In a lot of big cities, the traffic network is overloaded, with congestion and unnecessary emissions as consequence. Therefore, different traffic control methods are useful, especially in case of an incident. One key problem for traffic control is traffic prediction and the aim of this thesis is to develop, calibrate and evaluate a route flow model using only observed travel times and travel demand as input. The route flow model was used to calculate the metric link flow destination distribution, that presents to which destinations the travelers on a link are going in percentage.

Pedestrian walking and choice behavior presented was first studied by Fruin in 1971, and since then a lot of research have been carried out in order to understand how humans move and what does make them make choices and obtain certain patterns. In relation to pedestrians, a significant bottleneck inside public stations evaluated by research are the level-change facilities, as Stair Walks and Escalators. The aim of this research is studying how pedestrian behave in the vicinity to stairways and escalators, and how does that affect pedestrian choice, speed and acceleration when choosing one of the two facilities. Also, with a need for more data on pedestrian traffic, further data collection is a big requirement to analyze their behavior and use as tools in future measures. At last, how to optimize the movement of pedestrians in relation to level changes, considering the effects of the movements observed. Two case studies were analysed, Stockholm Central Station and Uppsala Central Station.The study compares data collection methods, tracking methods and previous studies to better fit the scope of this research. The data is backed up from previous research and explains which method better fitted the options available. As a result, video data collection was chosen to collect the data, a semi-automatic tracking software called T-analyst was used to extract speed, trajectories and acceleration from the videos, and microsimulation modelling from VISSIM further investigated different design options to optimize the overall performance and improve travel time in the same area. The analysis found out that there was a possibility to increase the overall performance of the location in higher flow levels, where the most significant queues could be seen, since there was the possibility to achieve higher speeds by modifying the width and position of the stair walks, which allow for a smaller queue in both directions.

This research investigates route choice behavior in networks with risky travel times and real-time information. A stated preference survey is conducted in which subjects use a PC-based interactive maps to choose routes link-by-link in various scenarios. The scenarios include two types of maps: the first presenting a choice between one stochastic route and one deterministic route, and the second with real-time information and an available detour. The first type measures the basic risk attitude of the subject. The second type allows for strategic planning, and measures the effect of this opportunity on subjects' choice behavior. Results from each subject are analyzed to determine whether subjects planned strategically for the en route information or simply selected fixed paths from origin to destination. The full data set is used to estimate route choice models that account for both risk attitude and strategic thinking. Estimation results are used to assess whether models that incorporate strategic behavior more accurately reflect route choice than do simpler path-based models.

This paper explores how cities have integrated formal planning into improving public space. Through a review of literature on the topic, this the paper identifies the potential design has to renovate narrow streets and alleys, within the public right of way. By preforming an assessment of plans and programs, this paper identifies the common themes or components that have been used by planners, architects, and engineers to improve the urban environment for pedestrians. Based on this information, a pilot study was created to measure the influence the most common components have on pedestrian route choice. The results are then compared to the information gathered from the assessed plans and programs. Suggestions for expanding the pilot study and other recommendations are presented upon the conclusion of this report.

