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Ureta, Icaza Sebastian. "Machines for living in : communication technologies and everyday life in times of urban transformation." Thesis, London School of Economics and Political Science (University of London), 2006. http://etheses.lse.ac.uk/114/.
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This thesis investigates the degree to which our everyday conceptions of 'place' have changed in contemporary society, especially in relation to the use of information and communications technologies (ICTs). The empirical evidence is a case study of 20 low-income families who live in Santiago, Chile. These families had just moved to a new social housing estate from the shantytowns and/or situations of extreme overcrowding. The first section of the thesis examines how their conceptions of 'place' have changed as a result of the move. On the one hand, it is difficult for them to perceive the housing estate as a 'place' with the same characteristics as their former home environments (close social networks, common history, etc.) due to a difficult and still incomplete adaptation. On the other hand, their social exclusion, especially demonstrated in terms of their limited spatial mobility, means that their everyday life still unfolds in a limited and relatively static number of places. In these circumstances they develop a minimal concept of place based not on an emotional attachment to a space, but rather on particular practices located in certain time and space. This concept of place is labelled here as 'localities of practices'. The second part of the thesis examines how these 'localities of practices' are becoming increasingly 'mediated,' or the increasing degree to which the use of ICTs permeates the conceptions of place of the members of these families through an analysis of practices related to the use of three particular technologies. The first study shows how the home is a project that has to be constructed in a constant competitive interplay with the place created by television use. The second analyses how the noise produced by hi-fi technologies at very high volumes is used to redefine the spaces of the housing estate against the background of their quite limited material surroundings. The third shows how the use of mobile phones, and the 'media space' created by them, reconstitutes and gives a new meaning to the limitations that these families face when moving through the urban environment of Santiago. As a result of these continual processes of mediation the thesis concludes that along with the physical environment of the housing estate, the spatial environments created by the use of media technologies are key to the construction of 'place' to such a degree that is almost impossible to consider one without the other. They, together, are their "machines for living in"; the setting in which their everyday lives unfold.
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Barbieri, Matteo. "Seamless infrastructure for "Big-Data" collection and transportation and distributed elaboration oriented to predictive maintenance of automatic machines." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2017.
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In questo progetto di tesi, realizzato all'interno del laboratorio industriale LIAM Lab, si propone lo sviluppo e la sperimentazione di un'infrastruttura hardware e software per l'acquisizione e l'elaborazione di segnali da sensori di macchine automatiche da utilizzare per effettuare operazioni di diagnostica predittiva su di essa. La tematica sta avendo sempre più seguito all'interno del settore, in quanto la sua realizzazione si basa profondamente sui concetti di industria 4.0, internet delle cose e big data. Nel caso particolare l'infrastruttura riceverà dati da accelerometri con frequenze variabli dai 5KHz a 50KHz e su di questi applicherà un algoritmo di identificazione e semplici test statistici. Successivamente, i parametri dentificati e i risultati dei test verranno poi inviati via OPC ad un computer che provvederà alla loro rielaborazione. Con rielaborazione si intende l'utilizzo di ulteriori test statistici più complessi e anche algoritmi di machine learning. L'infrastruttura ha quindi il compito di "prepare la strada" per l'acquisizione e rielaborazione dei segnali ricevuti dai sensori per poter realizzare in seguito algoritmi in grado di apprendere le condizioni operative della macchina cosicchè sia possibile prevederne produzione e manutenzione.
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Vanajakshi, Lelitha Devi. "Estimation and prediction of travel time from loop detector data for intelligent transportation systems applications." Texas A&M University, 2004. http://hdl.handle.net/1969.1/2667.
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With the advent of Advanced Traveler Information Systems (ATIS), short-term travel time prediction is becoming increasingly important. Travel time can be obtained directly from instrumented test vehicles, license plate matching, probe vehicles etc., or from indirect methods such as loop detectors. Because of their wide spread deployment, travel time estimation from loop detector data is one of the most widely used methods. However, the major criticism about loop detector data is the high probability of error due to the prevalence of equipment malfunctions. This dissertation presents methodologies for estimating and predicting travel time from the loop detector data after correcting for errors. The methodology is a multi-stage process, and includes the correction of data, estimation of travel time and prediction of travel time, and each stage involves the judicious use of suitable techniques. The various techniques selected for each of these stages are detailed below. The test sites are from the freeways in San Antonio, Texas, which are equipped with dual inductance loop detectors and AVI.
?? Constrained non-linear optimization approach by Generalized Reduced Gradient (GRG) method for data reduction and quality control, which included a check for the accuracy of data from a series of detectors for conservation of vehicles, in addition to the commonly adopted checks.
?? A theoretical model based on traffic flow theory for travel time estimation for both off-peak and peak traffic conditions using flow, occupancy and speed values obtained from detectors.
?? Application of a recently developed technique called Support Vector Machines (SVM) for travel time prediction. An Artificial Neural Network (ANN) method is also developed for comparison.
Thus, a complete system for the estimation and prediction of travel time from loop detector data is detailed in this dissertation. Simulated data from CORSIM simulation software is used for the validation of the results.
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Pomikálek, Adam. "Mobilní soustružnické obráběcí centrum se svislou osou obrobku." Master's thesis, Vysoké učení technické v Brně. Fakulta strojního inženýrství, 2021. http://www.nusl.cz/ntk/nusl-443775.
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The subject of this diploma thesis is the conceptual design of a mobile lathe centre with a vertical workpiece axis. First, a search in the field of mobile lathes, stationary lathes and methods of their transport is performed. Then, from the described transport machine options, the most suitable transport variant is selected, thanks to which variants of the mobile machine solution are created. Subsequently, the design of the winning variant is fully adapted to the method of transport using containers. Emphasis is placed on simple construction, maximum use of the transport packaging in the machine frame and easy assembly of the machine customer. For these reasons, the machine frame must consist of two containers. Finally, the assembly and adjustment of the machine tool are solved in combination with the verification of the functionality of the proposed concept.
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Sobue, Gustavo. "Modelagem paramétrica de pórticos rolantes: estabilidade estrutural e otimização." Universidade de São Paulo, 2005. http://www.teses.usp.br/teses/disponiveis/3/3135/tde-30082005-141837/.
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O objetivo deste trabalho é desenvolver uma ferramenta de automatização de cálculo para projeto estrutural de pórticos rolantes. Com o apoio da geração automática de um modelo de elementos finitos e um memorial de cálculo, o projeto estrutural desses equipamentos pode ser rapidamente verificado quanto ao limite de escoamento do material e resistência à flambagem. Optou-se pela utilização do método dos elementos finitos para o cálculo estrutural, pois se trata de uma ferramenta de cálculo moderna, que permite avaliar soluções para as quais não há ferramentas analíticas disponíveis. Porém, o tempo para geração de modelos de cálculo pode ser longo em relação ao cronograma do projeto, principalmente se houver a necessidade de se alterar a geometria inicial ou se existirem várias condições de carregamento a serem analisadas. A utilização de um pré-processador permite que várias alternativas sejam analisadas para escolha da que melhor atenda aos requisitos de projeto e de custo. Assim como ocorre com outras estruturas de engenharia, não existe uma equação de dimensionamento, mas sim de verificação; as estruturas ótimas são procuradas por tentativa e erro com base na experiência do projetista. Para facilitar a busca de uma estrutura ótima, implementou-se também uma rotina para otimizar as estruturas metálicas do pórtico. Adotou-se como função objetivo nesta implementação a minimização da massa, o que no caso dos pórticos implica em redução da área da seção transversal das vigas. Como restrições a esta redução adotaram-se o limite de escoamento do material e limite de estabilidade da estrutura (flambagem). Foram utilizados os aplicativos Excel (Microsoft), Ansys (Ansys Inc.) e Mathcad (Mathsoft) de maneira integrada a fim de se obter uma interface amigável, uma análise estrutural confiável e a elaboração automática de um memorial de cálculo.<br>The objective of this work is to develop a tool to generate an automatic structural design of gantry cranes. With an automatic generation of finite element models and also a design report, this routine allows a fast verification against yield of material and structural instability. The use of the finite element method was chosen for the structural design because it is a modern analysis tool that permits the evaluation of geometric configurations for which there are no analytical formulations available. However, the time necessary to build these models may be high, especially if there are changes in the initial geometry and many load cases. The use of a pre-processor allows the evaluation of a series of geometric alternatives, within which would be chosen the one with the lowest cost that attends the clients specifications. Like many other engineering problems, there are no direct equations to find an adequate structure; there are only verification procedures available; the optimum structures are searched by trial and error, based on the designers experience. To accelerate this search process, an optimization routine was developed. Mass reduction was adopted as the objective function, which leads to reduction of the cross section area of the beams. Yield strength and buckling were adopted as restrictions to this optimization. Excel (Microsoft), Ansys (Ansys Inc) and Mathcad (Mathsoft) software were integrated to provide an user-friendly interface, reliable structural analysis and an automatic report generation.
