Transportation engineering has taken upon a new role; to empower the alternative modes of travel: walking, biking, and bus transit. In this new era, engineers are rethinking a network designed predominately for the automobile. The ultimate goal of this research is to create a process that can make a vehicle dominant corridor a desirable, livable thoroughfare by livability design and context sensitive performance measures. Balancing travel modes requires an account of vehicular traffic and the impact of reconfiguring existing conditions. The analysis herein is conducted by field data collection, transportation equations and microsimulation. Simulating traffic behavior will be the means to apply livable alternatives comparable to existing Southeast Florida conditions. The results herein have shown that micro-simulation can be utilized in transportation planning to reveal good livability alternatives. / Includes bibliography. / Thesis (M.S.)--Florida Atlantic University, 2015 / FAU Electronic Theses and Dissertations Collection

Nowadays it is very hard to find available spots in public parking lots and even harder at facilities such as universities and sports venues. A system that provides drivers with parking availability and parking lot occupancy will allow users find a parking space much easier and faster. This thesis presents a system for automatic parking lot occupancy computation using motion tracking. The use of computer vision techniques and low cost video sensors makes it possible to have an accurate system that allows drivers to find a parking spot. Video bitrate and quality reduction and its impact on performance were studied. It was concluded that high quality video is not necessary for the proposed algorithm to obtain accurate results. The results show that relatively inexpensive and low bandwidth networks can be used to develop large scale parking occupancy applications. / by Francisco Alberto Justo Torres. / Thesis (M.S.C.S.)--Florida Atlantic University, 2013. / Includes bibliography. / Mode of access: World Wide Web. / System requirements: Adobe Reader.

Esta dissertação propõe um modelo de caracterização de recursos energéticos do lado da demanda que pretende abranger todos os tipos de ações e medidas referentes ao gerenciamento da demanda e à conservação energética, avaliando-as de forma holística, dentro de quatro dimensões: técnico-econômica, ambiental, social e política. Os recursos considerados na composição do modelo são submetidos às fases de Inventário de Recursos Energéticos do Lado da Demanda, Caracterização das Dimensões e Atributos de Avaliação de Recursos, Formulação dos Potenciais Energéticos e Aplicação do Modelo em Caso Piloto. O Inventário de Recursos Energéticos do Lado da Demanda pretende levantar, descrever e segmentar todas as alternativas energéticas de GLD e setores de consumo energético. A Caracterização da Avaliação de Recursos delimita as esferas de análise de recursos e a caracterização de seus atributos quanto a custos e benefícios provenientes de seu emprego. A Formulação dos Potenciais Energéticos define seu cálculo a partir de sua amplitude e restrições gerais de aplicação. O modelo é aplicado em um estudo piloto, na Região Administrativa de Araçatuba, por meio de um exercício de formulação do PIR, considerando a utilização de ferramentas de Avaliação dos Custos Completos, Cálculo de Potenciais Energéticos e Elaboração de Cenários de Projeção do Consumo Energético para um horizonte de tempo determinado. / This work presents a model for the characterization and assessment of demand side energy resources, which intends to comprehend every kind of action or measure related to demand side management and energy conservation, evaluated thouroughly into four dimensions: technical-economical, environmental, social and political. The resources included in the composition of the model are subjected to the stages of Inventary of Demand Side Resources, Characterization of Dimensions and Attributes of the Resources Assessment, Formulation of Energy Potentials and Model Application on a Real Case Study. The Inventary of Demand Side Resources intends to enlist, describe and segment all demand side management energy alternatives and their application in different economy sectors. The Characterization of the Resources Assessment defines the spheres of the assessment of resources and the characterization of its attributes as for costs and benefits resulting from their utilization. The Formulation of Energy Potentials defines their accounting based on their range and general application restrictions. The Model is applied in a case study, in the Administrative Region of Araçatuba, through an exercise of IRP formulation, regarding the utilization of Full Cost Accounting, Energy Potentials Accounting and Scenarios of Energy Consumption for a determined horizon.

