A real-time fault diagnosis system for high-speed power system protection based on machine learning algorithms

This paper puts forward a real-time smart fault diagnosis system (SFDS) intended for high-speed protection of power system transmission lines. This system is based on advanced signal processing techniques, traveling wave theory results, and machine learning algorithms. The simulation results show that the SFDS can provide an accurate internal/external fault discrimination, fault inception time estimation, fault type identification, and fault location. This paper presents also the hardware requirements and software implementation of the SFDS

Congestion based Truck Drone intermodal delivery optimization

Commerce companies have experienced a rise in the number of parcels that need to be delivered each day. The goal of this study is to provide a decision-making procedure to assist carriers in taking a more significant role in selecting cost and risk-efficient truck-drone intermodal delivery routing plan. The congestion-based model is developed to select the method of parcel delivery utilizing a truck and a drone for optimizing cost and time. A study also has been conducted to compare drone-only and truck-only delivery routing plan. The proposed A* Heuristic algorithm and the OSRM application generate the travel path for drone and a truck along with the time of travel. Case studies have been conducted by varying the weight provided to cost and risk variable, studies indicate that there is a significant change in drone delivery travel time and cost with increase of cost weightage

ItsBlue: A Distributed Bluetooth-Based Framework for Intelligent Transportation Systems

Inefficiency in transportation networks is having an expanding impact, at a variety of levels. Transportation authorities expect increases in delay hours and in fuel consumption and, consequently, the total cost of congestion. Nowadays, Intelligent Transportation Systems (ITS) have become a necessity in order to alleviate the expensive consequences of the rapid demand on transportation networks. Since the middle of last century, ITS have played a significant role in road safety and comfort enhancements. However, the majority of state of the art ITS are suffering from several drawbacks, among them high deployment costs and complexity of maintenance. Over the last decade, wireless technologies have reached a wide range of daily users. Today\u27s Mobile devices and vehicles are now heavily equipped with wireless communication technologies. Bluetooth is one of the most widely spread wireless technologies in current use. Bluetooth technology has been well studied and is broadly employed to address a variety of challenges due to its cost-effectiveness, data richness, and privacy perverseness, yet Bluetooth utilization in ITS is limited to certain applications. However, Bluetooth technology has a potential far beyond today\u27s ITS applications. In this dissertation, we introduce itsBlue, a novel Bluetooth-based framework that can be used to provide ITS researchers and engineers with desired information. In the itsBlue framework, we utilize Bluetooth technology advantages to collect road user data from unmodified Bluetooth devices, and we extract a variety of traffic statistics and information to satisfy ITS application requirements in an efficient and cost-effective way. The itsBlue framework consists of data collection units and a central computing unit. The itsBlue data collection unit features a compact design that allows for stationary or mobile deployment in order to extend the data collection area. Central computing units aggregate obtained road user data and extract a number of Bluetooth spatial and temporal features. Road users’ Bluetooth features are utilized in a novel way to determine traffic-related information, such as road user context, appearance time, vehicle location and direction, etc. Extracted information is provided to ITS applications to generate the desired transportation services. Applying such a passive approach involves addressing several challenges, like discovering on-board devices, filtering out data received from vehicles out of the target location, or revealing vehicle status and direction. Traffic information provided by the itsBlue framework opens a wide to the development of a wide range of ITS applications. Hence, on top of the itsBlue framework, we develop a pack of intersection management applications that includes pedestrians’ volume and waiting times, as well as vehicle queue lengths and waiting times. Also, we develop a vehicle trajectory reconstruction application. The itsBlue framework and applications are thoroughly evaluated by experiments and simulations. In order to evaluate our work, we develop an enhanced version of the UCBT Network Simulator 2 (NS-2). According to evaluation outcomes, itsBlue framework and applications evaluations show promising results. For instance, the evaluation results show that the itsBlue framework has the ability to reveal road user context with accuracy exceeding 95% in 25s

