A busca de soluções para a mobilidade urbana que minimizem a agressão do setor de tráfego e transportes ao meio ambiente está cada vez maior. Os veículos elétricos se posicionam como uma alternativa interessante, pois reduzem a emissão de gases poluentes na atmosfera, a poluição sonora e o consumo de petróleo. No entanto, sua limitada autonomia e a escassez de postos de recarga intimidam sua adoção. Por conta disso, políticas governamentais de incentivo têm sido desenvolvidas para a oferta de benefícios a quem optar por um veículo elétrico. Estima-se que dentro de poucas décadas toda a frota urbana será substituída por veículos dessa natureza. Por isso, é importante entender as mudanças no tempo de viagem e no consumo de energia oriundos da inclusão de veículos elétricos em cenários de tráfego. Trabalhos anteriores estudaram as diferenças entre os mecanismos internos de veículos convencionais e elétricos na determinação destas mudanças. Porém, dadas as características destes últimos, motoristas de veículos elétricos se preocupam com a economia de energia e podem optar por rotas diferentes. Logo, uma análise completa destes impactos deve considerar uma nova distribuição de tráfego. Este trabalho propõe um método biobjetivo de alocação de tráfego que considera o tempo de viagem e o consumo de energia para determinar a distribuição de veículos elétricos em cenários de tráfego urbano. Duas estratégias de distribuição de fluxo são propostas como mecanismos de escolha de rotas. Como parte da alocação de tráfego, é proposto um algoritmo biobjetivo de caminhos mínimos para veículos elétricos. A abordagem apresentada foi aplicada a três cenários distintos, onde percebeu-se uma diminuição de até 80% no consumo total de energia. Em cenários com congestionamento, observou-se um aumento de 10% no tempo de viagem. Já em cenários sem congestionamento o tempo de viagem diminuiu cerca de 2%. A recuperação de energia representa quase 6% da economia total dos veículos elétricos. Além disso, experimentos mostraram que investimentos na eficiência dos veículos elétricos podem resultar em uma economia de até 15% de energia. / The search for urban mobility solutions that minimize the aggression to the environment is increasing. Electric vehicles are an attractive alternative because they reduce greenhouse gas emissions, noise pollution, and oil consumption. However, their limited autonomy and the lack of charging stations restrict their popularization. Therefore, government incentive policies have been developed in order to offer benefits to those who choose an electric vehicle. It is estimated that the entire urban fleet will be replaced by these vehicles in a few decades. Therefore, it is important to understand the changes in travel time and energy consumption from the inclusion of electric vehicles in traffic scenarios. Previous works determined these changes by studying the differences between the internal engine of conventional and electric vehicles. However, given the characteristics of the latter, drivers of electric vehicles care about saving energy and may want to choose different routes. Thus, a complete analysis of these impacts should consider a redistribution of traffic. This work proposes a bi-objective traffic assignment method that considers the travel time and the energy consumption to determine the distribution of electric vehicles in urban traffic scenarios. We introduce two strategies for flow distribution as models of route choice. As a procedure of the traffic assignment method, we propose a bi-objective shortest path algorithm for electric vehicles. Our approach was applied to three different scenarios, which resulted in a decrease of up to 80% in total energy consumption. In congested scenarios, we observe an increase of about 10% in average travel time. In uncongested scenarios, travel time decreases about 2%. Energy recovery is almost 6% of the total savings of electric vehicles. Moreover, experiments have shown that investments in the efficiency of electric vehicles can result in up to 15% of energy savings.

<p>New technologies such as electric vehicles, Autonomous vehicles and transportation platforms are changing the way humanity move in a dramatic way and cities around the world need to adjust to this rapid change brought by technology. One of the aspects more challenging for urban planners is the parking problem as the new increase or desire for these private technologies may increase traffic congestion and change the parking requirements across the city. For example, Electric vehicles will need parking places for both parking and charging and Autonomous vehicles could increase the congestion by making longer trips in order to search better parking alternatives. Thus, it becomes essential to have clear, precise and practical models for transportation engineers in order to better represent present and future scenarios including normal vehicles, autonomous vehicles and electric vehicles in the context of parking and traffic alike. Classical network model such as traffic assignment have been frequently used for this purpose although they do not take into account essential aspects of parking such as fixed capacities, variety of users and autonomous vehicles. In this work a new methodology for modelling parking for multi class traffic assignment is proposed including autonomous vehicles and hard capacity constraints. The proposed model is presented in the classical Cournot Game formulation based on path flows and in a new link-node formulation which states the traffic assignment problem in terms of link flows instead of path flows. This proposed model allows for the creation of a new algorithm which is more flexible to model requirements such as linear constrains among different players flows and take advantage of fast convergence of Linear programs in the literature and in practice. Also, this link node formulation is used to redefine the network capacity problem as a linear program making it more tractable and easier to calculate. Numerical examples are presented across this work to better exemplify its implications and characteristics. The present work will allow planners to have a clear methodology for modelling parking and traffic in the context of multiusers which can represent diverse characteristics as parking time or type of vehicles. This model will be modified to take into account AV and the necessary assumptions and discussion will be provided.</p>