A contínua ampliação da infraestrutura viária já se provou ineficaz em resolver os problemas de congestionamento nos grandes centros urbanos e uma solução não sustentável tendo em vista os altos investimentos envolvidos. Em contraposição ao tradicional aumento da capacidade viária, surge a Gestão da Demanda de Viagens (GDV). Uma das medidas englobadas pela GDV é a mobilidade corporativa que visa a promover o uso de opções de transporte mais sustentáveis e eficientes nos deslocamentos casa-trabalho das pessoas. Aproximadamente 50% dos deslocamentos diários nas cidades brasileiras ocorrem por motivo de trabalho. Por isso organizações públicas e privadas desempenham um papel fundamental em questões atreladas ao transporte. Embora não controlem a forma como seus funcionários vão ao trabalho, as organizações, por muitas vezes, podem estimular a mudança de hábitos de deslocamento ao prover informações e incentivos para isso. Este trabalho tem por objetivo verificar a aplicabilidade da adoção de estratégias de mobilidade corporativa em organizações situadas no Brasil. Em um primeiro momento, apresenta-se mais profundamente o conceito, os benefícios e as medidas de mobilidade corporativa que podem ser implementadas. A seguir, são comparados e analisados diferentes métodos existentes para a elaboração de planos de mobilidade corporativa. Propõe-se um novo método adaptado à realidade local composto por sete passos que totalizam 26 atividades que devem ser cumpridas para a construção de um plano bem-sucedido. Realiza-se uma pesquisa-ação em um complexo administrativo que reúne 17 mil funcionários e está localizado a 20 km do centro da cidade. Investiga-se o padrão de deslocamento casa-trabalho dos funcionários da organização e são analisadas que medidas de mobilidade corporativa poderiam ser adotadas para estimular o transporte sustentável entre os funcionários deste local. / The continuous expansion of road infrastructure has proven to be ineffective in solving the problem of congestion in large urban areas. It is also an unsustainable solution due to the high investments involved. Instead of increasing road capacity, Travel Demand Management (TDM) emerges as an alternative to deal with the problem. One of TDM’s measures is corporate mobility, which aims to promote more sustainable and efficient transport options for commuting to work. Approximately 50% of daily trips in Brazilian cities are work related. Therefore public and private organizations play a key role in issues related to transportation. Although they do not control how employees commute to work, organizations have the ability to stimulate travel behavior change by providing information and incentives. This study aims to verify the applicability of the adoption of corporate mobility strategies in organizations located in Brazil. At first, a more in depth concept is explained in addition to the benefits and corporate mobility measures that can be implemented. Then, we compare and analyze different existing methods for the construction of corporate mobility plans. We propose a new method based on the local context. The method is composed by seven steps totalizing 26 activities which must be followed for developing of a successful plan. Finally, we describe the application of the method that was carried out in an Administrative Center which gathers 17 thousand employees and is located 20 km away from the city center. We investigate employees’ commute patterns and analyze which corporate mobility measures could be implemented to promote a more sustainable commute pattern to work.

Nos dias de hoje, o processo de gestão da demanda vem tornando-se cada vez mais importante num mercado competitivo como o brasileiro. Isto se deve a sua capacidade de ao mesmo tempo integrar a manufatura das empresas às necessidades de seus clientes, reduzir custos, melhorar o relacionamento com os clientes e organizar melhor o chão de fábrica e os estoques, entre outros. Neste contexto, muitas empresas estão buscando novas formas de tornar mais eficiente a gerência e operacionalização deste processo. O e-business surge como um facilitador da gestão da demanda em um ambiente colaborativo. O planejamento colaborativo da demanda é uma das formas de tornar o processo da gestão da demanda mais eficiente. Assim, o objetivo desse trabalho é contribuir ao tema com o estudo do impacto do e-business no processo de gestão da demanda, utilizando um modelo de referência para isto. / Nowadays, the demand management business process has been widely discussed in a competitive market as in Brazil. At the same time, it has a capacity of integrate the manufacture of the companies to the customer necessities, reduce costs, improve the relationship with clients and better orgazize the shop floor and inventory, among others. In this context, many companies are looking for new ways to become more efficient the operation and management of this process. The e-business facilitate the demand management in a collaborative environment. The demand collaborative planning is a way to become the demand management more eficient. The objective of this research is analize the impact of e-business in the demand management process, using a reference model.

