In this thesis, automatic routing technologies for unmanned air vehicles are investigated. A route planner that minimizes the fuel consumption and maximizes the survivability is developed. While planning the route, using more than one objective entails the auto-routing problem to multi-objective optimization considerations. In this work, these considerations are handled with search algorithms. In order to assess the route options, a fuel consumption model and a survivability model are utilized for the route planner. As the assessment models are established, required computational time is taken into account without deteriorating the fidelity.

Aerial surveillance missions require a geographical region known as the area of interest to be inspected. The route that the aerial reconnaissance vehicle will follow is known as the flight route. Flight route planning operation has to be done before the actual mission is executed. A flight route may consist of hundreds of pre-defined geographical positions called waypoints. The optimal flight route planning manages to find a tour passing through all of the waypoints by covering the minimum possible distance. Due to the combinatorial nature of the problem it is impractical to devise a solution using brute force approaches. This study presents a strategy to find a cost effective and near-optimal solution to the flight route planning problem. The proposed approach is implemented on GPU using CUDA.

Geographical Information Systems (GIS) have been rapidly developed in the fields that need spatial data and transportation planning is one of these fields. Since transportation data is spatially distributed and need spatial, statistical and network based analysis / GIS applications have contributions to transportation planning. In this study, it is aimed to determine a new public transportation mode and route in the METU campus with the aid of GIS by considering the stations of &Ccedil / ayyolu metro route. Besides, it is also aimed to show that GIS can be a useful tool for constructing transport planning database and exploring, analyzing planning data. Gross settlement area of the campus, covering about 220 hectare land on the southern side of the Ankara &amp / #8211 / EskiSehir highway, is the study area of this thesis. First, campus land-use, topography, population characteristics and transportation structure are explored. Then, campus trip demand and pedestrian traffic are estimated. Afterwards, eight public transport route alternatives are proposed with their stops or stations for three different modes as / guided light transit, modern trolleybus and monorail. Proposed routes and stops or stations are evaluated with their physical characteristics and in terms of service areas shaped relative to pedestrian accessibility for determining the suitable public transport service in the METU campus. Consequently, Trolleybus B alternative is selected as the first degree suitable public transport service in campus. Besides, Monorail B and Trolleybus A services are determined as the second degree suitable services in campus.

Connected and Autonomous Vehicles (CAVs) benefit from both connectivity between vehicles and city infrastructures and automation of vehicles. In this respect, CAVs can improve safety and reduce traffic congestion and environmental impacts of daily commutes through making collaborative decisions. This dissertation studies how to reduce the energy consumption of vehicles and traffic congestion by making high-level routing decisions of CAVs. The first half of this dissertation considers the problem of eco-routing (finding the energy-optimal route) for Plug-In Hybrid Electric Vehicles (PHEVs) to minimize the overall energy consumption cost. Several algorithms are proposed that can simultaneously calculate an energy-optimal route (eco-route) for a PHEV and an optimal power-train control strategy over this route. The results show significant energy savings for PHEVs with a near real-time execution time for the algorithms. The second half of this dissertation tackles the problem of routing for fleets of CAVs in the presence of mixed traffic (coexistence of regular vehicles and CAVs). In this setting, all CAVs belong to the same fleet and can be routed using a centralized controller. The routing objective is to minimize a given overall fleet traveling cost (travel time or energy consumption). It is assumed that regular vehicles (non-CAVs) choose their routing decisions selfishly to minimize their traveling time. A framework is proposed that deals with the routing interaction between CAVs and regular uncontrolled vehicles under different penetration rates (fractions) of CAVs. The results suggest collaborative routing decisions of CAVs improve not only the cost of CAVs but also that of the non-CAVs. This framework is further extended to consider congestion-aware route-planning policies for Autonomous Mobility-on-Demand (AMoD) systems, whereby a fleet of autonomous vehicles provides on-demand mobility under mixed traffic conditions. A network flow model is devised to optimize the AMoD routing and rebalancing strategies in a congestion-aware fashion by accounting for the endogenous impact of AMoD flows on travel time. The results suggest that for high levels of demand, pure AMoD travel can be detrimental due to the additional traffic stemming from its rebalancing flows, while the combination of AMoD with walking or micromobility options can significantly improve the overall system performance.

The diploma thesis focuses on the use of genetic algorithms for tasks related to the travelling salesman problem. Based on theoretical knowledge and problem analysis a proposal of the solution is provided. This creates a daily route plan for service technicians with regard to constraints. The case study shows that the proposed solution in comparison with manual scheduling by experience enables to reduce transportation costs.