Para tomar decisiones sobre alternativas para reducir los efectos negativos de la congestión, se debe planificar el transporte. La planificación requiere, según el enfoque clásico, el conjunto secuencial de cuatro (04) modelos de transporte para tomar decisiones pertinentes. El cuarto y último modelo corresponde a la asignación de tráfico, el cual requiere de la función BPR. La función BPR (Bureau of Public Roads) estima el tiempo de viaje (Tf) respecto al flujo de transporte (V), a la capacidad (C), al tiempo de viaje en flujo libre (to) y a los parámetros (α y β), los cuales deben reflejan el desempeño de la infraestructura vial. En Lima Metropolitana, como en otras ciudades de Latinoamérica, suele emplearse valores estandarizados de α y β en la función BPR, esto significa que dichos valores no están ajustado a las condiciones actuales de la ciudad. A partir de ello, se genera incertidumbre en la estimación de los datos de salida de la función (tiempo de viaje) y en efecto, incertidumbre en la eficiencia de las decisiones para disminuir la congestión. El objetivo de la presente investigación es calibrar la función BPR, es decir, estimar los valores de α y β. Y, en consecuencia, asegurar que esta represente, de manera confiable, el comportamiento vial en Lima Metropolitana, específicamente en la vía colectora: Avenida San Luis. La metodología se desarrolla en tres (03) etapas: Levantamiento de información, determinación de los parámetros y análisis de los resultados. Como resultado de la calibración se obtiene valores de α=2.22 y β=1.19, y se demuestra que la función calibrada representa de forma adecuada los tiempos de viaje observados en la vía en estudio. En cambio, la utilización de valores estandarizados no resulta ser la mejor para dicha representación, y por el contrario, presenta errores significativos. / In order to take decisions about alternatives to reduce the congestion’s negative effects, transport must be planified. Planification requires, according to the classic approach, the four-model transport sequential set to take the pertinent decisions. The fourth and last model corresponds to the traffic allocation, that requires the BPR function. The BPR function (Bureau of Public Roads) estimates the travel time (Tf) regarding the transport’s flow (V), the capacity (C), the travel time in free flow (to) and the parameters (α y β), that reflect the performance of the road infraestructure. In the city of Lima, as in others Latinamerican cities, it is common to use standarised values of α and β in BPR function. That means that those values are not adjusted to the city’s current conditions. On that basis, uncertainty arises on the function of the data output (travel time) and also on the efficiency of the decisions taken to diminish the traffic congestion. The main approach of this investigation is to calibrate the BPR function, that means, to estimate the functions of α y β, and, because of it, ensure that this function represents the traffic behavoir in the city of Lima in a reliable way, specifically in the collector road: San Luis Avenue. The methodology is developed in three stages: gathering of information, determination of the parameters and analysis of the results. As a result of the calibration, the values obtained are α=2.22 and β=1.19, and it is demonstraded that the calibrated functions represent in a reliable way the travel times observed in the studied avenue. However, the use of standarised values is not the best one for such representation, and, on the contrary, it shows significative errors. / Tesis

In this study, a new SUE model using the Weibull random error terms is proposed as an alternative to overcome the drawbacks of the multinomial logit (MNL) SUE model. A path-size weibit (PSW) model is developed to relax both independently and identically distributed assumptions, while retaining an analytical closed-form solution. Specifically, this route choice model handles route overlapping through the path-size factor and captures the route-specific perception variance through the Weibull distributed random error terms. Both constrained entropy-type and unconstrained equivalent MP formulations for the PSW-SUE are provided. In addition, model extensions to consider the demand elasticity and combined travel choice of the PSW-SUE model are also provided. Unlike the logit-based model, these model extensions incorporate the logarithmic expected perceived travel cost as the network level of service to determine the demand elasticity and travel choice. Qualitative properties of these minimization programs are given to establish equivalency and uniqueness conditions. Both path-based and link-based algorithms are developed for solving the proposed MP formulations. Numerical examples show that the proposed models can produce a compatible traffic flow pattern compared to the multinomial probit (MNP) SUE model, and these models can be implemented in a real-world transportation network.