Today, numerous alternative modes of mobility are emerging to provide a solution to the problems created by the automobile. This research envisions a future where transportation in urban areas will be dominated by small personal mobility devices (PMDs) instead of automobiles. This Intelligent Mobility System (IMS) would be a car-free zone where people travel by a shared-system of PMDs providing levels of mobility greater than walking but less than a car. This research effort focuses on the operational aspects of this future system by studying PMD performance characteristics as inputs for a computer simulation model of an IMS environment. Therefore, the primary objective of this research is to evaluate the operations of PMDs that are currently used in a variety of settings. GPS recorders are used to log speed and location data each second of pedestrian, bicycle, Segway, and electric cart trips. Segway speed and acceleration are analyzed using three factors, sidewalk width, surface quality, and pedestrian density to study their effect on Segway speed. Pedestrians have the lowest mean speed and the most narrow speed distribution. Segways, bicycles and electric carts have increasingly faster mean speeds and wider speed distributions, respectively. Segways and bicycles were found to have similar acceleration distributions. Segways seem to provide a level of speed and mobility between that of pedestrians and cyclists, meaning that Segways might capture new users by providing a level of mobility and convenience previously unseen. Narrow sidewalk widths, poor sidewalk quality, and heavy pedestrian density all decreased Segway speeds. The researchers suspect that surface quality is likely an independent constraint for Segway speed and that sidewalk width and pedestrian density interact to limit Segway speeds under certain conditions. This research concludes that these external factors may affect PMD speed and should be considered when analyzing PMD mobility, especially in an IMS setting.

Suburban growth in the U.S. urban regions has been defined by large subdivisions of single-family detached units. This growth is made possible by the mobility supported by automobiles and an extensive highway network. These dispersed and highly automobile-dependent developments have generated a large body of work examining the socioeconomic and environmental impacts of suburban growth on cities. The particular debate that this study addresses is whether suburban residents are more energy intensive in their travel behavior than central city residents. If indeed suburban residents have needs that are not satisfied by the amenities around them, they may be traveling farther to access such services. However, if suburbs are becoming like cities with a wide range of services and amenities, travel might be contained and no different from the travel behavior of residents in central areas. This paper will compare the effects of long term suburban growth on travel behavior, energy consumption, and GHG emissions through a case study of neighborhoods in central Phoenix and the city of Gilbert, both in the Phoenix metropolitan region. Motorized travel patterns in these study areas will be generated using 2001 and 2009 National Household Travel Survey (NHTS) data by developing a four-step transportation demand model in TransCAD. Energy consumption and GHG emissions, including both Carbon Dioxide (CO₂) and Nitrous Oxide (N₂O) for each study area will be estimated based on the corresponding trip distribution results. The final normalized outcomes will not only be compared spatially between Phoenix and Gilbert within the same year, but also temporally between years 2001 and 2009 to determine how the differential land use changes in those places influenced travel. The results from this study reveal that suburban growth does have an impact on people's travel behaviors. As suburbs grew and diversified, the difference in travel behavior between people living in suburban and urban areas became smaller. In the case of shopping trips the average length of trips for suburban residents in 2009 was slightly shorter than that for central city residents. This convergence was substantially due to the faster growth in trip lengths for central city compared to suburban residents in the 8-year period. However, suburban residents continue to be more energy intensive in their travel behavior, as the effect of reduction in trip length is likely to be offset by the more intensive growth in trip frequency. Additionally, overall energy consumption has grown significantly in both study areas over the period of study.

