been declining with the increased income level. Discretionary activities appear to rise prominently in urban business and leisure hubs, attracting large volumes of crowds which in turn imply new and changed demand for building floorspace and urban infrastructure. Despite impressive advances in the theories and models of infrastructure demand forecasting, there appear to be an apparent research gap in addressing the practical needs of infrastructure planning in and around those growing urban activity hubs. First, land use and transport interaction models which have to date been the mainstay of practical policy analytics tend to focus on non-discretionary activities such as rush-hour commuting. Secondly, the emerging activity based models, while providing significant new insights into personal, familial activities, especially the discretionary travel, are so data hungry and computing intensive that they have not yet found their roles in practical policy applications. This dissertation builds on the insights from above schools of modelling to develop a new approach that addresses the infrastructure planning needs of the growing urban hubs while keeping the data and computing realistic in medium to high income cities. The new model is designed based on an overarching hypothesis that considerable efficiency and welfare gains can be achieved in the planning and development of urban business and leisure hubs if the infrastructure provisions for discretionary and non-discretionary activities can be coordinated. This is a research theme that has been little explored in current literature. The new infrastructure demand forecasting model has been designed with regard to the above hypothesis and realistic data availability, including those emerging online. The model extends the framework of land use transport interaction models and aim to provide a practical modelling tool. Land use changes are accounted for when testing new infrastructure investment initiatives and especially the road and public transport loads are assessed throughout all time periods of a working day. The new contribution to the modelling methodology includes the extension to the land use transport interaction framework, the use of social media data for estimating night market activity distribution and a rapid estimation of road traffic speeds from Google directions API, and model validation. Another new contribution is the understanding of the nature and magnitude of future infrastructure demand through assessing three alternative land use scenarios: (1) business as usual, (2) inner city regeneration for a major business hub around the night market, and (3) dispersed suburban growth with distant subcentres. The model is able to assess the implications for future infrastructure demand and user welfare through discerning the distinct discretionary and non-discretionary activity patterns.

abstract: This dissertation research contributes to the advancement of activity-based travel forecasting models along two lines of inquiry. First, the dissertation aims to introduce a continuous-time representation of activity participation in tour-based model systems in practice. Activity-based travel demand forecasting model systems in practice today are largely tour-based model systems that simulate individual daily activity-travel patterns through the prediction of day-level and tour-level activity agendas. These tour level activity-based models adopt a discrete time representation of activities and sequence the activities within tours using rule-based heuristics. An alternate stream of activity-based model systems mostly confined to the research arena are activity scheduling systems that adopt an evolutionary continuous-time approach to model activity participation subject to time-space prism constraints. In this research, a tour characterization framework capable of simulating and sequencing activities in tours along the continuous time dimension is developed and implemented using readily available travel survey data. The proposed framework includes components for modeling the multitude of secondary activities (stops) undertaken as part of the tour, the time allocated to various activities in a tour, and the sequence in which the activities are pursued. Second, the dissertation focuses on the implementation of a vehicle fleet composition model component that can be used not only to simulate the mix of vehicle types owned by households but also to identify the specific vehicle that will be used for a specific tour. Virtually all of the activity-based models in practice only model the choice of mode without due consideration of the type of vehicle used on a tour. In this research effort, a comprehensive vehicle fleet composition model system is developed and implemented. In addition, a primary driver allocation model and a tour-level vehicle type choice model are developed and estimated with a view to advancing the ability to track household vehicle usage through the course of a day within activity-based travel model systems. It is envisioned that these advances will enhance the fidelity of activity-based travel model systems in practice. / Dissertation/Thesis / Doctoral Dissertation Civil and Environmental Engineering 2014

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.