Connected vehicle is a fully connected transportation system in which vehicles, infrastructure, and mobile devices are enabled to exchange information in real-time to bring advancements in transportation operations. It is important to incorporate the new characteristics of the connected vehicle in the transportation planning process. Also, it is vital for planning and road agencies to better understand the impacts of connected vehicle on transportation networks, system demand, and travel behavior of road users in order to properly prepare for them. In addition, developers of connected vehicle systems can gain insight into how their systems will impact road users and network performance. When a change in performance of a transportation network occurs it can potentially cause users to change travel modes, known as mode choice. In this research, the change in mode choice, due to the change in network performance by introduction of connected vehicle is studied. This provides a more accurate depiction of the performance of the network and indicates how connected vehicles could change travellers’ preference in travel mode. The effect of this technology is explored on the performance of the Toronto waterfront, in a microsimulation environment. The results show that average travel time increases for high market penetrations when a dynamic route guidance algorithm is implemented, a phenomenon that occurs in dense, and complex traffic networks. Analysis of mode choice shows a loss in the auto mode share, for high market penetrations, due to the increased auto travel times. This loss in the auto mode share is compensated by increases in the other modes. / Thesis / Master of Applied Science (MASc)

Disasters, ranging from manmade events to natural occurrences, can happen anywhere on the planet, and their consequences can range from economic loss to catastrophic loss of life. Determining how the transportation system fares in the face of these disasters is important so that proper planning can take place before, rather than after, an event has happened. Modeling the transportation system gives operators the ability to discover bottlenecks, to determine the possible benefit of using lane reversals, and to find out the influence of evacuation speed on system efficiency. Models have already been created that are able to model some of these types of disasters with some level of accuracy. These models range from microscopic simulation to regional, macroscopic models. This research examines how an off-the-shelf regional modeling software package, TransCAD, can be used to model emergency evacuations. More specifically, this thesis presents four case studies involving three different types of disasters in Western Massachusetts. Because this research documents a first-hand experience using TransCAD in emergency evacuation planning, the results give regional modelers the ability to modify their models to fit their specific region. These case studies demonstrate how the modified inputs and existing portions of the four-step transportation planning model can be used in place of the usual data demands of the software. Dynamic traffic assignment is used in three of the case studies while the fourth case study uses static traffic assignment. An evaluation of the software package along with lessons learned is provided to measure the performance of the software.

Urban transportation networks are vulnerable to various incidents. In order to combat the negative effects due to incident-related congestion, various mitigation strategies have been proposed and implemented. The effectiveness of these congestion mitigation strategies for incident conditions largely depends on the accuracy of information regarding network conditions. Therefore, an efficient and accurate procedure to determine the link states, reflected by flows and density over time, is essential to incident management. This thesis presents a user equilibrium Dynamic Traffic Assignment (DTA) model that incorporates the Cell Transmission Model (CTM) to evaluate the temporal variation of flow and density over links, which reflect the link states of a transportation network. Encapsulation of the CTM equips the model with the capability of accepting inputs of incidents like duration and capacity reduction. Moreover, the proposed model is capable of handling multiple origin-destination (OD) pairs. By using this model, the temporal variation of flows over links can be readily evaluated. The visualized prediction of link density variations is used to investigate the link state relationships. By isolating the effects of an incident, the parallel routes of a specific OD pair display the relationship of substituting for each other, which is consistent with the general expectation regarding such parallel routes. A closer examination of the density variations confirms the existence of a substitution relationship between the unshared links of the two parallel routes. This information regarding link state relationship can be used as general guidance for incident management purposes. / Master of Science

京都大学 / 新制・課程博士 / 博士(工学) / 甲第24217号 / 工博第5045号 / 新制||工||1788(附属図書館) / 京都大学大学院工学研究科都市社会工学専攻 / (主査)教授 山田 忠史, 教授 藤井 聡, 准教授 SCHMOECKER Jan-Dirk / 学位規則第4条第1項該当 / Doctor of Philosophy (Engineering) / Kyoto University / DFAM