Traffic congestion has been placing an extremely high burden on the development of modern cities. Congestion can be alleviated by either increasing road capacity, or by reducing traffic demand. For decades, increasing capacity by building more roads and lanes has been the major solution applied to accommodate the ever-growing traffic demand. However, it turns out to be of limited effect due to some well-known phenomenon such as latent demand. Controlling and managing traffic demand has in turn been viewed as a cost-effective alternative to increasing road capacity, as has been demonstrated many successful applications all around the world. Within the concept framework of Traffic Demand Management (TDM), Active Transportation and Demand Management (ATDM) is the dynamic management, control, and influence of traffic demand and traffic flow of transportation facilities. ATDM strategies attempt to influence traveler behavior and further manage traffic flow in a time-dependent manner within the existing infrastructure Successful ATDM applications include congestion pricing, adaptive ramp metering, dynamic speed limits, dynamic lane use control, etc. Singapore stands out to be an excellent success story of ATDM, as the implementations of "Cap and Trade" license plates and electronic road pricing make motoring a high cost privilege for citizens of Singapore, making the public relies on transit. Monetary leverage is an effective instrument to facilitate ATDM. Examples of ATDM applications adopting monetary instrument includes dynamic congestion pricing, "Cap and Trade" of car licenses, etc. Taking congestion pricing as an example, policy makers are inducing travelers' behavior and alternating their preferences towards different behavior decisions by levying price tags to different choices. As an important underpinning of rationing choice theory, an individual assigns an ordinal number over the available actions and this ordinal number is calculated by their utility function or payoff function. The individual's preference is expressed as the relationship between those ordinal assignments. In the implementation of congestion pricing, policy makers are imposing an additional high disutility to congested roads and therefore pushing some of the travelers to take alternative routes or shift to alternative departure times or even cancel the trips. However, congestion pricing suffers from public aversion as it creates burden on the motoring of low-income people and therefore doesn't help to alleviate social inequality. The concept of Tradable Mobility Credit (TMC) has been proposed by a group of researchers as another innovative application to facilitate dynamic traffic demand management and solve social inequality issues using pricing instruments. The concept of TMC is borrowed from carbon trading in environmental control. A limited quota of personal auto usage is issued to eligible travelers and credits can be traded in a free market fashion. This guarantees that the roadway usage does not exceed capacity while avoiding the negative effects of shortages normally associated with quotation systems. TMC is literally not a market-ready policy as the integration of the supporting infrastructures, including the trading market, the credit assignment component, and the credit charging component, has not been fully explored yet. Existing TMC research focuses on explaining and exploring the equilibrium condition through analytical methods such as mathematical modeling. Analytical models produce perfect convergence curves and deterministic equilibrium traffic flow patterns. Analytical models provide influential guidance for further works but the solution procedure may encounter problems when dealing with larger real world networks and scenarios. Meantime, current analytical models don't consider the microstructure of the credit trading market sufficiently while it's actually the most unique component of TMC system. Motivated by those concerns, an integrated TMC evaluation platform consisting of a policy making module and traveler behavior modules are proposed in this research. The concept of Agent-Based Modeling and Simulation (ABMS) is extensively adopted in this integrated platform as each individual traveler carries his/her personal memory across iterations. The goal of establishing this framework is to better predict a traveler's route choice and trading behavior if TMC is imposed and further provide intelligence to potential policy makers' decision making process. The proposed integrated platform is able to generate results at different aggregation levels, including both individual level microscopic behavior data as well as aggregated traffic flow and market performance data. In order to calibrate the proposed integrated platform, an online interactive experiment is designed based on an experimental economic package and a human research element with 22 participants has been conducted on this experiment platform to gather field data regarding a real person's route choice behavior and credit trading behavior in an artificial TMC system. Participants are recruited from forum, listserve, social media, etc. The calibrated platform is proved to have the ability to predict travelers' behavior accurately. A prototype market microstructure is proposed in this research as well and it is proved to be a cost-effective setting and resulted to a vast amount of economic saving given the fact that travelers would behave similar to the prediction generated by traveler behavior module. It's also demonstrated that the principle of Pareto-improving is not achieved in the proposed ABMS models.