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Siriwardana, Pallege Gamini Dilupa. "Machine learning-based multi-robot cooperative transportation of objects." Thesis, University of British Columbia, 2009. http://hdl.handle.net/2429/14700.
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Multi robot cooperative transportation is an important research area in the multi robot
domain. In the process of object transportation, several autonomous robots navigate cooperatively in either a static or a dynamic environment to transport an object to a goal location and orientation. The environment may consist of both fixed and removable obstacles and it will be subject to uncertainty and unforeseen changes within the environment. Furthermore, more than one robot may be required in a cooperative mode
for handling heavy and large objects. These are some of the challenges addressed in the
present thesis.
This thesis develops a machine learning approach and investigates relevant research
issues for object transportation utilizing cooperative and autonomous multiple mobile
robots. It makes significant original contributions in distributed multi robot coordination and self deterministic learning for robot decision making, and comes up with an optimal solution to the action selection conflicts of the robots in the cooperative system. This will help to improve the real time performance and robustness of the system. Also, the thesis develops a new method for object and obstacle identification in complex environments
using a laser range finder, which is more realistic than the available methods. A new
algorithm for object pose estimation algorithm is developed, enabling a robot to identify the objects and obstacles in a multi-robot environment by utilizing the laser range finder and color blob tracking.
The thesis develops a fully distributed hierarchical multi-robot architecture for enhanced coordination among robots in a dynamic and unknown environment. It strives to improve the real time performance and robustness. The system consists with three layers. By combining two popular artificial intelligence (Al) techniques such as learning and behavior based decision making, the developed architecture is expected to facilitate effective autonomous operation of cooperative multi-robot systems in a dynamically changing, unstructured, and unknown environment.
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Fang, Yajun Ph D. Massachusetts Institute of Technology. "Fusion-layer-based machine vision for intelligent transportation systems/." Thesis, Massachusetts Institute of Technology, 2010. http://hdl.handle.net/1721.1/60143.
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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2010.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (p. 307-317).<br>Environment understanding technology is very vital for intelligent vehicles that are expected to automatically respond to fast changing environment and dangerous situations. To obtain perception abilities, we should automatically detect static and dynamic obstacles, and obtain their related information, such as, locations, speed, collision/occlusion possibility, and other dynamic current/historic information. Conventional methods independently detect individual information, which is normally noisy and not very reliable. Instead we propose fusion-based and layered-based information-retrieval methodology to systematically detect obstacles and obtain their location/timing information for visible and infrared sequences. The proposed obstacle detection methodologies take advantage of connection between different information and increase the computational accuracy of obstacle information estimation, thus improving environment understanding abilities, and driving safety.<br>by Yajun Fang.<br>Ph.D.
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Martin, Sébastien Ph D. Massachusetts Institute of Technology. "The edge of large-scale optimization in transportation and machine learning." Thesis, Massachusetts Institute of Technology, 2019. https://hdl.handle.net/1721.1/122388.
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This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.<br>Thesis: Ph. D., Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2019<br>Cataloged from student-submitted PDF version of thesis.<br>Includes bibliographical references (pages 273-284).<br>This thesis focuses on impactful applications of large-scale optimization in transportation and machine learning. Using both theory and computational experiments, we introduce novel optimization algorithms to overcome the tractability issues that arise in real world applications. We work towards the implementation of these algorithms, through software contributions, public policy work, and a formal study of machine learning interpretability. Our implementation in Boston Public Schools generates millions of dollars in yearly transportation savings and led to important public policy consequences in the United States. This work is motivated by large-scale transportation problems that present significant optimization challenges. In particular, we study the problem of ride-sharing, the online routing of hundreds of thousands of customers every day in New York City.<br>We also contribute to travel time estimation from origin-destination data, on city routing networks with tens of thousands of roads. We additionally consider the problem of school transportation, the scheduling of hundreds of buses to send tens of thousands of children to school everyday. This transportation problem is related to the choice of school start times, for which we also propose an optimization framework. Building on these applications, we present methodological contributions in large- scale optimization. We introduce state-of-the-art algorithms for scheduling problems with time-window (backbone) and for school bus routing (BiRD). Our work on travel time estimation tractably produces solutions to the inverse shortest path length problem, solving a sequence of second order cone problems. We also present a theoretical and empirical study of the stochastic proximal point algorithm, an alternative to stochastic gradient methods (the de-facto algorithm for large-scale learning).<br>We also aim at the implementation of these algorithms, through software contributions, public policy work (together with stakeholders and journalists), and a collaboration with the city of Boston. Explaining complex algorithms to decision-makers is a difficult task, therefore we introduce an optimization framework to decomposes models into a sequence of simple building blocks. This allows us to introduce formal measure of the "interpretability" of a large class of machine learning models, and to study tradeoffs between this measure and model performance, the price of interpretability.<br>by Sébastien Martin.<br>Ph. D.<br>Ph.D. Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center
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Carpineti, Claudia. "Sensors relevance analysis for transportation mode recognition." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2017. http://amslaurea.unibo.it/13298/.
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Identificare la mobilità di un utente attraverso la sua osservazione, o l’osservazione dell’ambiente, è un tema di ricerca di crescente interesse, con numerose applicazioni.
La maggior parte dei lavori accademici trae vantaggio dalla diffusione dei cellulari, utilizzando i dati derivati dai sensori e le tecniche di apprendimento automatico, per inferire la mobilità dell’utente.
Una limitazione di questi lavori è l’utilizzo di dataset creati ad hoc. Essi, infatti, raccolgono i dati dei soli sensori di loro interesse, hanno una base utente ridotta e prevedono processi di collezione in condizioni non reali. Come conseguenza, i risultati dei lavori non sono fra loro confrontabili.
Il primo obiettivo di questa tesi è stato la costruzione di un dataset che superasse le suddette limitazioni. Il dataset ottenuto distingue cinque attività: stare in macchina, in autobus, in treno, fermi e camminare.
Il dataset è stato utilizzato per la costruzione di modelli di classificazione della mobilità, raggiungendo un livello massimo di accuratezza del 96%.
Si è indagato sull’importanza dei sensori nel riconoscimento delle singole attività. A tal fine, sono stati definiti tre insiemi di dati: il primo composto dai dati derivati da tre soli sensori (accelerometro, giroscopio e microfono), il secondo da tutti i dati dei sensori ad esclusione di quelli derivati dal GPS e l’ultimo dai dati di tutti i sensori disponibili.
I risultati ottenuti mostrano come all’aumentare dei sensori, aumenti l’accuratezza. Non tutte le classi di attività, però, traggono lo stesso beneficio dall’aumento di informazione. L’analisi di queste differenze permette di individuare quali sensori sono più utili all’individuazione di ogni singola attività.
Questi ultimi risultati suggeriscono la possibilità di scegliere il set di sensori da utilizzare sulla base delle attività da riconoscere.
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Trivedi, Ankit P. "Decision tree-based machine learning algorithm for in-node vehicle classification." Thesis, California State University, Long Beach, 2017. http://pqdtopen.proquest.com/#viewpdf?dispub=10196455.
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<p> This paper proposes an in-node microprocessor-based vehicle classification approach to analyze and determine the types of vehicles passing over a 3-axis magnetometer sensor. The approach for vehicle classification utilizes J48 classification algorithm implemented in Weka (a machine learning software suite). J48 is Quinlan's C4.5 algorithm, an extension of decision tree machine learning based on an ID3 algorithm. The decision tree model is generated from a set of features extracted from vehicles passing over the 3-axis sensor. The features are attributes provided with correct classifications to the J48 training algorithm to generate a decision tree model with varying degrees of classification rates based on cross-validation. Ideally, using fewer attributes to generate the model allows for the highest computational efficiency due to fewer features needed to be calculated while minimalizing the tree with fewer branches. The generated tree model can then be easily implemented using nested if-loops in any language on a multitude of microprocessors. Also, setting an adaptive baseline to negate the effects of the background magnetic field allows reuse of the same tree model in multiple environments. The result of the experiment shows that the vehicle classification system is effective and efficient.</p>
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Guo, Zhuo. "Tabu search for parallel identical machine disruption problem considering multiple transportation modes /." View abstract or full-text, 2006. http://library.ust.hk/cgi/db/thesis.pl?IEEM%202006%20GUO.
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Srněnský, Jan. "Návrh systému automatické výměny vřetenových hlav." Master's thesis, Vysoké učení technické v Brně. Fakulta strojního inženýrství, 2017. http://www.nusl.cz/ntk/nusl-318528.
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The purpose of this thesis is to design system for automatic exchange of spindle heads shared by several machines. Spindle heads are transported between these machines and placed to the position for exchange. The system also allows storing of heads. Requirements for system are load capacity of 2 000 kg and placing a spindle head to the exchange position with precision of ± 0,02 mm. The thesis consists of three parts. The first part involves search of head exchange principles and shows possible ways to transport heads and other elements that could be used in the system. In the next part are several possible solutions introduced and the selection of the best one is made. The last part contains the design of the chosen solution, visualisation, description and calculations. The result is design of two solutions which are described in this work.