Departments of Transportation (DOTs) are responsible for keeping their road network in a state of good repair while also aiming to reduce congestion through the implementation of different traffic control and demand management strategies. These strategies can result in changes in traffic volume distributions, which in turn affect the level of pavement deterioration due to traffic loading. To address this issue, this dissertation introduces an integrated simulation-optimization framework that accounts for the combined effects of pavement conditions and traffic management decision-making strategies. The research focuses on exploring the range of possible performance outcomes resulting from this integrated modeling approach. The research also applied the developed framework to a particular traffic demand management strategy and assessed the impact of dynamic tolls around the specific site of I-66 inside the beltway. The integrated traffic-management/pavement-treatment framework was applied to address both the operational and pavement performance of the network. Aimsun hybrid macro/meso dynamic user equilibrium experiments were used to simulate the network with a modified cost function taking care of the dynamic pricing along the I-66 tolled facility. Furthermore, the framework was expanded to include the development of a systematic and comprehensive methodology to optimize the allocation of networkwide pavement treatment work zones over space and time. The proposed methodology also contributed to the development of a surrogate function that reduces the optimization computation burden so that researchers would be able to conduct work zone allocation optimization without having to run expensive simulation work. Finally, in this dissertation, a user-friendly decision-support tool was developed to assist in the pavement treatment and project selection planning process. We use machine learning models to encapsulate the simulation optimization process. / Doctor of Philosophy / Departments of Transportation (DOTs) are responsible for keeping their road network in a state of good repair. Improvement in pavement rehabilitation plans can lead to savings in the order of tens of millions of dollars. Pavement rehabilitation plans result in work zone schedules on the transportation network. Limited roadway capacities due to work zones affect traffic assignments and routing on the roads, which impacts the selection of optimal operation strategies to manage the resulting traffic. On the other hand, the choice of any particular operation and routing strategy will result in different distributions of traffic volumes on the roads and affect the pavement deterioration levels due to traffic loading, leading to other optimal rehabilitation plans and corresponding work zones. While there have been several research efforts on infrastructure condition assessment and other research efforts on traffic control and demand management strategies, there is a wide gap in the nexus of the two fields. To address this issue, this dissertation introduces an integrated simulation-optimization framework that accounts for the combined effects of pavement conditions and traffic management decision-making strategies. The research focuses on exploring the range of possible performance outcomes resulting from this integrated modeling approach. The research also applied the developed framework to a particular traffic demand management strategy and assessed the impact of dynamic tolls around the specific site of I-66 inside the beltway. The integrated traffic-management/pavement-treatment framework was applied to address both the operational and pavement performance of the network. Furthermore, the framework was expanded to include the development of a systematic and comprehensive methodology to optimize the allocation of networkwide pavement treatment work zones over space and time. The proposed methodology also contributed to the development of a surrogate function that reduces the optimization computation burden so that researchers would be able to conduct work zone allocation optimization without having to run expensive simulation work. Finally, in this dissertation, a user-friendly decision-support tool was developed to assist in the pavement treatment and project selection planning process. We use machine learning models to encapsulate the simulation optimization process.

This dissertation addresses the development and analysis of optimization models for evacuation planning. Specifically we consider the cases of large-scale regional evacuation using household vehicles and hospital evacuation. Since it is difficult to estimate the exact number of people evacuating, we first consider the case where the population size is uncertain. We review the methods studied in the literature, mainly the strategy of using a deterministic counterpart, i.e., a single deterministic parameter to represent the uncertain population, and we show that these methods are not very effective in generating a good traffic management strategy. We provide alternatives, where we describe some networks where an optimal policy exist independent of the demand realization and we propose some simple heuristics for more complex ones. Next we consider the traffic management tools that can be generated from an evacuation plan. We start by introducing the cell transmission model with flow reduction. This model captures the flow reduction after the onset of congestion. We then discuss the management tools that can be extracted from this model. We also propose some simplification to the model formulation to enhance its tractability. A heuristic for generating a solution is also proposed, and its solution quality is analyzed. Finally, we discuss the hospital evacuation problem where we develop an integer programming model that integrates the building evacuation with the transportation of patients. The impact of building evacuation capabilities on the transportation plan is investigated through the case of a large regional hospital case study. We also propose a decomposition scheme to improve the tractability of the integer program. / Ph. D.