With the technological advancement in computer science, Geographic Information Science (GIScience), and transportation, more and more complex path finding queries including category based queries are proposed and studied across diverse disciplines. A category based query, such as Optimal Sequenced Routing (OSR) queries and Trip Planning Queries (TPQ), asks for a minimum-cost path that traverses a set of categories with or without a predefined order in a graph. Due to the extensive computing time required to process these complex queries in a large scale environment, efficient algorithms are highly desirable whenever processing time is a consideration. In Artificial Intelligence (AI), a best first search is an informed heuristic path finding algorithm that uses domain knowledge as heuristics to expedite the search process. Traditional best first searches are single-variate in terms of the number of variables to describe a state, and thus not appropriate to process these queries in a graph. In this dissertation, 1) two new types of category based queries, Category Sequence Traversal Query (CSTQ) and Optimal Sequence Traversal Query (OSTQ), are proposed; 2) the existing single-variate best first searches are extended to multivariate best first searches in terms of the state specified, and a class of new concepts--state graph, sub state graph, sub state graph space, local heuristic, local admissibility, local consistency, global heuristic, global admissibility, and global consistency--is introduced into best first searches; 3) two bivariate best first search algorithms, C* and O*, are developed to process CSTQ and OSTQ in a graph, respectively; 4) for each of C* and O*, theorems on optimality and optimal efficiency in a sub state graph space are developed and identified; 5) a family of algorithms including C*-P, C-Dijkstra, O*-MST, O*-SCDMST, O*- Dijkstra, and O*-Greedy is identified, and case studies are performed on path finding in transportation networks, and/or fully connected graphs, either directed or undirected; and 6) O*- SCDMST is adopted to efficiently retrieve optimal solutions for OSTQ using network distance metric in a large transportation network. / Ph. D.

Submitted by William Justo Figueiro (williamjf) on 2015-07-18T13:23:58Z No. of bitstreams: 1 51c.pdf: 3316384 bytes, checksum: 9eb62dc3d062f0ca82674a6d3feb7fdc (MD5) / Made available in DSpace on 2015-07-18T13:23:58Z (GMT). No. of bitstreams: 1 51c.pdf: 3316384 bytes, checksum: 9eb62dc3d062f0ca82674a6d3feb7fdc (MD5) Previous issue date: 2011-03-29 / Nenhuma / O crescimento do tráfego nas redes de telecomunicações por serviços de banda larga, telefonia fixa e móvel tem demandado esforços no planejamento e estudo da recomposição da rede, em especial redes de transporte, tornando elevado o nível de complexidade na elaboração de projetos que envolvem redes. A complexidade de interligação aumenta de acordo com as restrições impostas pela capacidade de investimentos e custos operacionais na obtenção da solução ótima para a melhor topologia de rede. Para resolver problemas de planejamento e recomposição da rede de telecomunicações é necessária a utilização de recurso computacional, pois problemas que envolvem redes desta natureza possuem a característica do conjunto de problemas de otimização combinatória, considerados difíceis. Com o objetivo de planejar e realizar estudos na recomposição da rede de telecomunicações, neste trabalho é apresentada a estratégia que visa o uso de recursos heurísticos como algoritmo genético e Dijkstra, bem como o conceito de rede fictícia na modelagem e solução computacional na obtenção da solução ótima referente à topologia de rede, possibilitando a análise prévia do tráfego na topologia proposta caso houver a incidência de falhas. / The growth of traffic on telecommunications networks for broadband services, fixed and mobile telephony has demanded efforts in planning and study the restoration of the network, especially transport networks, making the high level of complexity in developing projects that involve networks. The interconnection complexity increases with the restrictions imposed by the capacity of investment and operating costs in obtaining the optimal solution for the best network topology. To resolve issues of planning and rebuilding of the telecommunication networks is necessary to use computational resources, since problems involving networks of this nature have the feature set of combinatorial optimization problems, considered difficult. With the objective to plan and carry out studies on the recomposition of the telecommunications network, in this work the strategy for use as heuristic genetic algorithm and Dijkstra, as well as the concept of fictitious network modeling and computational solution to obtain the solution great on the network topology, enabling preliminary analysis of traffic on the proposed topology where there incidence of failures.