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Monmousseau, Philippe. "Passengers : customers, actors and sensors of the air transportation system." Thesis, Toulouse 3, 2020. http://www.theses.fr/2020TOU30244.
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Le transport aérien est fondé sur l'utilisation de l'avion pour transporter des passagers entre deux aéroports, et son développement est allé de pair avec l'amélioration continue de l'efficacité et de la sécurité des avions comme moyens de transport. Cependant, si la pandémie liée au COVID-19 nous a appris une leçon, c'est qu'un problème qui touche les passagers du transport aérien peut avoir bien plus de conséquences sur le système dans son ensemble qu'un problème qui concerne les avions. Partant du principe que les passagers sont omniprésents et nécessaires au transport aérien, cette thèse propose de considérer les passagers comme des capteurs du transport aérien, et d'utiliser les données générées par les passagers pour évaluer la performance du transport aérien en quasi temps réel. Ces données générées par les passagers ont également l'avantage d'offrir un moyen d'évaluer les interactions entre les passagers et les autres acteurs du transport aérien, en particulier les aéroports et les compagnies aériennes. Comme le parcours d'un passager commence et se termine au delà des limites d'un aéroport, les données générées par les passagers tout au long de ce parcours peuvent également être utilisées pour évaluer le trajet porte-à-porte complet d'un passager du transport aérien<br>Air transportation uses planes to transport passengers efficiently between two airports, and its development has been driven by the continuous improvement of planes as a safe and efficient means of transportation. However, if the COVID-19 pandemic has taught the air transportation system one lesson, it's that a problem affecting passengers can be far more detrimental to the air transportation system than a problem affecting planes. Acknowledging the fact that passengers are omnipresent and necessary to the air transportation system, this study proposes to consider passengers as sensors of the air transportation system and harness data generated by passengers to evaluate in near real time the flight-centric metrics traditionally used to evaluate the air transportation system performance. Data generated by passengers have the additional benefit of offering a means of evaluating the interactions between passengers and the other stakeholders of the air transportation system, such as airlines and airports. The journey of a passenger starting and ending beyond the boundaries of airport facilities, the data generated by passengers throughout their journey can also be used to evaluate the full door-to-door journey of a passenger of the air transportation system
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Di, Ciccio Claudio, der Aa Han van, Macias Cristina Cabanillas, Jan Mendling, and Johannes Prescher. "Detecting flight trajectory anomalies and predicting diversions in freight transportation." Elsevier, 2016. http://dx.doi.org/10.1016/j.dss.2016.05.004.
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Timely identifying flight diversions is a crucial aspect of efficient multi-modal transportation. When an airplane diverts, logistics providers must promptly adapt their transportation plans in order to ensure proper delivery despite such an unexpected event. In practice, the different parties in a logistics chain do not exchange real-time information related to flights. This calls for a means to detect diversions that just requires publicly available data, thus being independent of the communication between different parties. The dependence on public data results in a challenge to detect anomalous behavior without knowing the planned flight trajectory. Our work addresses this challenge by introducing a prediction model that just requires information on an airplane's position, velocity, and intended destination. This information is used to distinguish between regular and anomalous behavior. When an airplane displays anomalous behavior for an extended period of time, the model predicts a diversion. A quantitative evaluation shows that this approach is able to detect diverting airplanes with excellent precision and recall even without knowing planned trajectories as required by related research. By utilizing the proposed prediction model, logistics companies gain a significant amount of response time for these cases.
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Golshan, Arman. "A contemporary machine learning approach to detect transportation mode - A case study of Borlänge, Sweden." Thesis, Högskolan Dalarna, Mikrodataanalys, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:du-35966.
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Understanding travel behavior and identifying the mode of transportation are essential for adequate urban devising and transportation planning. Global positioning systems (GPS) tracking data is mainly used to find human mobility patterns in cities. Some travel information, such as most visited location, temporal changes, and the trip speed, can be easily extracted from GPS raw tracking data. GPS trajectories can be used as a method to indicate the mobility modes of commuters. Most previous studies have applied traditional machine learning algorithms and manually computed data features, making the model error-prone. Thus, there is a demand for developing a new model to resolve these methods' weaknesses. The primary purpose of this study is to propose a semi-supervised model to identify transportation mode by using a contemporary machine learning algorithm and GPS tracking data. The model can accept GPS trajectory with adjustable length and extracts their latent information with LSTM Autoencoder. This study adopts a deep neural network architecture with three hidden layers to map the latent information to detect transportation mode. Moreover, different case studies are performed to evaluate the proposed model's efficiency. The model results in an accuracy of 93.6%, which significantly outperforms similar studies.
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Dabiri, Sina. "Application of Deep Learning in Intelligent Transportation Systems." Diss., Virginia Tech, 2019. http://hdl.handle.net/10919/87409.
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The rapid growth of population and the permanent increase in the number of vehicles engender several issues in transportation systems, which in turn call for an intelligent and cost-effective approach to resolve the problems in an efficient manner. A cost-effective approach for improving and optimizing transportation-related problems is to unlock hidden knowledge in ever-increasing spatiotemporal and crowdsourced information collected from various sources such as mobile phone sensors (e.g., GPS sensors) and social media networks (e.g., Twitter). Data mining and machine learning techniques are the major tools for analyzing the collected data and extracting useful knowledge on traffic conditions and mobility behaviors. Deep learning is an advanced branch of machine learning that has enjoyed a lot of success in computer vision and natural language processing fields in recent years. However, deep learning techniques have been applied to only a small number of transportation applications such as traffic flow and speed prediction. Accordingly, my main objective in this dissertation is to develop state-of-the-art deep learning architectures for resolving the transport-related applications that have not been treated by deep learning architectures in much detail, including (1) travel mode detection, (2) vehicle classification, and (3) traffic information system. To this end, an efficient representation for spatiotemporal and crowdsourced data (e.g., GPS trajectories) is also required to be designed in such a way that not only be adaptable with deep learning architectures but also contains efficient information for solving the task-at-hand. Furthermore, since the good performance of a deep learning algorithm is primarily contingent on access to a large volume of training samples, efficient data collection and labeling strategies are developed for different data types and applications. Finally, the performance of the proposed representations and models are evaluated by comparing to several state-of-the-art techniques in literature. The experimental results clearly and consistently demonstrate the superiority of the proposed deep-learning based framework for each application.<br>PHD
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Gay, Juliana Siqueira. "Learning spatial inequalities: an approach to support transportation planning." Universidade de São Paulo, 2018. http://www.teses.usp.br/teses/disponiveis/3/3138/tde-03052018-103817/.
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Part of the literature of transportation planning understand transportation infrastructure as a mean of distributing people and opportunities across the territory. Therefore, the spatial inequalities become a relevant issue in transportation and land use planning. To meet the challenge of evaluating the heterogeneity of transportation provision and land use in the urban environment, this work aims at identifying and describing patterns hidden the distribution of accessibility to different urban facilities and socioeconomic information using Machine Learning (ML) techniques to inform the decision making of transportation plans. To feature the current consideration of spatial inequalities measures in the practice of transportation planning in Brazil, nine mobility plans were reviewed. For investigating the potentialities and restrictions of ML application, unsupervised and supervised analysis of income and accessibility indicators to health, education and leisure were performed. The data of the São Paulo municipality from the years of 2000 and 2010 was explored. The analyzed plans do not present measures for evaluating spatial inequalities. It is possible to identify that the low-income population has low accessibility to all facilities, especially, hospital and cultural centers. The east zone of the city presents a low-income group with intermediate level to public schools and sports centers, revealing the heterogeneity in regions out of the city center. Finally, a framework is proposed to incorporate spatial inequalities by using ML techniques in transportation plans.<br>Parte da literatura de planejamento de transportes conceitua a infraestrutura de transportes como uma forma de distribuir pessoas e oportunidades no território. Portanto, as desigualdades espaciais tornaram-se uma questão relevante a ser endereçada no planejamento de transportes e uso do solo. De maneira a contribuir com o desafio de avaliar desigualdades e sua heterogeneidade no ambiente urbano, esse trabalho tem como objetivo identificar e descrever padrões existentes na distribuição acessibilidade a diferentes equipamentos urbanos e dados socioeconômicos por meio de técnicas de Aprendizagem de Máquina (AM) para informar a tomada de decisão em planos de transportes. De forma a caracterizar a atual consideração de métricas de desigualdades espaciais na prática do planejamento de transportes no Brasil, nove planos de mobilidade foram revisados. Para investigar as potencialidades e restrições da aplicação de AM, análises supervisionadas e não supervisionadas de indicadores de renda e acessibilidade a saúde, educação e lazer foram realizadas. Os dados do município de São Paulo dos anos de 2000 e 2010 foram explorados. Os Planos de Mobilidade analisados não apresentam medidas para avaliação de desigualdades espaciais. Além disso, é possível identificar que a população de baixa renda tem baixa acessibilidade a todos os equipamentos urbanos, especialmente hospitais e centros culturais. A zona leste da cidade apresenta um grupo de baixa renda com nível intermediário de acessibilidade a escolas públicas e centros esportivos, evidenciando a heterogeneidade nas regiões periféricas da cidade. Finalmente, um quadro de referência é proposto para incorporação de técnicas de AM no planejamento de transportes.