都市の持続可能性に向けた旅行行動と知的移動データ統合に関する包括的研究

過去数十年にわたり世界中で都市の持続可能性がトレンドとなり研究対象となっている．人々は，非効率な天然資源の消費や社会経済活動による環境破壊など，地球環境に有害な活動を行い，これには都市計画や交通計画を始め，多くの分野が密接に関係している．現在では，これらを解決する新技術の開発や応用が広範囲な研究分野で日々取り組まれている．本研究では観光に関する問題を，交通と都市の研究の観点からさまざまなビックデータを使用し，持続可能な都市開発を目標とした具体的な解決策を示した．本研究では都市や地域の持続可能性に資するデータの活用方法として，Wi-Fiパケットセンサーを使用した旅行者にとって魅力的な観光目的地マネジメントに関する研究，およびETCプローブデータを使用した旅行時間の信頼性の観測における天候の影響に関する分析を組み合わせて示した．本論文では，都市の移動性の認知に対して以下に示す3つの研究から，特徴的な結果と有効な分析手法を確立した．1）Wi-Fiパッケージセンシング調査を使用した，広域観光エリアでの周遊パターンのマイニングベースの関連法則の調査，2）Wi-Fi追跡データでの大規模な観光地の持続可能な開発に向けた魅力的な目的地の抽出，3）ETC2.0プローブデータを使用して，様々な道路タイプを考慮した旅行の信頼性に対する降雪の影響の評価．以上の研究から，複数視点の考察を積み重ね，包括的な評価と提案を行い，いくつかの重要な結果が得られた．この論文の貢献は，より良い社会への問題解決への糸口となり，今後の政策立案者にとって有意義な内容となるだろう．According to sustainability, the trend is spreading out around the world for past decades. There are many area subjects involved, such as city planning, transportation planning, and so on, because people realized human activities harmful to the environment by consuming natural resources with less efficiency process or damage environment by social and economic movements. Currently, emerging technologies considered for the proactive procedure in extensive study areas regarding new technology application and knowledge based. In term of transport and urban study, including tourism concerns, we used intelligent data from deferent sources to be demonstrating the possible solutions which involve sustainable urban development concept. In this study, as a method of utilizing data that contributes to the sustainability of cities and regions, consideration of attractive destination management for tourists by using wireless probe data, and the weather impact on travel time reliability observation by using electronic toll collection probe data, it represented as combination experiments throughout comprehensive study. This dissertation addressed three contribution studies to the composed acknowledgment of urban mobility, and it obtained the intelligent data and specific method of research-based. It consists of; 1) an association rule mining-based exploration of travel patterns in wide tourism areas using a Wi-Fi package sensing survey, 2) Attractive destinations mining towards massive tourism area sustainable development on Wi-Fi tracking data, and 3) Assessment of the impact of snowfall on travel reliability considering different road types using ETC2.0 probe data. Hence, a stack of varying viewpoints researches provided a comprehensive review and suggestion throughout significant results. The contribution of this dissertation could be an advantage substance for strategy and policies planner to recognize alternative solutions leading to a better society.室蘭工業大学 (Muroran Institute of Technology)博士（工学

Utilizing Call Detail Records for Travel Mode Discovery in Urban Areas

Mobile network operators often bill their customers based on their network usage. For this purpose, operators collect information about billable events, such as calls, text messages, and data usage. In recent years, operators have realized that they can monetize these billing records by selling insights extracted from them. In this thesis, a multi-stage data analysis algorithm is presented that uses these billing records for travel mode classification. This algorithm identifies whether a mobile phone user has traveled using a public transportation bus or using another transportation mode. The billing records collected by a network operator contain the time at which a billable event happened, as well as the network cell from which the event originated. The coverage area of each network cell is known to the operator. Therefore, the billing records of a mobile phone user give an overview of that user’s approximate location at different times. This data can be used to discover the sequence of network cells that the user has traveled through during a trip. Travel mode classification algorithms in literature analyze long-distance or medium- distance trips. The data analysis algorithm presented in this thesis is novel for analyzing and classifying short-distance, intra-city trips. To classify mobility traces, it uses publicly available bus timetable data and road network infrastructure data. The accuracy of the classification algorithm is evaluated using a two-fold cross-validation analysis

INTERRELATIONSHIPS BETWEEN USERS AND SYSTEM FLEXIBILITIES WITH PERCEIVED USABILITY OF ONLINE AIRLINE RESERVATION SYSTEMS

It is very critical for the organizations to design flexible systems that are easy to use and can accomplish all the requirements by way of offering customizability. Philosophers argue that users are good in adapting the systems; however, research shows users dissatisfaction with existing Online Airline Reservation Systems in terms of task completion. Therefore, researchers are eager to find out ways for improving online usability of the systems, how users' Perceived Usability of the system is formulated by its flexibility functions. This research therefore examines travelers' expectations, preferences and online behavior (Users' Flexibility) and aligns that with designing of flexible online airline reservation systems (System's Flexibility) and users' as evaluators of the online systems to determine its Perceived Usability through users' effectiveness, efficiency and satisfaction (Perceived Usability). In this dissertation, both quantitative and qualitative techniques were used to analyze the data collected in the context of SF, lJF and PU of the systems. A redesign solution for enhanced usability was developed based on HCI guidelines and the flexibility tactics used in online travel agencies, which led to a proposed interface with the integration of opaque mechanism. The two interfaces were used in the experiment. Participants were requested to complete the evaluation of the existing and proposed interfaces. The findings suggested that users can be classified on the basis of their Flexible Traveling Behavior which led to the development of a Users' Flexibility measuring scale. It is further investigated that integration of opaque fares concept would increase the usability of the system. Since flexibility is referred to its ability to respond to internal or external changes, systems incorporated with opaque fares would serve the role of external change agent by way of providing flexibility in users' decision making and will also serve the role of internal change agent by way of providing the capability of accepting changed decisions

Efficient Traffic Management in Urban Environments

[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/10251/129865TESI