Aprendizagem por reforço multiagente (do inglês, MARL) é uma tarefa desafiadora em que agentes buscam, concorrentemente, uma política capaz de maximizar sua utilidade. Aprender neste tipo de cenário é difícil porque os agentes devem se adaptar uns aos outros, tornando o objetivo um alvo em movimento. Consequentemente, não existem garantias de convergência para problemas de MARL em geral. Esta tese explora um problema em particular, denominado escolha de rotas (onde motoristas egoístas deve escolher rotas que minimizem seus custos de viagem), em busca de garantias de convergência. Em particular, esta tese busca garantir a convergência de algoritmos de MARL para o equilíbrio dos usuários (onde nenhum motorista consegue melhorar seu desempenho mudando de rota) e para o ótimo do sistema (onde o tempo médio de viagem é mínimo). O principal objetivo desta tese é mostrar que, no contexto de escolha de rotas, é possível garantir a convergência de algoritmos de MARL sob certas condições. Primeiramente, introduzimos uma algoritmo de aprendizagem por reforço baseado em minimização de arrependimento, o qual provamos ser capaz de convergir para o equilíbrio dos usuários Nosso algoritmo estima o arrependimento associado com as ações dos agentes e usa tal informação como sinal de reforço dos agentes. Além do mais, estabelecemos um limite superior no arrependimento dos agentes. Em seguida, estendemos o referido algoritmo para lidar com informações não-locais, fornecidas por um serviço de navegação. Ao usar tais informações, os agentes são capazes de estimar melhor o arrependimento de suas ações, o que melhora seu desempenho. Finalmente, de modo a mitigar os efeitos do egoísmo dos agentes, propomos ainda um método genérico de pedágios baseados em custos marginais, onde os agentes são cobrados proporcionalmente ao custo imposto por eles aos demais. Neste sentido, apresentamos ainda um algoritmo de aprendizagem por reforço baseado em pedágios que, provamos, converge para o ótimo do sistema e é mais justo que outros existentes na literatura. / Multiagent reinforcement learning (MARL) is a challenging task, where self-interested agents concurrently learn a policy that maximise their utilities. Learning here is difficult because agents must adapt to each other, which makes their objective a moving target. As a side effect, no convergence guarantees exist for the general MARL setting. This thesis exploits a particular MARL problem, namely route choice (where selfish drivers aim at choosing routes that minimise their travel costs), to deliver convergence guarantees. We are particularly interested in guaranteeing convergence to two fundamental solution concepts: the user equilibrium (UE, when no agent benefits from unilaterally changing its route) and the system optimum (SO, when average travel time is minimum). The main goal of this thesis is to show that, in the context of route choice, MARL can be guaranteed to converge to the UE as well as to the SO upon certain conditions. Firstly, we introduce a regret-minimising Q-learning algorithm, which we prove that converges to the UE. Our algorithm works by estimating the regret associated with agents’ actions and using such information as reinforcement signal for updating the corresponding Q-values. We also establish a bound on the agents’ regret. We then extend this algorithm to deal with non-local information provided by a navigation service. Using such information, agents can improve their regrets estimates, thus performing empirically better. Finally, in order to mitigate the effects of selfishness, we also present a generalised marginal-cost tolling scheme in which drivers are charged proportional to the cost imposed on others. We then devise a toll-based Q-learning algorithm, which we prove that converges to the SO and that is fairer than existing tolling schemes.

For a number of reasons--congestion, public health, greenhouse gas emissions, energy use, demographic shifts, and community livability to name a few--the importance of walking and bicycling as transportation options will only continue to increase. Currently, policy interest and infrastructure funding for nonmotorized modes far outstrip our ability to model bike and walk travel. To ensure scarce resources are used most effectively, accurate models sensitive to key policy variables are needed to support long-range planning and project evaluation, and to continue adding to our growing understanding of key factors driving walk and bike behavior. This research attempts to synthesize and advance the state of the art in trip-based, nonmotorized mode choice modeling. Over the past fifteen years, efforts to model the decision to walk or bike on a given trip have been hampered by the lack of a comprehensive behavioral framework and inconsistency in measurement scales and model specification. This project develops a mode choice behavioral framework that acknowledges the importance of attributes along the specific walk and bike routes that travelers are likely to consider, in addition to more traditional area-based measures of travel environments. The proposed framework is applied to a revealed preference, GPS-based travel dataset collected from 2010-2013 in Portland, Oregon. Measurement of nonmotorized trip distance, built environment, tour-level variables, and attitudinal attributes as well as mode availability are explicitly addressed. Route and mode choice models are specified using discrete choice techniques, and predicted walking and bicycling routes are tested as inputs to various mode choice models. Results suggest strong potential for predicted route measures to enhance walk and bicycle mode choice modeling. Findings also support the specific notion that bicycle and pedestrian infrastructure contribute not only to route choice but also to the choice of whether to bike or walk. For decisions to bicycle, availability of low-traffic routes may be particularly important to women. Model results further indicate that land use and built environments around trip ends and a person’s home still have important effects on nonmotorized travel when controlling for route quality. Both route and area travel environment impacts are mostly robust to the inclusion of residential self-selection variables, consistent with the idea that built environment differences matter even for households that choose to live in a walkable or bikeable neighborhood. The combination of area and route-based built environment measures alongside trip context, sociodemographic, and attitudinal attributes provides a new perspective on nonmotorized travel behavior relevant to both policy and practice.