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TANG, LI. "Automatic Extraction of Number of Lanes from Aerial Images for Transportation Applications." FIU Digital Commons, 2015. http://digitalcommons.fiu.edu/etd/2200.
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Number of lanes is a basic roadway attribute that is widely used in many transportation applications. Traditionally, number of lanes is collected and updated through field surveys, which is expensive especially for large coverage areas with a high volume of road segments. One alternative is through manual data extraction from high-resolution aerial images. However, this is feasible only for smaller areas. For large areas that may involve tens of thousands of aerial images and millions of road segments, an automatic extraction is a more feasible approach. This dissertation aims to improve the existing process of extracting number of lanes from aerial images automatically by making improvements in three specific areas: (1) performance of lane model, (2) automatic acquisition of external knowledge, and (3) automatic lane location identification and reliability estimation. In this dissertation, a framework was developed to automatically recognize and extract number of lanes from geo-rectified aerial images. In order to address the external knowledge acquisition problem in this framework, a mapping technique was developed to automatically estimate the approximate pixel locations of road segments and the travel direction of the target roads in aerial images. A lane model was developed based on the typical appearance features of travel lanes in color aerial images. It provides more resistance to “noise” such as presence of vehicle occlusions and sidewalks. Multi-class classification test results based on the K-nearest neighbor, logistic regression, and Support Vector Machine (SVM) classification algorithms showed that the new model provides a high level of prediction accuracy. Two optimization algorithms based on fixed and flexible lane widths, respectively, were then developed to extract number of lanes from the lane model output. The flexible lane-width approach was recommended because it solved the problems of error-tolerant pixel mapping and reliability estimation. The approach was tested using a lane model with two SVM classifiers, i.e., the Polynomial kernel and the Radial Basis Function (RBF) kernel. The results showed that the framework yielded good performance in a general test scenario with mixed types of road segments and another test scenario with heavy plant occlusions.
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Nyberg, Roger Gote. "Automating condition monitoring of vegetation on railway trackbeds and embankments." Thesis, Edinburgh Napier University, 2015. http://researchrepository.napier.ac.uk/Output/462294.
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Vegetation growing on railway trackbeds and embankments present potential problems. The presence of vegetation threatens the safety of personnel inspecting the railway infrastructure. In addition vegetation growth clogs the ballast and results in inadequate track drainage which in turn could lead to the collapse of the railway embankment. Assessing vegetation within the realm of railway maintenance is mainly carried out manually by making visual inspections along the track. This is done either on-site or by watching videos recorded by maintenance vehicles mainly operated by the national railway administrative body. A need for the automated detection and characterisation of vegetation on railways (a subset of vegetation control/management) has been identified in collaboration with local railway maintenance subcontractors and Trafikverket, the Swedish Transport Administration (STA). The latter is responsible for long-term planning of the transport system for all types of traffic, aswell as for the building, operation and maintenance of public roads and railways. The purpose of this research project was to investigate how vegetation can be measured and quantified by human raters and how machine vision can automate the same process. Data were acquired at railway trackbeds and embankments during field measurement experiments. All field data (such as images) in this thesis work was acquired on operational, lightly trafficked railway tracks, mostly trafficked by goods trains. Data were also generated by letting (human) raters conduct visual estimates of plant cover and/or count the number of plants, either on-site or in-house by making visual estimates of the images acquired from the field experiments. Later, the degree of reliability of (human) raters' visual estimates were investigated and compared against machine vision algorithms. The overall results of the investigations involving human raters showed inconsistency in their estimates, and are therefore unreliable. As a result of the exploration of machine vision, computational methods and algorithms enabling automatic detection and characterisation of vegetation along railways were developed. The results achieved in the current work have shown that the use of image data for detecting vegetation is indeed possible and that such results could form the base for decisions regarding vegetation control. The performance of the machine vision algorithmwhich quantifies the vegetation cover was able to process 98% of the image data. Investigations of classifying plants from images were conducted in in order to recognise the specie. The classification rate accuracy was 95%. Objective measurements such as the ones proposed in thesis offers easy access to the measurements to all the involved parties and makes the subcontracting process easier i.e., both the subcontractors and the national railway administration are given the same reference framework concerning vegetation before signing a contract, which can then be crosschecked post maintenance. A very important issue which comes with an increasing ability to recognise species is the maintenance of biological diversity. Biological diversity along the trackbeds and embankments can be mapped, and maintained, through better and robust mo nitoring procedures. Continuously monitoring the state of vegetation along railways is highly recommended in order to identify a need for maintenance actions, and in addition to keep track of biodiversity. The computational methods or algorithms developed formthe foundation of an automatic inspection system capable of objectively supporting manual inspections, or replacing manual inspections.
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Yusuf, Adeel. "Advanced machine learning models for online travel-time prediction on freeways." Diss., Georgia Institute of Technology, 2013. http://hdl.handle.net/1853/50408.
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The objective of the research described in this dissertation is to improve the travel-time prediction process using machine learning methods for the Advanced Traffic In-formation Systems (ATIS). Travel-time prediction has gained significance over the years especially in urban areas due to increasing traffic congestion. The increased demand of the traffic flow has motivated the need for development of improved applications and frameworks, which could alleviate the problems arising due to traffic flow, without the need of addition to the roadway infrastructure.
In this thesis, the basic building blocks of the travel-time prediction models are discussed, with a review of the significant prior art. The problem of travel-time prediction was addressed by different perspectives in the past. Mainly the data-driven approach and the traffic flow modeling approach are the two main paths adopted viz. a viz. travel-time prediction from the methodology perspective. This dissertation, works towards the im-provement of the data-driven method.
The data-driven model, presented in this dissertation, for the travel-time predic-tion on freeways was based on wavelet packet decomposition and support vector regres-sion (WPSVR), which uses the multi-resolution and equivalent frequency distribution ability of the wavelet transform to train the support vector machines. The results are compared against the classical support vector regression (SVR) method. Our results indi-cate that the wavelet reconstructed coefficients when used as an input to the support vec-tor machine for regression (WPSVR) give better performance (with selected wavelets on-ly), when compared against the support vector regression (without wavelet decomposi-tion).
The data used in the model is downloaded from California Department of Trans-portation (Caltrans) of District 12 with a detector density of 2.73, experiencing daily peak hours except most weekends. The data was stored for a period of 214 days accumulated over 5 minute intervals over a distance of 9.13 miles. The results indicate an improvement in accuracy when compared against the classical SVR method.
The basic criteria for selection of wavelet basis for preprocessing the inputs of support vector machines are also explored to filter the set of wavelet families for the WDSVR model. Finally, a configuration of travel-time prediction on freeways is present-ed with interchangeable prediction methods along with the details of the Matlab applica-tion used to implement the WPSVR algorithm.
The initial results are computed over the set of 42 wavelets. To reduce the compu-tational cost involved in transforming the travel-time data into the set of wavelet packets using all possible mother wavelets available, a methodology of filtering the wavelets is devised, which measures the cross-correlation and redundancy properties of consecutive wavelet transformed values of same frequency band.
An alternate configuration of travel-time prediction on freeways using the con-cepts of cloud computation is also presented, which has the ability to interchange the pre-diction modules with an alternate method using the same time-series data.
Finally, a graphical user interface is described to connect the Matlab environment with the Caltrans data server for online travel-time prediction using both SVR and WPSVR modules and display the errors and plots of predicted values for both methods. The GUI also has the ability to compute forecast of custom travel-time data in the offline mode.
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Hussain, Bilaal Yusef. "Dynamic simulations of carbon dioxide pipeline transportation for the purpose of carbon capture and storage." Thesis, University of Birmingham, 2018. http://etheses.bham.ac.uk//id/eprint/8575/.
Full textAbstract:
This Engineering Doctorate project aimed to study the effects of varying flowrates on the flow dynamics of carbon dioxide within a pipeline. The researched utilised the software gCCS to simulate three different transport system. The first system looked at the effects of transporting pure carbon dioxide in both the supercritical phase and the sub-cooled liquid phase. The outputs from the model showed that when the inlet flowrate is decreased, the outlet flowrate responds in three distinct phases. The second system compared the effects of three different impurities; hydrogen, nitrogen and oxygen, on the flow response when the inlet flowrate is decreased. It was found that all three impurities caused an increase in the offset between the inlet and outlet flowrate. The third system looked at how multiple sources of carbon dioxide effect the flowrate within the main trunk pipeline. It was found that multiple sources of carbon dioxide do not affect the flow of CO2 within the pipeline beyond that of the base case. The final part of the research compared data from the Shell QUEST pipeline to the model. This showed the model was able to predict the flowrate and pressure of the carbon dioxide with good accuracy.