In the past research, smart sensor devices have become mature enough for large, distributed networks of such sensors to start to be deployed. Such networks can include tens or hundreds of independent nodes that can perform their functions without human interactions such as recharging of batteries, the configuration of network routes and others. Each of the sensors in the wireless sensor network is considered as microsystem, which consists of memory, processor, transducers and low bandwidth as well as a low range radio transceiver. This study investigates an adaptive sampling strategy for WSS aimed at reducing the number of data samples by sensing data only when a significant change in these processes is detected. This detection strategy is based on an extension to Holt's Method and statistical model. To investigate this strategy, the water consumption in a household is used as a case study. A query distribution approach is proposed, which is presented in detail in chapter 5. Our developed wireless sensor query engine is programmed on Sensinode testbed cc2430. The implemented model used on the wireless sensor platform and the architecture of the model is presented in chapters six, seven, and eight. This thesis presents a contribution by designing the experimental simulation setup and by developing the required database interface GUI sensing system, which enables the end user to send the inquiries to the sensor s network whenever needed, the On-Demand Query Sensing system ODQS is enhanced with a probabilistic model for the purpose of sensing only when the system is insufficient to answer the user queries. Moreover, a dynamic aggregation methodology is integrated so as to make the system more adaptive to query message costs. Dynamic on-demand approach for aggregated queries is implemented, based in a wireless sensor network by integrating the dynamic programming technique for the most optimal query decision, the optimality factor in our experiment is the query cost. In-network query processing of wireless sensor networks is discussed in detail in order to develop a more energy efficient approach to query processing. Initially, a survey of the research on existing WSN query processing approaches is presented. Building on this background, novel primary achievements includes an adaptive sampling mechanism and a dynamic query optimiser. These new approaches are extremely helpful when existing statistics are not sufficient to generate an optimal plan. There are two distinct aspects in query processing optimisation; query dynamic adaptive plans, which focus on improving the initial execution of a query, and dynamic adaptive statistics, which provide the best query execution plan to improve subsequent executions of the aggregation of on-demand queries requested by multiple end-users. In-network query processing is attractive to researchers developing user-friendly sensing systems. Since the sensors are a limited resource and battery powered devices, more robust features are recommended to limit the communication access to the sensor nodes in order to maximise the sensor lifetime. For this reason, a new architecture that combines a probability modelling technique with dynamic programming (DP) query processing to optimise the communication cost of queries is proposed. In this thesis, a dynamic technique to enhance the query engine for the interactive sensing system interface is developed. The probability technique is responsible for reducing communication costs for each query executed outside the wireless sensor networks. As remote sensors have limited resources and rely on battery power, control strategies should limit communication access to sensor nodes to maximise battery life. We propose an energy-efficient data acquisition system to extend the battery life of nodes in wireless sensor networks. The system considers a graph-based network structure, evaluates multiple query execution plans, and selects the best plan with the lowest cost obtained from an energy consumption model. Also, a genetic algorithm is used to analyse the performance of the approach. Experimental testing are provided to demonstrate the proposed on-demand sensing system capabilities to successfully predict the query answer injected by the on-demand sensing system end-user based-on a sensor network architecture and input query statement attributes and the query engine ability to determine the best and close to the optimal execution plan, given specific constraints of these query attributes . As a result of the above, the thesis contributes to the state-of-art in a network distributed wireless sensor network query design, implementation, analysis, evaluation, performance and optimisation.

The master’s thesis deals with the use of artificial intelligence as support for managerial decision making in the company. This thesis contains the application which utilize genetic and graph algorithms to optimize the location of production facilities and logistic warehouses according to transport cost aspects.