This thesis develops quantitative methods which incorporate transportation modeling for tactical and operational home health logistics planning problems. We define home health nurse routing and scheduling (HHNRS) problems, which are dynamic periodic routing and scheduling problems with fixed appointment times, where a set of patients must be visited by a home health nurse according to a prescribed weekly frequency for a prescribed number of consecutive weeks during a planning horizon, and each patient visit must be assigned an appointment time belonging to an allowable menu of equally-spaced times. Patient requests are revealed incrementally, and appointment time selections must be made without knowledge of future requests. First, a static problem variant is studied to understand the impact of fixed appointment times on routing and scheduling decisions, independent of other complicating factors in the HHNRS problem. The costs of offering fixed appointment times are quantified, and purely distance-based heuristics are shown to have potential limitations for appointment time problems unless proposed arc cost transformations are used. Building on this result, a new rolling horizon capacity-based heuristic is developed for HHNRS problems. The heuristic considers interactions between travel times, service times, and the fixed appointment time menu when inserting appointments for currently revealed patient requests into partial nurse schedules. The heuristic is shown to outperform a distance-based heuristic on metrics which emphasize meeting as much patient demand as possible. The home health nurse districting (HHND) problem is a tactical planning problem which influences HHNRS problem solution quality. A set of geographic zones must be partitioned into districts to be served by home health nurses, such that workload is balanced across districts and nurse travel is minimized. A set partitioning model for HHND is formulated and a column generation heuristic is developed which integrates ideas from optimization and local search. Methods for estimating district travel and workload are developed and implemented within the heuristic, which outperforms local search on test instances.

Bei verkehrspolitischen und infrastrukturellen Massnahmen folgen als wesentliche Nachfrageveränderungen vor allem Routen- und Verkehrsmittelwahleffekte. Mit der Anwendung der sequentiellen Routen- und Verkehrsmittelwahlmodelle, ist bei solchen Massnahmen aus verschiedenen Gründen eine konsistente und gesamthafte Gleichgewichtslösung nicht möglich. Das Ziel dieser Untersuchung war, ein konsistentes und verfeinertes Verfahren zu entwickeln, mit dem die Routen- und Verkehrsmittelwahl simultan bzw. in einem Schritt als eine Entscheidung berechnet werden kann. Neben dem Gleichgewicht bei der Verteilung der Verkehrsnachfrage auf die Alternativen, war die konsistente Schätzung der Modellparameter für die Bewertung von Einflussfaktoren bei den Entscheidungen hier eine weitere wichtige Anforderung. Das Modell ist in der Lage, ein realitätsentsprechendes Verhalten der Verkehrsteilnehmer, sowohl bei schwach, als auch bei stark belasteten Strassennetzen, zu beschreiben. Die unterschiedliche Wahrnehmung der Reisekosten der Verkehrsteilnehmer und die Netzüberbelastungen werden durch ein stochastisches Nutzergleichgewicht abgebildet. Das entwickelte Verfahren ermöglicht es: - die Nachfrageaufteilung mit einem konsistenten Gleichgewicht zwischen Verkehrsangebot und Verkehrsnachfrage zu berechnen. Dabei wird ein Gleichgewicht nicht nur innerhalb des Strassen- oder Schienennetzes, sondern zwischen allen verfügbaren Alternativen (unabhängig vom Verkehrsmittel) gesucht. - durch die iterative Kalibration der Modellparameter und die Nachfrageaufteilung ein konsistentes Gleichgewicht zwischen den geschätzten Modellparametern für die Nutzenfunktion und der Nachfrageaufteilung auf die vorhandenen Alternativen (Routen) zu berechnen. - mit einem stochastischen Nutzergleichgwicht die unterschiedliche Wahrnehmung der Nutzen bzw. der generalisierten Kosten der Verkehrsteilnehmer bei der Nachfrageaufteilung zu berücksichtigen. - die Auswirkungen von Angebotsveränderungen auf die Verkehrsmittelwahl und Routenwahl durch simultane Modellierung der Entscheidungen konsistent und ohne Rückkoppelungschritte zu berechnen.