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Panovski, Dancho. "Simulation, optimization and visualization of transportation data." Electronic Thesis or Diss., Institut polytechnique de Paris, 2020. http://www.theses.fr/2020IPPAS016.
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Aujourd'hui, toutes les grandes métropoles de France, d'Europe et du reste du monde souffrent de graves problèmes de congestion et de saturation des infrastructures routières, qui concernent à la fois les transports individuels et publics. Les systèmes de transport actuels atteignent leurs limites de capacité et semblent incapables d'absorber l'augmentation des flux de passagers à l'avenir.Dans ce contexte, l'un des principaux défis à relever concerne la création de méthodologies dédiées pour l'analyse des données de transport géo-localisées pour le stockage instantané, l'analyse, la gestion et la diffusion de flux de données massives. Les algorithmes associés doivent être capables de gérer des listes d'événements de plusieurs dizaines de minutes pour calculer des trajectoires réelles, des occupations instantanées, des cycles de changement de feux de circulation ainsi que des prévisions de flux de circulation de véhicules.Dans cette thèse, nous abordons deux différentes problématiques liées à ce sujet.Une première contribution concerne l'optimisation des systèmes de feux tricolores. L'objectif est de minimiser le temps de trajet total des véhicules présents dans une certaine partie d'une ville. Dans ce but, nous proposons une technique d’optimization de type PSO (Particle Swarm Optimization). Les résultats expérimentaux obtenus montrent qu'une telle approche permet d'obtenir des gains importants (5.37% - 21.53%) en termes de temps de parcours moyen global des véhicules.La deuxième partie de la thèse est consacrée à la problématique de la prédiction des flux de trafic. En particulier, nous nous concentrons sur la prédiction de l'heure d'arrivée des bus dans les différentes stations présentes sur un itinéraire donné. Ici, nos contributions concernent tout d'abord l'introduction d'un nouveau modèle de données, appelé TDM (Traffic Density Matrix), qui capture dynamiquement la situation du trafic tout au long d'un itinéraire de bus donné. Ensuite, nous montrons comment différentes techniques d'apprentissage statistique peuvent exploiter une cette structure de données afin d'effectuer une prédiction efficace. L'analyse des résultats obtenus par les méthodes traditionnelles (régression linéaire, SVR avec différents noyaux…) montre que l'augmentation du niveau de non-linéarité permet d’obtenir des performences supérieures. En partant de ce constat, nous proposons différentes techniques de deep learning avec des réseaux conçus sur mesure, que nous avons spécifiquement adaptés à nos objectifs. L'approche proposée inclut des réseaux de neurones récurrents, des approches de type LSTM (Long Short Time Memory), des réseaux entièrement connectés et enfin convolutionnels. L'analyse des résultats expérimentaux obtenus confirme notre intuition initiale et démontre que ces techniques hautement non-linéaires surpassent les approches traditionnelles et sont capables de prendre en compte les singularités qui apparaissent dans ce type de données et qui, dans notre cas, correspondent à des embouteillages localisés qui affectent globalement le comportement du système.En raison du manque de disponibilité de ce type d'informations géo-localisées qui très sensibles et soumises à des réglementations variées, toutes les données prises en compte dans nos expériments ont été générées à l'aide du simulateur microscopique SUMO (Simulation of Urban Mobility). Nous montrons notamment comment SUMO peut être exploité pour construire des scénarios réalistes, proches de situations réelles et exploitables à des fins d'analyse.Enfin, une dernière contribution concerne l’élaboration et la mise en œuvre de deux applications de visualisation différentes, une première dédiée aux opérateurs et la seconde aux clients. Pour assurer le déploiement et la compatibilité de ces applications sur différents terminaux (PC, ordinateurs portables, smartphones, tablettes…), une solution scalable est proposée<br>Today all major metropolises in France, Europe and the rest of the world suffer from severe problems of congestion and saturation of infrastructures, which concern both individual and public transport. Current transportation systems are reaching capacity limits and appear unable to absorb increases in passenger flows in the future. The transport of the future is part of the various so-called Smart City initiatives and should be ”intelligent”, that is to say not only react to the demands but anticipate them, relying on the data exchanged between the end user and the information system of transportation operators.Within this context, one of the main challenges is the creation of appropriate methodologies for analysis of geo-localized transport data for instantaneous storage, analysis, management and dissemination of massive (typically thousands of instant geo-localizations , with a refresh rate of the order of a few seconds) data flows. The related algorithms must be capable of managing event lists of several tens of minutes to calculate real trajectories, instantaneous occupations, traffic lights changing cycles as well as vehicular traffic flow forecasts.In this thesis, we address two different issues related to this topic.A first contribution concerns the optimization of the traffic lights systems. The objective is to minimize the total journey time of the vehicles that are present in a certain part of a city. To this purpose, we propose a PSO (Particle Swarm Optimization) technique. The experimental results obtained show that such an approach makes it possible to obtain significant gains (5.37% - 21.53%) in terms of global average journey time.The second part of the thesis is dedicated to the issue of traffic flow prediction. In particular, we focus on prediction of the bus arrival time in the various bus stations existent over a given itinerary. Here, our contributions first concern the introduction of a novel data model, so-called TDM (Traffic Density Matrix), which captures dynamically the situation of the traffic along a given bus itinerary. Then, we show how different machine learning (ML) techniques can exploit such a structure in order to perform efficient prediction. To this purpose, we consider first traditional ML techniques, including linear regression and support vector regression with various kernels. The analysis of the results show that increasing the level of non-linearity can lead to superior results. Based on this observation, we propose various deep learning techniques with hand-crafted networks that we have specifically adapted to our objectives. The proposed approach include recurrent neural networks, LSTM (Long Short Time Memory) approaches, fully connected and convolutional networks. The analysis of the obtained experimental results confirm our intuition and demonstrate that such highly non-linear techniques outperform the traditional approaches and are able to deal with the singularities of the data that in this case correspond to localized traffic jams that globally affect the behavior of the system.Due to the lack of availability of such highly sensitive type of geo-localized information, all the data considered in our experiments has been produced with the help of the SUMO (Simulation of Urban Mobility) microscopic simulator. We notably show how SUMO can be exploited to construct realistic scenarios, close to real-life situations and exploitable for analysis purposes.Interpretation and understanding the data is of vital importance, nevertheless an adequate visualization platform is needed to present the results in a visually pleasing and understandable manner. To this purpose, we finally propose two different visualization application, a first one dedicated to the operators and the second one to clients. To ensure the deployment and compatibility of such applications on different devices (desktop PCs, Laptops, Smartphones, tablets…) a scalable solution is proposed
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Huková, Martina. "Stavebně technologická příprava prodejny Smart Light v Bratislavě." Master's thesis, Vysoké učení technické v Brně. Fakulta stavební, 2019. http://www.nusl.cz/ntk/nusl-392005.
Full textAbstract:
The main subject of this diploma thesis is elaboration of construction and technological preparation for the main structure of the Smart Light shop in Bratislava. For main structure has been processed time schedule of the construction, single item budget, machine configuration design, drawing of building site, coordination situation of the building with connection to the infrastructure, safety and health protection during work on building site. Part of the thesis is processing study of main construction technological parts. Diploma thesis in technological prescript focuses on implementation of floor structure with cast epoxy walking surface. There has been elaborated testing and quality plan of this technological part. Additional chapter approximates built-in technology – cooling ceiling structure. For elaboration of this diploma thesis were used programs AutoCAD, CONTEC, BuildPowerS, Microsoft Excel, Microsoft Word.
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Mistry, Dilip. "Building a Predictive Model on State of Good Repair by Machine Learning Algorithm on Public Transportation Rolling Stock." Diss., North Dakota State University, 2018. https://hdl.handle.net/10365/28754.
Full textAbstract:
Achieving and maintaining public transportation rolling stocks in a state of good repair is very crucial to provide safe and reliable services to riders. Besides, transit agencies who seek federal grants must keep their transit assets in a state of good repair. Therefore, transit agencies need an intelligent predictive model for analyzing their transportation rolling stocks, finding out the current condition, and predicting when they need to be replaced or rehabilitated. Since many transit agencies do not have good analytical tools for predicting the service life of vehicles, this simple predictive model would be a valuable resource for their state of good repair needs and their prioritization of capital needs for replacement and rehabilitation. The ability to accurately predict the service life of revenue vehicles is crucial achieving the state of good repair. In this dissertation, three unique tree-based ensemble learning methods have been applied to build three predictive models. The machine learning methods used in this dissertation are random forest regression, gradient boosting regression, and decision tree regression. After evaluation and comparison of the performance results amongst all models, the gradient boosting regression model with the top 30 most important features was found to be the best fit for predicting the service life of transit vehicles. This model can be used to predict the projected retired year for all nationwide vehicles in operation, the single transit agency?s transit vehicle, and any single vehicle. The revenue vehicle inventory data from National Transit Database (NTD) has been used to build the machine learning predictive model. Before feeding the data into the model, a variety of new features were created, missing data were fixed, and extreme values or outliers were handled for the machine learning algorithm.