When we look at the eld of robotics we nd that exist a lot of types of robots. Some of tham use location navigation and global navigation for their work. This work aims to map options of location navigation and description of basic technique which used. Especially, we will deal with algorithms which work with optical sensors, for example camera, stereocamera or laser which scan medium. Practise section this work is focused on the proposal and implementacion algorithm which working with local navigation for robot's return to the starting position. All this work is connecting with tracked robot which formed in the framework one of project realization at Faculty of information technology.

This thesis addresses route planning of Battery Electric Heavy-Duty Commercial Vehicles to enhance the reliability of electric vehicle transport. Collaborating with Scania, a Swedish truck manufacturing company, the goal is to develop a pipeline that uses open source data from OpenStreetMap and performs a modified Contraction Hierarchy in order to create a graph that can be used as input to a modified Vehicle Routing Problem formulation using Mixed Integer Programming. The input graph is preprocessed to support a Battery Electric Heavy-Duty Commercial Vehicle model in order to more accurately predict energy consumption. The challenges lie in balancing computational efficiency and electric vehicle characteristics. The implemented pipeline demonstrates success but initial tests show that a naive version of the pipeline, not implementing Contraction Hierarchies, can perform better. Several speedups can be made in order to improve the efficiency of the pipeline, the main being in programming in a more efficient programming language than Python. Further testing is needed for larger input graphs to assess performance accurately.

This diploma work deals with proposal and implementation GPS navigation system. Work includes described basic types of cement quarry and proposal to create mathematical and software model of quarry. Then work is devoted to possibilities of basic described algorithms for searching the shortest way in graph and two algorithms are described. They are Floyd-Warshall and Dikjstra algorithms. The work describe implementation of Dijkstra algorithm to model of quarry and description of the programs Autec RouteEditor and AQL Control Library. MINÁČ, J. GPS navifgation system for cement for cement technology. Brno: Brno university of technology, Faculty of electrical engineering and communication, 2009. 90 p. Supervisor prof. Ing. František Zezulka, CSc.

This work is intended on identification with basic problems from the graphs theory area. There are the basic conceptions as well more complicated problems described. The one part of this work is specialized in working of individual types of graphs. It starts with single linked list through double linked list after as much as trees which represented the simplest graphs textures. The other part of this work devotes to the whole graph and describes more complicated problems and their resolution from the theory graphs area. Among these problems belongs to searching in graphs help by Depth First Search and Breadth First Search methods. Then searching the shortest way help by the specific algorithms as are: Dijkstra´s algorithm, Floyd-Warshall´s algorithm and Bellman-Ford´s algorithm. The last part is devoted to problems with searching minimal frames of graphs with usage Kruskal´s algorithm, Jarnik´s algorithm and Boruvka´s algorithm methods.