Aprendizagem por reforço multiagente (do inglês, MARL) é uma tarefa desafiadora em que agentes buscam, concorrentemente, uma política capaz de maximizar sua utilidade. Aprender neste tipo de cenário é difícil porque os agentes devem se adaptar uns aos outros, tornando o objetivo um alvo em movimento. Consequentemente, não existem garantias de convergência para problemas de MARL em geral. Esta tese explora um problema em particular, denominado escolha de rotas (onde motoristas egoístas deve escolher rotas que minimizem seus custos de viagem), em busca de garantias de convergência. Em particular, esta tese busca garantir a convergência de algoritmos de MARL para o equilíbrio dos usuários (onde nenhum motorista consegue melhorar seu desempenho mudando de rota) e para o ótimo do sistema (onde o tempo médio de viagem é mínimo). O principal objetivo desta tese é mostrar que, no contexto de escolha de rotas, é possível garantir a convergência de algoritmos de MARL sob certas condições. Primeiramente, introduzimos uma algoritmo de aprendizagem por reforço baseado em minimização de arrependimento, o qual provamos ser capaz de convergir para o equilíbrio dos usuários Nosso algoritmo estima o arrependimento associado com as ações dos agentes e usa tal informação como sinal de reforço dos agentes. Além do mais, estabelecemos um limite superior no arrependimento dos agentes. Em seguida, estendemos o referido algoritmo para lidar com informações não-locais, fornecidas por um serviço de navegação. Ao usar tais informações, os agentes são capazes de estimar melhor o arrependimento de suas ações, o que melhora seu desempenho. Finalmente, de modo a mitigar os efeitos do egoísmo dos agentes, propomos ainda um método genérico de pedágios baseados em custos marginais, onde os agentes são cobrados proporcionalmente ao custo imposto por eles aos demais. Neste sentido, apresentamos ainda um algoritmo de aprendizagem por reforço baseado em pedágios que, provamos, converge para o ótimo do sistema e é mais justo que outros existentes na literatura. / Multiagent reinforcement learning (MARL) is a challenging task, where self-interested agents concurrently learn a policy that maximise their utilities. Learning here is difficult because agents must adapt to each other, which makes their objective a moving target. As a side effect, no convergence guarantees exist for the general MARL setting. This thesis exploits a particular MARL problem, namely route choice (where selfish drivers aim at choosing routes that minimise their travel costs), to deliver convergence guarantees. We are particularly interested in guaranteeing convergence to two fundamental solution concepts: the user equilibrium (UE, when no agent benefits from unilaterally changing its route) and the system optimum (SO, when average travel time is minimum). The main goal of this thesis is to show that, in the context of route choice, MARL can be guaranteed to converge to the UE as well as to the SO upon certain conditions. Firstly, we introduce a regret-minimising Q-learning algorithm, which we prove that converges to the UE. Our algorithm works by estimating the regret associated with agents’ actions and using such information as reinforcement signal for updating the corresponding Q-values. We also establish a bound on the agents’ regret. We then extend this algorithm to deal with non-local information provided by a navigation service. Using such information, agents can improve their regrets estimates, thus performing empirically better. Finally, in order to mitigate the effects of selfishness, we also present a generalised marginal-cost tolling scheme in which drivers are charged proportional to the cost imposed on others. We then devise a toll-based Q-learning algorithm, which we prove that converges to the SO and that is fairer than existing tolling schemes.

Made available in DSpace on 2016-06-02T20:00:55Z (GMT). No. of bitstreams: 1 5944.pdf: 3753394 bytes, checksum: 4babbe51bdbcdccb3d5611114b35d9f9 (MD5) Previous issue date: 2014-05-12 / Universidade Federal de Minas Gerais / This study reports on the information obtained by analyzing actual urban bicyclecommuter routes in São Carlos, using Global Positioning Systems (GPSs) for collecting the data and a Geographic Information System (GIS) for analyzing the data. The characteristics of the routes used by cyclists were compared with the characteristics of the shortest-path routes. The extra distance travelled was computed by the difference between these two lengths, and a multiple linear regression was created for explain the reason for this extra distance. The results showed that 70% of the trips were, at most, 15% longer than the shortest path, the average extra distance was 220 meters and the factors that have an influence was the street hierarchy and pavement quality. / Este trabalho relata as informações obtidas através da análise de rotas reais de bicicleta em viagens urbanas na cidade de São Carlos, utilizando Sistemas de Posicionamento Global (GPSs) para a coleta de dados e um Sistema de Informação Geográfica (SIG) para a análise das informações. As características dos percursos realizados pelos ciclistas foram comparadas com as características dos caminhos mais curtos entre seus pontos de origem e destino. A distância adicional percorrida foi calculada pela diferença de comprimento entre estas duas rotas e uma regressão linear múltipla foi criada pra explicar o motivo deste acréscimo. Verificou-se que 70% das viagens realizadas foram, no máximo, 15% mais longas que o menor caminho, a distância adicional média foi igual a 220 metros e que os fatores que influenciam diretamente no acréscimo da viagem é a hierarquia viária e a qualidade do pavimento.