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Yi, Jianglin. "Transport boundaries for pneumatic conveying." Faculty of Engineering, 2001. http://ro.uow.edu.au/theses/1840.
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Pneumatic conveying is being selected for an increasing number of industrial applications and products and is playing a more vital and integral role in the transportation of solid materials such as plastic pellets, grain and chemicals. However, despite all the minimum conveying velocity research (one of the operating boundaries for pneumatic conveying) that has been undertaken for several decades, the wide scatter and contradictions in the predictions of the minimum conveying velocity for dilute phase pneumatic conveying exist yet, determination of the operating boundaries for pneumatic conveying (mainly maximum conveying velocity for dense phase and minimum conveying velocity for dilute phase) still has been one of the most important tasks to be solved for the design, optimising and upgrade of pneumatic conveying systems as a consequence of that the mechanisms involved in the formation of boundaries between dilute-phase and dense-phase pneumatic conveying through a horizontal pipeline have not been well explored.Saltation velocity was investigated initially in this thesis and then the emphasis was placed on the transition between dilute-phase and dense-phase. With careful observations, it is found that pneumatic conveying of granular solid materials through a horizontal pipeline can exhibit five different flow modes (as the air velocity is decreased): fully suspended flow; strand flow; stable or unstable strand flow over a stationary layer for low solid mass flow rates; stable or unstable strand flow over a slowly moving bed for high solid mass flow rates; low-velocity slug-flow. The pressure fluctuations within the unstable zone result from the flow mode alternation between a strand flow over a stationary layer (or slowly moving bed) and slug flow starting at the inlet due to a decrease in air velocity. The first slug moves quickly at a relatively high velocity and picks up a relatively thick stationary layer in front of it but only deposits a small amount of the material behind it. The increase in slug length and large increase in pressure cause severe pressure fluctuations and pipeline vibrations. Two different flow modes may exist simultaneously in the conveying pipeline: strand flow over a stationary layer or slowly moving bed near the feed point followed by the dilute-phase (suspension) flow of particles. For the latter, material erodes away from the end of the stationary layer or slowly moving bed and is conveyed in the form of small dunes (or pulsating strand flow).Based on the mass balance, force balance, momentum balance and the unstable flow forming mechanism, a theoretical three-layer model for the prediction of the transition zone boundaries has been established. With stability analysis, the boundaries of the transition zone in the state diagram have been identified, and have been found to agree very well with experimental data. According to the model established, the discussion on the influence of design parameters of particle and bulk properties of the material being conveyed and pipe wall properties on boundaries in the state diagram has been conducted.The discussion on the operating boundaries for pneumatic conveying of granular materials has been extended to conveying of powder materials and a principle for classification of granular materials and powder materials, which have different flow mode in PCC, has been proposed.The research also has been carried out on the pressure drop prediction for pneumatic conveying of granular materials in the form of low-velocity slug-flow in order to have a perfect PCC state diagram. A new approach for the direct measurement of stress transmission factor has been developed in this thesis. The effect of the weight of the granular material in the slug on pressure drop is taken in account according to the experimental test results. The model for pressure drop prediction also includes a modified equation for the frontal force of the moving slug - allowing for momentum balance of accelerating particles and the additional force from the stationary layer to resist the movement. The modelling predictions agree very well with test results obtained on poly pellets conveyed through 98 mm and 60.3 mm ID horizontal stainless steel pipelines, each 21 m in length.
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Lundström, Caroline, and Sara Hedberg. "Coordinating transportation services in a hospital environment using Deep Reinforcement Learning." Thesis, Uppsala universitet, Avdelningen för datalogi, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-355737.
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Artificial Intelligence has in the recent years become a popular subject, many thanks to the recent progress in the area of Machine Learning and particularly to the achievements made using Deep Learning. When combining Reinforcement Learning and Deep Learning, an agent can learn a successful behavior for a given environment. This has opened the possibility for a new domain of optimization. This thesis evaluates if a Deep Reinforcement Learning agent can learn to aid transportation services in a hospital environment. A Deep Q-learning Networkalgorithm (DQN) is implemented, and the performance is evaluated compared to a Linear Regression-, a random-, and a smart agent. The result indicates that it is possible for an agent to learn to aid transportation services in a hospital environment, although it does not outperform linear regression on the most difficult task. For the more complex tasks, the learning process of the agent is unstable, and implementation of a Double Deep Q-learning Network may stabilize the process. An overall conclusion is that Deep Reinforcement Learning can perform well on these types of problems and more applied research may result in greater innovations.
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Li, Ying. "Interest management scheme and prediction model in intelligent transportation systems." Diss., Georgia Institute of Technology, 2012. http://hdl.handle.net/1853/45856.
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This thesis focuses on two important problems related to DDDAS: interest management (data distribution) and prediction models. In order to reduce communication overhead, we propose a new interest management mechanism for mobile peer-to-peer systems. This approach involves dividing the entire space into cells and using an efficient sorting algorithm to sort the regions in each cell. A mobile landmarking scheme is introduced to implement this sort-based scheme in mobile peer-to-peer systems. The design does not require a centralized server, but rather, every peer can become a mobile landmark node to take a server-like role to sort and match the regions. Experimental results show that the scheme has better computational efficiency for both static and dynamic matching. In order to improve communication efficiency, we present a travel time prediction model based on boosting, an important machine learning technique, and combine boosting and neural network models to increase prediction accuracy. We also explore the relationship between the accuracy of travel time prediction and the frequency of traffic data collection with the long term goal of minimizing bandwidth consumption. Several different sets of experiments are used to evaluate the effectiveness of this model. The results show that the boosting neural network model outperforms other predictors.
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Zhao, Yiheng. "Towards the Design of Neural Network Framework for Object Recognition and Target Region Refining for Smart Transportation Systems." Thesis, Université d'Ottawa / University of Ottawa, 2018. http://hdl.handle.net/10393/37978.
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Object recognition systems have significant influences on modern life. Face, iris and finger point recognition applications are commonly applied for the security purposes; ASR (Automatic Speech Recognition) is commonly implemented on speech subtitle generation for various videos and audios, such as YouTube; HWR (Handwriting Recognition) systems are essential on the post office for cheque and postcode detection; ADAS (Advanced Driver Assistance System) are well applied to improve drivers’, passages’ and pedestrians’ safety. Object recognition techniques are crucial and valuable for academia, commerce and industry.
Accuracy and efficiency are two important standards to evaluate the performance of recognition techniques. Accuracy includes how many objects can be indicated in real scene and how many of them can be correctly classified. Efficiency means speed for system training and sample testing. Traditional object detecting methods, such as HOG (Histogram of orientated Gradient) feature detector combining with SVM (Support Vector Machine) classifier, cannot compete with frameworks of neural networks in both efficiency and accuracy. Since neural network has better performance and potential for improvement, it is worth to gain insight into this field to design more advanced recognition systems.
In this thesis, we list and analyze sophisticated techniques and frameworks for object recognition. To understand the mathematical theory for network design, state-of-the-art networks in ILSVRC (ImageNET Large Scale Visual Recognition Challenge) are studied. Based on analysis and the concept of edge detectors, a simple CNN (Convolutional Neural Network) structure is designed as a trail to explore the possibility to utilize the network of high width and low depth for region proposal selection, object recognition and target region refining. We adopt Le-Net as the template, taking advantage of multi-kernels of GoogLe-Net.
We made experiments to test the performance of this simple structure of the vehicle and face through ImageNet dataset. The accuracy for the single object detection is 81% in average and for plural object detection is 73.5%. We refined networks through many aspects to reach the final accuracy 95% for single object detection and 89% for plural object detection.
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Heidaripak, Samrend. "PREDICTION OF PUBLIC BUS TRANSPORTATION PLANNING BASED ON PASSENGER COUNT AND TRAFFIC CONDITIONS." Thesis, Mälardalens högskola, Akademin för innovation, design och teknik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-53408.
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Artificial intelligence has become a hot topic in the past couple of years because of its potential of solving problems. The most used subset of artificial intelligence today is machine learning, which is essentially the way a machine can learn to do tasks without getting any explicit instructions. A problem that has historically been solved by common knowledge and experience is the planning of bus transportation, which has been prone to mistakes. This thesis investigates how to extract the key features of a raw dataset and if a couple of machine learning algorithms can be applied to predict and plan the public bus transportation, while also considering the weather conditions. By using a pre-processing method to extract the features before creating and evaluating an k-nearest neighbors model as well as an artificial neural network model, predicting the passenger count on a given route could help planning of the bus transportation. The outcome of the thesis was that the feature extraction was successful, and both models could successfully predict the passenger count based on normal conditions. However, in extreme conditions such as the pandemic during 2020, the models could not be proven to successfully predict the passenger count nor being used to plan the bus transportation.