Throughout United States and world history, disasters have caused not only significant loss of life, property but also enormous financial loss. The tsunami that occurred on December 26, 2004 is a telling example of the devastation that can occur unexpectedly. This unexpected natural event never happened before in this area. In addition, there was a lack of an emergency response plan for events of that magnitude. Therefore, this event resulted not only in a natural catastrophe for the people of South and Southeast Asia, but it is also considered one of the greatest natural disasters in world history. After the giant wave dissipated, there were more than 230,000 people dead and more than US$10 billion in property damage and loss. Another significant event was the terrorist incident on September 11, 2001 (commonly referred to as 9/11) in United States. This event was unexpected and an unnatural, i.e., man-made event. It resulted in approximately 3,000 lives lost and about US$21 billion in property damage. These and other unexpected (or unanticipated) events give emergency management officials short- or no-notice to prevent or respond to the situation. These and other facts motivate the need for better emergency evacuation route planning (EERP) approaches in order to minimize the loss of human lives and property in short- or no-notice emergency situations. This research considers aspects of evacuation routing that have received little attention in research and, more importantly, in practice. Previous EERP models only either consider unidirectional evacuee flow from the source of a hazard to destinations of safety or unidirectional emergency first responder flow to the hazard source. However, in real-life emergency situations, these heterogeneous, incompatible flows occur simultaneously over a bi-directional capacitated lane-based travel network, especially in short- and no-notice emergencies. After presenting a review of the work related to the multiple flow EERP problem, mathematical formulations are presented for the EERP problem where the objective for each problem is to identify an evacuation routing plan (i.e., a traffic flow schedule) that maximizes evacuee and responder flow and minimizes network clearance time of both types of flow. In addition, we integrate the general human response behavior flow pattern, where the cumulative flow behavior follows different degrees of an S-shaped curve depending upon the level of the evacuation order. We extend the analysis to consider potential traffic flow conflicts between the two types of flow under these conditions. A conflict occurs when flow of different types occupy a roadway segment at the same time. Further, with different degrees of flow movement flow for both evacuee and responder flow, the identification of points of flow congestion on the roadway segments that occur within the transportation network is investigated.

The main subject of the thesis is planning &ldquo / cultural routes&rdquo / as a method for conservation of cultural and natural heritage areas at regional scale. Defining a framework of conceptual principles which should be considered in spatial planning of cultural routes and regional networks constitutes the major aim of the thesis. Within the scope of the study, a new developing concept recently, cultural routes are discussed as a tool for sustaining historic and local values of rural and archaeological landscapes. In this respect, the study is structured in two main parts. Firstly, conceptual background on the issue is discussed as the development of cultural route concept, definitions declared by international organizations working on the issue and principles of route planning. Secondly, based on the conceptual research, basic concepts and principles for route planning process is discussed through a case study. The case study for the thesis is selected as the Kas- Kekova region in Antalya, known as the Central Lycia in antiquity. Depending on the assessment of region&rsquo / s cultural landscape, the study is resulted by description of a spatial and conceptual framework for planning of a cultural route network in the region. Through areas rich in cultural and natural heritage, planning routes and networks at regional scale could be used as an effective tool for presenting and sustaining multivalent character of the place, and leading economic sectors which have effect on heritage.

In recent years, multi-agent traffic simulation has been widely used to accurately evaluate the performance of a road network considering individual and dynamic movements of vehicles under a virtual roadway environment. Given initial traffic demands and road conditions, the simulation is executed with multiple iterations and provides users with converged roadway conditions for the performance evaluation. For an accurate traffic simulation model, the driver's learning behavior is one of the major components to be concerned, as it affects road conditions (e.g., traffic flows) at each iteration as well as performance (e.g., accuracy and computational efficiency) of the traffic simulation. The goal of this study is to propose a realistic learning behavior model of drivers concerning their uncertain perception and interactions with other drivers. The proposed learning model is based on the Extended Belief-Desire-Intention (E-BDI) framework and two major decisions arising in the field of transportation (i.e., route planning and decision-making at an intersection). More specifically, the learning behavior is modeled via a dynamic evolution of a Bayesian network (BN) structure. The proposed dynamic learning approach considers three underlying assumptions: 1) the limited memory of a driver, 2) learning with incomplete observations on the road conditions, and 3) non-stationary road conditions. Thus, the dynamic learning approach allows driver agents to understand real-time road conditions and estimate future road conditions based on their past knowledge. In addition, interaction behaviors are also incorporated in the E-BDI framework to address influences of interactions on the driver's learning behavior. In this dissertation work, five major human interactions adopted from a social science literature are considered: 1) accommodation, 2) collaboration, 3) compromise, 4) avoidance, and 5) competition. The first three interaction types help to mimic information exchange behaviors between drivers (e.g., finding a route using a navigation system) while the last two interaction types are relevant with behaviors involving non-information exchange behaviors (e.g., finding a route based on a driver's own experiences). To calibrate the proposed learning behavior model and evaluate its performance in terms of inference accuracy and computational efficiency, drivers' decision data at intersections are collected via a human-in-the-loop experiment involving a driving simulator. Moreover, the proposed model is used to test and demonstrate the impact of five interactions on drivers' learning behavior under an en route planning scenario with real traffic data of Albany, New York, and Phoenix, Arizona. In this dissertation work, two major traffic simulation platforms, AnyLogic® and DynusT®, are used for the demonstration purposes. The experimental results reveal that the proposed model is effective in modeling realistic learning behaviors of drivers in conduction with interactions with other drivers.