[ES] En la actualidad, uno de los principales desafíos a los que se enfrentan las grandes áreas metropolitanas es la congestión provocada por el tráfico, la cual se ha convertido en un problema importante al que se enfrentan las autoridades de cada ciudad. Para abordar este problema es necesario implementar una solución eficiente para controlar el tráfico que genere beneficios para los ciudadanos, como reducir los tiempos de viaje de los vehículos y, en consecuencia, el consumo de combustible, el ruido, y la contaminación ambiental. De hecho, al analizar adecuadamente la demanda de tráfico, es posible predecir las condiciones futuras del tráfico, y utilizar esa información para la optimización de las rutas tomadas por los vehículos. Este enfoque puede ser especialmente efectivo si se aplica en el contexto de los vehículos autónomos, que tienen un comportamiento más predecible, lo cual permite a los administradores de la ciudad mitigar los efectos de la congestión, como es la contaminación, al mejorar el flujo de tráfico de manera totalmente centralizada. La validación de este enfoque generalmente requiere el uso de simulaciones que deberían ser lo más realistas posible. Sin embargo, lograr altos grados de realismo puede ser complejo cuando los patrones de tráfico reales, definidos a través de una matriz de Origen/Destino (O-D) para los vehículos en una ciudad, son desconocidos, como ocurre la mayoría de las veces. Por lo tanto, la primera contribución de esta tesis es desarrollar una heurística iterativa para mejorar el modelado de la congestión de tráfico; a partir de las mediciones de bucle de inducción reales hechas por el Ayuntamiento de Valencia (España), pudimos generar una matriz O-D para la simulación de tráfico que se asemeja a la distribución de tráfico real. Si fuera posible caracterizar el estado del tráfico prediciendo las condiciones futuras del tráfico para optimizar la ruta de los vehículos automatizados, y si se pudieran tomar estas medidas para mitigar de manera preventiva los efectos de la congestión con sus problemas relacionados, se podría mejorar el flujo de tráfico en general. Por lo tanto, la segunda contribución de esta tesis es desarrollar una Ecuación de Predicción de Tráfico para caracterizar el comportamiento en las diferentes calles de la ciudad en términos de tiempo de viaje con respecto al volumen de tráfico, y aplicar una regresión logística a esos datos para predecir las condiciones futuras del tráfico. La tercera y última contribución de esta tesis apunta directamente al nuevo paradigma de gestión de tráfico previsto, tratándose de un servidor de rutas capaz de manejar todo el tráfico en una ciudad, y equilibrar los flujos de tráfico teniendo en cuenta las condiciones de congestión del tráfico presentes y futuras. Por lo tanto, realizamos un estudio de simulación con datos reales de congestión de tráfico en la ciudad de Valencia (España), para demostrar cómo se puede mejorar el flujo de tráfico en un día típico mediante la solución propuesta. Los resultados experimentales muestran que nuestra solución, combinada con una actualización frecuente de las condiciones del tráfico en el servidor de rutas, es capaz de lograr mejoras sustanciales en términos de velocidad promedio y tiempo de trayecto, ambos indicadores de un menor grado de congestión y de una mejor fluidez del tráfico. / [CA] En l'actualitat, un dels principals desafiaments als quals s'enfronten les grans àrees metropolitanes és la congestió provocada pel trànsit, que s'ha convertit en un problema important al qual s'enfronten les autoritats de cada ciutat. Per a abordar aquest problema és necessari implementar una solució eficient per a controlar el trànsit que genere beneficis per als ciutadans, com reduir els temps de viatge dels vehicles i, en conseqüència, el consum de combustible, el soroll, i la contaminació ambiental. De fet, en analitzar adequadament la demanda de trànsit, és possible predir les condicions futures del trànsit, i utilitzar aqueixa informació per a l'optimització de les rutes preses pels vehicles. Aquest enfocament pot ser especialment efectiu si s'aplica en el context dels vehicles autònoms, que tenen un comportament més predictible, i això permet als administradors de la ciutat mitigar els efectes de la congestió, com és la contaminació, en millorar el flux de trànsit de manera totalment centralitzada. La validació d'aquest enfocament generalment requereix l'ús de simulacions que haurien de ser el més realistes possible. No obstant això, aconseguir alts graus de realisme pot ser complex quan els patrons de trànsit reals, definits a través d'una matriu d'Origen/Destinació (O-D) per als vehicles en una ciutat, són desconeguts, com ocorre la majoria de les vegades. Per tant, la primera contribució d'aquesta tesi és desenvolupar una heurística iterativa per a millorar el modelatge de la congestió de trànsit; a partir dels mesuraments de bucle d'inducció reals fetes