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Dabiri, Sina. "Semi-Supervised Deep Learning Approach for Transportation Mode Identification Using GPS Trajectory Data." Thesis, Virginia Tech, 2018. http://hdl.handle.net/10919/86845.
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Identification of travelers' transportation modes is a fundamental step for various problems that arise in the domain of transportation such as travel demand analysis, transport planning, and traffic management. This thesis aims to identify travelers' transportation modes purely based on their GPS trajectories. First, a segmentation process is developed to partition a user's trip into GPS segments with only one transportation mode. A majority of studies have proposed mode inference models based on hand-crafted features, which might be vulnerable to traffic and environmental conditions. Furthermore, the classification task in almost all models have been performed in a supervised fashion while a large amount of unlabeled GPS trajectories has remained unused. Accordingly, a deep SEmi-Supervised Convolutional Autoencoder (SECA) architecture is proposed to not only automatically extract relevant features from GPS segments but also exploit useful information in unlabeled data. The SECA integrates a convolutional-deconvolutional autoencoder and a convolutional neural network into a unified framework to concurrently perform supervised and unsupervised learning. The two components are simultaneously trained using both labeled and unlabeled GPS segments, which have already been converted into an efficient representation for the convolutional operation. An optimum schedule for varying the balancing parameters between reconstruction and classification errors are also implemented. The performance of the proposed SECA model, trip segmentation, the method for converting a raw trajectory into a new representation, the hyperparameter schedule, and the model configuration are evaluated by comparing to several baselines and alternatives for various amounts of labeled and unlabeled data. The experimental results demonstrate the superiority of the proposed model over the state-of-the-art semi-supervised and supervised methods with respect to metrics such as accuracy and F-measure.<br>Master of Science<br>Identifying users' transportation modes (e.g., bike, bus, train, and car) is a key step towards many transportation related problems including (but not limited to) transport planning, transit demand analysis, auto ownership, and transportation emissions analysis. Traditionally, the information for analyzing travelers' behavior for choosing transport mode(s) was obtained through travel surveys. High cost, low-response rate, time-consuming manual data collection, and misreporting are the main demerits of the survey-based approaches. With the rapid growth of ubiquitous GPS-enabled devices (e.g., smartphones), a constant stream of users' trajectory data can be recorded. A user's GPS trajectory is a sequence of GPS points, recorded by means of a GPS-enabled device, in which a GPS point contains the information of the device geographic location at a particular moment. In this research, users' GPS trajectories, rather than traditional resources, are harnessed to predict their transportation mode by means of statistical models.
With respect to the statistical models, a wide range of studies have developed travel mode detection models using on hand-designed attributes and classical learning techniques. Nonetheless, hand-crafted features cause some main shortcomings including vulnerability to traffic uncertainties and biased engineering justification in generating effective features. A potential solution to address these issues is by leveraging deep learning frameworks that are capable of capturing abstract features from the raw input in an automated fashion. Thus, in this thesis, deep learning architectures are exploited in order to identify transport modes based on only raw GPS tracks. It is worth noting that a significant portion of trajectories in GPS data might not be annotated by a transport mode and the acquisition of labeled data is a more expensive and labor-intensive task in comparison with collecting unlabeled data. Thus, utilizing the unlabeled GPS trajectory (i.e., the GPS trajectories that have not been annotated by a transport mode) is a cost-effective approach for improving the prediction quality of the travel mode detection model. Therefore, the unlabeled GPS data are also leveraged by developing a novel deep-learning architecture that is capable of extracting information from both labeled and unlabeled data. The experimental results demonstrate the superiority of the proposed models over the state-of-the-art methods in literature with respect to several performance metrics.
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Gutta, Gayatri. "Machine vision for the automatic classification of images acquired from Non-destructive tests." Thesis, Högskolan Dalarna, Datateknik, 2007. http://urn.kb.se/resolve?urn=urn:nbn:se:du-2520.
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This project is based on Artificial Intelligence (A.I) and Digital Image processing (I.P) for automatic condition monitoring of sleepers in the railway track. Rail inspection is a very important task in railway maintenance for traffic safety issues and in preventing dangerous situations. Monitoring railway track infrastructure is an important aspect in which the periodical inspection of rail rolling plane is required.Up to the present days the inspection of the railroad is operated manually by trained personnel. A human operator walks along the railway track searching for sleeper anomalies. This monitoring way is not more acceptable for its slowness and subjectivity. Hence, it is desired to automate such intuitive human skills for the development of more robust and reliable testing methods. Images of wooden sleepers have been used as data for my project. The aim of this project is to present a vision based technique for inspecting railway sleepers (wooden planks under the railway track) by automatic interpretation of Non Destructive Test (NDT) data using A.I. techniques in determining the results of inspection.
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Manne, Mihira. "MACHINE VISION FOR AUTOMATICVISUAL INSPECTION OF WOODENRAILWAY SLEEPERS USING UNSUPERVISED NEURAL NETWORKS." Thesis, Högskolan Dalarna, Datateknik, 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:du-3977.
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The motivation for this thesis work is the need for improving reliability of equipment and quality of service to railway passengers as well as a requirement for cost-effective and efficient condition maintenance management for rail transportation. This thesis work develops a fusion of various machine vision analysis methods to achieve high performance in automation of wooden rail track inspection.The condition monitoring in rail transport is done manually by a human operator where people rely on inference systems and assumptions to develop conclusions. The use of conditional monitoring allows maintenance to be scheduled, or other actions to be taken to avoid the consequences of failure, before the failure occurs. Manual or automated condition monitoring of materials in fields of public transportation like railway, aerial navigation, traffic safety, etc, where safety is of prior importance needs non-destructive testing (NDT).In general, wooden railway sleeper inspection is done manually by a human operator, by moving along the rail sleeper and gathering information by visual and sound analysis for examining the presence of cracks. Human inspectors working on lines visually inspect wooden rails to judge the quality of rail sleeper. In this project work the machine vision system is developed based on the manual visual analysis system, which uses digital cameras and image processing software to perform similar manual inspections. As the manual inspection requires much effort and is expected to be error prone sometimes and also appears difficult to discriminate even for a human operator by the frequent changes in inspected material. The machine vision system developed classifies the condition of material by examining individual pixels of images, processing them and attempting to develop conclusions with the assistance of knowledge bases and features.A pattern recognition approach is developed based on the methodological knowledge from manual procedure. The pattern recognition approach for this thesis work was developed and achieved by a non destructive testing method to identify the flaws in manually done condition monitoring of sleepers.In this method, a test vehicle is designed to capture sleeper images similar to visual inspection by human operator and the raw data for pattern recognition approach is provided from the captured images of the wooden sleepers. The data from the NDT method were further processed and appropriate features were extracted.The collection of data by the NDT method is to achieve high accuracy in reliable classification results. A key idea is to use the non supervised classifier based on the features extracted from the method to discriminate the condition of wooden sleepers in to either good or bad. Self organising map is used as classifier for the wooden sleeper classification.In order to achieve greater integration, the data collected by the machine vision system was made to interface with one another by a strategy called fusion. Data fusion was looked in at two different levels namely sensor-level fusion, feature- level fusion. As the goal was to reduce the accuracy of the human error on the rail sleeper classification as good or bad the results obtained by the feature-level fusion compared to that of the results of actual classification were satisfactory.
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Lanka, Venkata Raghava Ravi Teja Lanka. "VEHICLE RESPONSE PREDICTION USING PHYSICAL AND MACHINE LEARNING MODELS." The Ohio State University, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=osu1511891682062084.
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Gao, Ce. "Use of Data Analytics and Machine Learning to Improve Culverts Asset Management Systems." University of Cincinnati / OhioLINK, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=ucin15541199012388.
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Zhou, Gordon. "Machine Learning-Based Cost Predictive Model for Better Operating Expenditure Estimations of U.S. Light Rail Transit Projects." Thesis, The George Washington University, 2021. http://pqdtopen.proquest.com/#viewpdf?dispub=28157527.
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Inaccurate forecasts of operating expenditures during the planning phase for new Light Rail Transit (LRT) projects in the United States underestimated future costs by up to 45% (Pickrell, 1989). When operating expenditures exceeded projected levels, local transit agencies often reduced public transit services to operate within their respective annual budgets. Therefore, it is imperative for transit agencies to produce reasonably accurate planning estimates to secure sufficient funding to support future operations, maintenance, and service delivery associated with LRT systems. The research aimed to develop a more accurate LRT operating expenditure predictive model to be used during the planning stage. Traditional statistical analysis and various machine learning-based algorithms were utilized with input from 22 LRT systems in the United States spanning between 2008 to 2018 from various U.S. governmental public databases. This praxis extended the current state of practice that relied primarily on sum of unit-cost estimates (also known as the unit-cost method) which generally failed to produce accurate forecasts due to lack of engineering details at the planning stage. Existing research attempted to develop regression-based methodologies using system-based attributes but did not substantially increase prediction accuracy from using the unit-cost method. The research improved current practices and research by having developed a more accurate and replicable machine learning-based predictive model using available geographic, socio-economic and LRT system-related variables.