Submitted by DIEGO RENAN BRUNO null (diego_renan_bruno@hotmail.com) on 2016-05-19T02:22:22Z No. of bitstreams: 1 DISSERTAÇÃO_Diego_Renan_Bruno_UNESP_IBILCE.pdf: 5786200 bytes, checksum: e4c11c8581ba4cc8af49f068d9d637d9 (MD5) / Approved for entry into archive by Felipe Augusto Arakaki (arakaki@reitoria.unesp.br) on 2016-05-23T14:27:37Z (GMT) No. of bitstreams: 1 bruno_dr_me_sjrp.pdf: 5786200 bytes, checksum: e4c11c8581ba4cc8af49f068d9d637d9 (MD5) / Made available in DSpace on 2016-05-23T14:27:37Z (GMT). No. of bitstreams: 1 bruno_dr_me_sjrp.pdf: 5786200 bytes, checksum: e4c11c8581ba4cc8af49f068d9d637d9 (MD5) Previous issue date: 2016-04-27 / Neste trabalho foi desenvolvido um sistema de controle híbrido bioinspirado para o planejamento de rota com vistas para a robótica móvel autônoma, baseado em redes neurais artificiais e algoritmos genéticos. O controlador tem como principal objetivo auxiliar o robô móvel em sua navegação quando aplicado em ambientes dinâmicos. Para este trabalho, o ambiente dinâmico utilizado é um “chão de fábrica” industrial, em que alguns obstáculos não são fixos e permanecem em movimentação constante. O controlador desenvolvido neste trabalho pode ser adaptado facilmente para operar em outros ambientes dinâmicos. Independentemente do ambiente utilizado, o controlador deve ser capaz de traçar uma rota possível entre o ponto inicial e o ponto de objetivo, tendo o potencial de evitar todo tipo de obstáculo que surgir nessa rota, seja um obstáculo estático ou dinâmico. O algoritmo foi implementado na linguagem C e simulado no software de modelagem e simulação de robôs V-REP (Virtual Robot Experimentation Platform). O controlador neurogenético mostrou ser eficiente para auxiliar o robô em sua navegação quando aplicado em ambientes dinâmicos. / In this work, a bioinspired hybrid control system was developed for route planning, aiming autonomous mobile robots based on artificial neural networks and genetic algorithms. The main objective of the controller is to assist the mobile robot in its navigation when applied in dynamic environments. For this work, the dynamic environment chosen was a " factory floor", in which some industrial obstacles are not fixed and remain in constant movements. The controller developed in this work can easily be adapted to operate in other dynamic environments. Regardless the environment chosen in this work, the controller must be able to map out a possible route between the starting point and the goal point with the potential to avoid all types of obstacles that appear along the routes, either a static or a dynamic one. The algorithm was implemented in C language and simulated on a robots modeling and simulation software called V-REP (Virtual Robot Experimentation Platform). The neurogenetic controller was efficient to assist the mobile robot in its navigation when applied in dynamic environments.

O nÃvel de serviÃo de uma empresa atacadista distribuidora pode ser medido pela frequÃncia e regularidade com que sua equipe de vendas atende os clientes. Mas como o sucesso no mercado tambÃm depende dos custos envolvidos, o planejamento adequado das sistemÃticas de atendimento à crÃtico. Aproveitando as similaridades entre essa situaÃÃo e o Problema de RoteirizaÃÃo PeriÃdica de VeÃculos (PRPV), foi proposta uma tÃcnica de resoluÃÃo deste problema. Para o PRPV, dado um horizonte de planejamento composto de vÃrios dias, clientes devem ter suas visitas alocadas aos dias conforme combinaÃÃes possÃveis ao mesmo tempo em que rotas sÃo geradas para cada dia, objetivando a reduÃÃo do custo total de atendimento nesse mesmo horizonte de planejamento. A tÃcnica proposta tambÃm foi adaptada para aplicaÃÃo no caso prÃtico de roteirizaÃÃo de uma equipe de vendas com horizonte de planejamento semanal e consiste em um algoritmo genÃtico para o qual foi desenvolvido um operador de cruzamento original. A tÃcnica foi validada com instÃncias da literatura para o PRPV e suas soluÃÃes para o caso prÃtico indicaram economias anuais significativas. / The service level of a wholesale distributor can be measured by the frequency and regularity with which its sales staff serves customers. But as the market success also depends on the costs involved, the proper planning of systematic servings is critical. Taking advantage of the similarities between this situation and the Periodic Vehicle Routing Problem (PVRP), a technique for solving the later was proposed. For the PVRP, given a planning horizon of several days, visits to customers must be assigned to possible days according to predefined schedule combinations at the same time as routes are generated for each day, aiming to reduce the total cost of serving in the same planning horizon. The proposed technique was also adapted to be applied to the practical case of routing a sales team within a weekly planning horizon and it consists of a genetic algorithm for which was developed an original crossover operator. The technique was validated with instances from the literature for the PVRP and its solutions for the case study indicated significant annual savings.