Traffic congestion has been imposing a tremendous burden on society as a whole. For decades, the most widely applied solution has been building more roads to better accommodate traffic demand, which turns out to be of limited effect. Active Traffic and Demand Management (ATDM) is getting more attention recently and is considered here, as it leverages market-ready technologies and innovative operational approaches to manage traffic congestion within the existing infrastructure. The key to a successful Active Traffic and Demand Management strategy is to effectively induce travelers' behavior to change. In spite of the increased attention and application throughout the U.S. or even the world, most ATDM strategies were implemented on-site through small-scale pilot studies. A systematic framework for analysis and evaluation of such a system in order to effectively track the changes in travelers' behavior and the benefit brought about by such changes has not been established; nor has the effect of its strategies been quantitatively evaluated. In order to effectively evaluate the system benefit and to analyze the behavior changes quantitatively, a systematic framework capable of supporting both macroscopic and microscopic analysis should be established. Such system should be carefully calibrated to reflect the traffic condition in reality, as only after the calibration can the baseline model be used as the foundation for other scenarios in which alternative design or management strategies are incorporated, so that the behavior changes and system benefit can be computed accurately by comparing the alternative scenarios with the baseline scenario. Any effective traffic management strategy would be impossible if the traveler route choice behavior in the urban traffic network has not been fully understood. Theoretical research assumes all users are homogeneous in their route choice decision and will always pick the route with the shortest travel cost, which is not necessarily the case in reality. Researchers in Minnesota found that only 34% of drivers strictly traveled on the shortest path. Drivers' decision is made usually based on several dimensions, and a full understanding of the travel route choice behavior in the urban traffic network is essential. The existence of most current Advanced Traveler Information Systems (ATIS) offer the capability to provide pre-trip and/or en route real time information, allowing travelers to quickly assess and react to unfolding traffic conditions. The basic design concept is to present generic information to drivers, leaving drivers to react to the information their own way. This "passive" way of managing traffic by providing generic traffic information has difficulty in predicting outcome and may even incur adverse effect, such as overreaction (aka herding effects). Furthermore, other questions remain on how to utilize the real-time information better and guide the traffic flow more effectively towards a better solution, and most current research fails to take the traveler's external cost into consideration. Motivated by those concerns, in this research, a behaviorally induced system optimal model is presented, aimed at further improving the system-level traffic condition towards System Optimal through incremental routing, as well as establishing the analysis methodology and evaluation framework to calibrate quantitatively the behavior change and the system benefits. In this process, the traffic models involved are carefully calibrated, first using a two-stage calibration model which is capable of matching not only the traffic counts, but also the time dependent speed profiles of the calibrated links. To the best of our knowledge, this research is the first with a methodology to incorporate the use of field observed data to estimate the Origin-Destination (OD) matrices departure profile. Also proposed in this dissertation is a Constrained K Shortest Paths algorithm (CKSP) that addresses route overlap and travel time deviation issues. This proposed algorithm can generate K Shortest Paths between two given nodes and provide sound route options to the drivers in order to assist their route choice decision process. Thirdly, a behaviorally induced system optimal model includes the development of a marginal cost calculation algorithm, a time-dependent shortest path search algorithm, and schedule delay as well as optimal path finding models, is present to improve the traffic flow from an initial traffic condition which could be User Equilibrium (UE) or any other non-UE or non-System-Optimal (SO) condition towards System Optimal. Case studies are conducted for each individual research and show a rather promising result. The goal of establishing this framework is to better capture and evaluate the effects of behaviorally induced system optimal traffic management strategies on the overall system performance. To realize this goal, the three research models are integrated in order to constitute a comprehensive platform that is not only capable of effectively guiding the traffic flow improvement towards System Optimal, but also capable of accurately evaluating the system benefit from the macroscopic perspective and quantitatively analyzing the behavior changes microscopically. The comprehensive case study on the traffic network in Tucson, Arizona, has been conducted using DynusT (Dynamic Urban Simulation for Transportation) Dynamic Traffic Assignment (DTA) simulation software; the outcome of this study shows that our proposed modeling framework is promising for improving network traffic condition towards System Optimal, resulting in a vast amount of economic saving.

This thesis examines transportation planning at the University of Manitoba Fort Garry campus with the view to improving efficiency, equity and reducing economic loss. Through a broad approach of Transportation Demand Management (TDM) a number of avenues are explored including a comprehensive literature review of sustainable transportation planning; the documentation of selected university TDM programs including University of Colorado, University of British-Columbia and the University of Ottawa; a University of Manitoba commuter web survey, and key informant interviews. Cost-benefit analysis, geographical information systems and key informants interviews are used. Twelve key recommendations are outlined in the concluding chapter. The research suggests optimal solutions can be reached if there is strong leadership from the University of Manitoba central administration in Transportation Demand Management (TDM) including a more collaborative approach to transportation and land use planning, as well as working closely with its stakeholders in reforming current practices. A series of incremental changes can give higher priority to walking, cycling, transit, and car pooling ahead of those driving alone resulting in a more equitable and efficient transportation system and leading to a healthier population and a healthier environment for the University of Manitoba community. The author can be contacted by email at tompearce@hotmail.com