per l'Ajuntament de València (Espanya), vam poder generar una matriu O-D per a la simulació de trànsit que s'assembla a la distribució de trànsit real. Si fóra possible caracteritzar l'estat del trànsit predient les condicions futures del trànsit per a optimitzar la ruta dels vehicles automatitzats, i si es pogueren prendre aquestes mesures per a mitigar de manera preventiva els efectes de la congestió amb els seus problemes relacionats, es podria millorar el flux de trànsit en general. Per tant, la segona contribució d'aquesta tesi és desenvolupar una Equació de Predicció de Trànsit per a caracteritzar el comportament en els diferents carrers de la ciutat en termes de temps de viatge respecte al volum de trànsit, i aplicar una regressió logística a aqueixes dades per a predir les condicions futures del trànsit. La tercera i última contribució d'aquesta tesi apunta directament al nou paradigma de gestió de trànsit previst. Es tracta d'un servidor de rutes capaç de manejar tot el trànsit en una ciutat, i equilibrar els fluxos de trànsit tenint en compte les condicions de congestió del trànsit presents i futures. Per tant, realitzem un estudi de simulació amb dades reals de congestió de trànsit a la ciutat de València (Espanya), per a demostrar com es pot millorar el flux de trànsit en un dia típic mitjançant la solució proposada. Els resultats experimentals mostren que la nostra solució, combinada amb una actualització freqüent de les condicions del trànsit en el servidor de rutes, és capaç d'aconseguir millores substancials en termes de velocitat faig una mitjana i de temps de trajecte, tots dos indicadors d'un grau menor de congestió i d'una fluïdesa millor del trànsit. / [EN] Currently, one of the main challenges that large metropolitan areas have to face is traffic congestion, which has become an important problem faced by city authorities. To address this problem, it becomes necessary to implement an efficient solution to control traffic that generates benefits for citizens, such as reducing vehicle journey times and, consequently, use of fuel, noise and environmental pollution. In fact, by properly analyzing traffic demand, it becomes possible to predict future traffic conditions, and to use that information for the optimization of the routes taken by vehicles. Such an approach becomes especially effective if applied in the context of autonomous vehicles, which have a more predictable behavior, thus enabling city management entities to mitigate the effects of traffic congestion and pollution by improving the traffic flow in a city in a fully centralized manner. Validating this approach typically requires the use of simulations, which should be as realistic as possible. However, achieving high degrees of realism can be complex when the actual traffic patterns, defined through an Origin/Destination (O-D) matrix for the vehicles in a city, are unknown, as occurs most of the times. Thus, the first contribution of this thesis is to develop an iterative heuristic for improving traffic congestion modeling; starting from real induction loop measurements made available by the City Hall of Valencia, Spain, we were able to generate an O-D matrix for traffic simulation that resembles the real traffic distribution. If it were possible to characterize the state of traffic by predicting future traffic conditions for optimizing the route of automated vehicles, and if these measures could be taken to preventively mitigate the effects of congestion with its related problems, the overall traffic flow could be improved. Thereby, the second contribution of this thesis was to develop a Traffic Prediction Equation to characterize the different streets of a city in terms of travel time with respect to the vehicle load, and applying logistic regression to those data to predict future traffic conditions. The third and last contribution of this thesis towards our envisioned traffic management paradigm was a route server capable of handling all the traffic in a city, and balancing traffic flows by accounting for present and future traffic congestion conditions. Thus, we perform a simulation study using real data of traffic congestion in the city of Valencia, Spain, to demonstrate how the traffic flow in a typical day can be improved using our proposed solution. Experimental results show that our proposed solution, combined with frequent updating of traffic conditions on the route server, is able to achieve substantial improvements in terms of average travel speeds and travel times, both indicators of lower degrees of congestion and improved traffic fluidity. / Finally, I want to thank the Ecuatorian Republic through the "Secretaría de Educación Superior, Ciencia, Tecnología e Innovación" (SENESCYT), for granting me the scholarship to finance my studies. / Zambrano Martínez, JL. (2019). Efficient Traffic Management in Urban Environments [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/129865 / TESIS