Autonomous Vehicles (AVs) are computer equipped vehicles that can operate without human driver’s active control using information provided by their sensors about the surrounding environment. Self-driving vehicles may have seemed to be a distant dream several years ago, but manufactures’ prototypes showed that AVs are becoming real now. Several car manufactures (i.e. Benz, Audi, etc.) and information technology firms (i.e. Google) have either showcased their fully AVs or announced their robot cars to be released in a few years. AVs hold the promise to transform the ways we live and travel. Although several studies have been conducted on the impacts of AVs, much remains to be explored regarding the various ways in which AVs could reshape our lifestyle. This dissertation addresses the knowledge gap in understanding the potential implications of AV technologies on travel behavior and system modeling. A comprehensive review of literature regarding AV adoption, potential impacts and system modeling was provided. Bass diffusion models were developed to investigate the market penetration process of AVs based on experience learned from past technologies. A stated preference survey was conducted to gather information from university population on the perceptions and attitudes toward AV technologies. The data collected from the Florida International University (FIU) was used to develop econometric models exploring the willingness to pay and relocation choices of travelers in light of the new technologies. In addition, the latest version of the Southeast Planning Regional Model (SERPM) 7.0, an Activity-Based Model (ABM), was employed to examine the potential impacts of AVs on the transportation network. Three scenarios were developed for short-term (2035), mid-term (2045) and long-term (2055) conditions. This dissertation provides a systematic approach to understand the potential implications of AV technologies on travel behavior and system modeling. The results of the survey data analysis and the scenario analysis also provide important inputs to guide planning and policy analysis on the impacts of AV technologies.

Shared modes of transport, including car sharing, have been pointed out as one way of reducing private car use, contributing to an efficient transportation system that fulfills societal and environmental goals.Previous studies show that a share of car sharing users sells or refrains from acquire a new vehicle, when entering car sharing. Also, on average, car sharing has been shown to reduce Vehicle Kilometers Traveled (VKT) by car among the users.This study is conducted in three parts. First, a literature review of the effects of car sharing on travel behavior and car ownership is presented. Second, an implementation of car sharing in an existing transport model is described and the estimated effects are analyzed in relation to the findings in the literature study. In the final part, the car sharing module is reformulated to model a station-based car sharing system, where the distances to car sharing vehicles are used to distribute the effect of car sharing on car ownership spatially.This work contributes to the field by connecting the results from previous research about car sharing with practical transport modelling. The model of the station-based car sharing system is a useful tool for planners when considering the placement of car sharing stations. Also, this study provides an updated literature review covering findings of the effects of car sharing on travel behaviour and car ownership.Keywords: car sharing, station-based car sharing, travel demand modelling, vehicle ownership modelling, four-step model

The present study examines a Mobility Management measure called “test traveller project”, which aims at increasing the public transport modal share by offering free public transport tickets to those who often use their car for their daily commuting and trips. The existing literature consists of a rather limited number of cases and their scope is usually limited because only some of the main elements that influence one’s travel behaviour are considered in each study. Furthermore, literature is not unanimous in concluding that this measure can reduce car use. This work studies more than 50 cases in Sweden, and by employing the Theory of Planned Behaviour the effects of test traveller projects have been examined in an empirical case in the Swedish municipality of Botkyrka, located in the Stockholm metropolitan area. The findings underline that a test traveller project, despite its limitations, may be a valid and relatively simple tool available to public bodies and public transport companies for enticing a segment of car drivers to switch to public transport where it is a valid alternative. In fact, according to the literature, the major results achievable are around 20% of new public transport users among test travellers, whereas in Sweden 20% has been achieved by the first upper quartile of the projects. In particular, the effectiveness of a test traveller project is greater when combined or conducted in parallel with other measures such as improvements in the public transport offer and/or changes in the transport system aiming at disadvantaging car use. The case study of Botkyrka has confirmed that attitudes are the major influencing factor when making the transport mode choice. Further, it has confirmed that environmental concerns and the time passed from one’s residential relocation also play an important role. Habits seem to be less important, thus adhering to that literature whose authors argue that an external event (such as moving home) makes people reflect upon and rethink their travel habits. The case study in Botkyrka has empirically demonstrated how the project participants correct their beliefs and perceptions about public transport, sometimes in a positive way and sometimes in a negative way. An interesting finding is the existence of a new category of people living in the suburbs. Literature indicates that, in the same suburban context, individuals with suburban land use preferences tend to use the car more that individuals with urban land use preferences. In the case study of Botkyrka clearly emerged as a majority among the test traveller project participants a category of individuals who have a suburban land use preference but at the same time would like to use public transportation instead of their car and have high environmental concerns.

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.

Informações desagregadas de demanda por transportes são recursos essenciais ao correto planejamento urbano, especialmente no que se refere ao transporte público. Contudo, o acesso a estes dados é limitado, devido ao alto custo para coleta de pesquisas domiciliares e à confidencialidade de informações individuais. A presente tese de doutorado aborda esta problemática ao propor dois métodos heurísticos de desagregação de dados, através de simulação geoestatística. Propõe-se empregar, como um input aos procedimentos, informações com alta disponibilidade, como, por exemplo, os microdados, coletados pelo censo demográfico. A diferença principal entre os métodos é que o primeiro não necessita de valores de dados provenientes de Pesquisa Origem/Destino do município de São Paulo, área de estudo deste trabalho. Ambas as abordagens, que podem ser aplicadas a outros diferentes estudos de caso, compreendem um procedimento alternativo para deconvolução de semivariogramas, Simulação Sequencial Gaussiana e validação, considerando malhas regulares de diferentes suportes. Os mapas e métricas estatísticas gerados comprovam que é possível desagregar dados, associados a Áreas de Ponderação de Setores Censitários (Método Proposto 1 – MP1) e a Zonas de Tráfego (Método Proposto 2 – MP2), através dos procedimentos aplicados. Além disso, este trabalho apresenta contribuições metodológicas ao viabilizar: a geração de diversos cenários que reproduzam o comportamento espacial da variável; e o estudo da incerteza associada às simulações. / Disaggregated data for travel demand are essential resources towards good urban planning, especially with regard to public transportation. However, the access to such data is limited due to high costs of collecting household data and due to individual information confidentiality. The present PhD dissertation addresses this issue by introducing two heuristic methods to disaggregate data using geostatistical simulation. It is proposed to employ, as input to the procedures, information with high availability, such as census microdata. The main difference between both methods rely on the fact that the first does not require data values of any Origin/Destination Survey of the São Paulo city, study area of this research. Both approaches, which can be applied to other different study cases, comprise an alternative procedure for semivariogram deconvolution, Sequential Gaussian Simulation and validation, using regular grids of various spatial scales. The resulting maps and statistical metrics corroborate that is possible to disaggregate data associated with a set of Census Tracts (Proposed Method 1 – MP1) and Traffic Analysis Zones (Proposed Method 2 – MP2). Besides, this dissertation presents relevant contributions as it enables: creating different scenarios to reproduce the spatial behavior of the study variable; and assessing the associated uncertainty.

Travel demand forecasting is essential for many decisions, such as infrastructure investments and policy measures. Traditionally travel demand modelling has considered trip frequency, mode, destination and route choice. This thesis considers two other choice dimensions, hypothesised to have implications for travel demand forecasting. The first part investigates how the increased possibilities to overcome space that ICT (information and communication technology) provides, can be integrated in travel demand forecasting models. We find that possibilities of modelling substitution effects are limited, irrespective of data source and modelling approach. Telecommuting explains, however, a very small part of variation in work trip frequency. It is therefore not urgent to include effects from telecommuting in travel demand forecasting. The results indicate that telecommuting is a privilege for certain groups of employees, and we therefore expect that negative attitudes from management, job suitability and lack of equipment are important obstacles. We find also that company benefits can be obtained from telecommuting. No evidences that telecommuting gives rise to urban sprawl is, however, found. Hence, there is ground for promoting telecommuting from a societal, individual and company perspective. The second part develops a departure time choice model in a mixed logit framework. This model explains how travellers trade-off travel time, travel time variability, monetary and scheduling costs, when choosing departure time. We explicitly account for correlation in unobserved heterogeneity over repeated SP choices, which was fundamental for accurate estimation of the substitution pattern. Temporal constraints at destination are found to mainly restrict late arrival. Constraints at origin mainly restrict early departure. Sensitivity to travel time uncertainty depends on trip type and intended arrival time. Given appropriate input data and a calibrated dynamic assignment model, the model can be applied to forecast peak-spreading effects in congested networks. Combined stated preference (SP) and revealed preference (RP) data is used, which has provided an opportunity to compare observed and stated behaviour. Such analysis has previously not been carried out and indicates that there are systematic differences in RP and SP data. / QC 20100825