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Gentek, Anna. "Activity Recognition Using Supervised Machine Learning and GPS Sensors." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-295600.
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Human Activity Recognition has become a popular research topic among data scientists. Over the years, multiple studies regarding humans and their daily motion habits have been investigated for many different purposes. This fact is not surprising when we look at all the opportunities and applications that can be applied and utilized thanks to the results of these algorithms. In this project we implement a system that can effectively collect sensor data from mobile devices, process it and by using supervised machine learning successfully predict the class of a performed activity. The project was executed based on datasets and features extracted from GPS sensors. The system was trained using various machine learning algorithms and Python SciKit to guarantee optimal solutions with accurate predictions. Finally, we applied a majority vote rule to secure the best possible accuracy of the activity classification process. As a result we were able to identify various activities including walking, cycling, driving and public transportation methods bus and metro with 90+% accuracy.<br>Att utföra aktivitetsigenkänning på människor har blivit ett populärt forskningsämne bland datavetare, där flertalet studier rörande människor och deras dagliga rörelsevanor undersökts för många olika syften. Detta är inte förvånande när man ser till de möjligheter och användningsområden som kan tillämpas och utnyttjas tack vare resultaten från dessa system. Detta projekt går ut på att implementera ett system som mha samlad sensordata från mobila enheter, kan bearbeta den och genom s.k övervakad maskininlärning med goda resultat bestämma den aktivitet som utförts. Projektet genomfördes baserat på dataset och egenskaper extraherade från GPS-data. Systemet tränades med olika maskininlärningsalgoritmer genom Python SciKit för att välja den bäst lämpade metoden för detta projekt. Slutligen tillämpade vi majority votemetoden för att säkerställa bästa möjliga noggrannhet i aktivitetsklassificeringsprocessen. Resultatet blev ett system som framgångsrikt kan identifiera aktiviteterna gå, cykla, köra bil samt med ett ytterligare fokus på kollektivtrafikmetoderna buss och tunnelbana, med en noggrannhet på över 90%.<br>Kandidatexjobb i elektroteknik 2020, KTH, Stockholm
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Mallick, Soumya Suddha. "Modelling of fluidised dense-phase pneumatic conveying of powders." Centre for bulk solids and particulate technologies - Faculty of Engineering, 2009. http://ro.uow.edu.au/theses/3077.
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Dense-phase pneumatic conveying of powders is becoming increasingly popular in various industries such as power, pharmaceutical, cement, alumina, chemical, limestone, refinery, and so on. Some of the reasons include: minimum gas flows and power consumption; improved product quality; increased workplace safety. However, due to the highly concentrated and turbulent mode of the solids-gas flow, only limited progress has been be achieved so far in understanding the fundamental transport mechanisms and accurately predicting pipeline pressure drop, which is a key system design parameter. This thesis aims to overcome the present limitations and provide the industry with a new validated modelling procedure for the accurate prediction and scale-up of pressure drop and optimal operating conditions for fluidised dense-phase pneumatic conveying systems.Various popular/existing models (and model formats) for solids friction (for straight horizontal pipes) have been evaluated for scale-up accuracy and stability. It has been found that the models (and their use of parameter groupings) are generally not capable of accurately predicting pressure drop under scale-up conditions of pipeline diameter and/or length. Two new approaches and another method based on the parameters used by other researcher have been employed in this study as improved design techniques. One approach, derived by modifying an existing reliable dilute-phase model to make it suitable for dense-phase, has resulted in a substantial relative improvement in the overall accuracy of predictions under scale-up conditions for two types of fly ash, ESP dust, pulverised coal and fly ash/cement mixture. Another method has been derived using the concept of “two-layer” slurry flow modelling (i.e. suspension flow occurring on top of a non-suspension moving layer), and this has also resulted in similar improvements. The third method, using parameters that were mentioned by another researcher as providing better representation of the flow phenomenon, has also resulted in similar reliable predictions. Three different popular/existing bend models have been evaluated to select an optimal (bend loss) model for dense-phase powder conveying. It has been found that the estimation of bend pressure drop can have a considerable impact towards correctly predicting the total pressure loss in a pneumatic conveying system.An existing method of representing “minimum transport criteria” (based on superficial air velocity and solids loading ratio) has been found inadequate for predicting the unstable boundary, especially under diameter scale-up conditions. Based on the experimental data of various powders conveyed over a wide range of pipe lengths and diameters, it is found that with increase in pipe diameter, the requirement of minimum conveying air velocity increases. To capture the pipe diameter effect, a Froude number based approach has been introduced to reliably represent the minimum transport boundary.The thesis also investigates the suitability of using a direct differential pressure (DP) measurement technique across a straight length of pipe for fine powder conveying in dense-phase. Standard Deviations (SD) of the DP, as well as the static pressure signals are presented. The trend shows the SD values are increasing with increase in pipe length from pipe inlet to exit (i.e. a dependence on tapping location).
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Totah, Thomas S. "Dense-phase pneumatic transport of cohesionless solids." Thesis, Virginia Polytechnic Institute and State University, 1987. http://hdl.handle.net/10919/80160.
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An experimental program has been undertaken to gain a more fundamental understanding of dense-phase pneumatic transport of cohesionless solids. A 50.8 mm internal diameter circulating unit with both horizontal and vertical sections has been constructed . The pipe material is transparent lexan which allows for visual observation of the flow pattern. The particles used were a mixture of 95% white and 5% black polyethylene granules (particle diameter approximately 3 mm). The black particles were used to aid the visual observation of the flow pattern. The flow patterns ranged from dilute-phase flow to dense-phase plug flow. High-speed photographic techniques have been used to document the flow patterns in both the horizontal and vertical sections. Pressure drop measurements across a 70 cm test section have been coordinated with the film work.
At the higher superficial air velocities (approximately 15 m/sec), the particles flow in a dilute suspension within the air stream. The pressure drop across the 70 cm section fluctuates very rapidly. For the horizontal dilute-phase flow, the mean pressure drop is approximately 0.12 kPa with fluctuations ranging from 0 to 0.3 kPa. For the vertical dilute-phase flow, the mean pressure drop is approximately 0.25 kPa with fluctuations ranging from 0 to 0.5 kPa. Upon reducing the superficial air velocity to 6.8 m/sec, the flow pattern in the horizontal section changes to a type of strand flow. The particles are conveyed in a dilute phase above a stationary layer. Large peaks in the pressure drop data (approximately 1 to 2 kPa) correspond to increases in the dilute-phase solids concentration.
At the lower superficial air velocities (below 5 m/sec) , the solids flow pattern changes to dense-phase flow. The particles move in the form of plugs that occupy the entire pipe cross-section. For the horizontal flow, the plug length ranged from 0.17 to 0.60 m and the pressure drop across the plugs ranged from 1 to 5.2 kPa. The pressure gradient range can be predicted from the equations of Konrad et al. (1980). The analysis of the vertical dense-phase flow films is not as straightforward as the horizontal films. However, the flow pattern resembles that described by Konrad (1987) and there is qualitative agreement with the concepts outlined by Konrad (1987).<br>Master of Science
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Jahangiri, Arash. "Investigating Violation Behavior at Intersections using Intelligent Transportation Systems: A Feasibility Analysis on Vehicle/Bicycle-to-Infrastructure Communications as a Potential Countermeasure." Diss., Virginia Tech, 2015. http://hdl.handle.net/10919/76729.
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The focus of this dissertation is on safety improvement at intersections and presenting how Vehicle/Bicycle-to-Infrastructure Communications can be a potential countermeasure for crashes resulting from drivers' and cyclists' violations at intersections. The characteristics (e.g., acceleration capabilities, etc.) of transportation modes affect the violation behavior. Therefore, the first building block is to identify the users' transportation mode. Consequently, having the mode information, the second building block is to predict whether or not the user is going to violate. This step focuses on two different modes (i.e., driver violation prediction and cyclist violation prediction). Warnings can then be issued for users in potential danger to react or for the infrastructure and vehicles so they can take appropriate actions to avoid or mitigate crashes.
A smartphone application was developed to collect sensor data used to conduct the transportation mode recognition task. Driver violation prediction task at signalized intersections was conducted using observational and simulator data. Also, a naturalistic cycling experiment was designed for cyclist violation prediction task. Subsequently, cyclist violation behavior was investigated at both signalized and stop-controlled intersections. To build the prediction models in all the aforementioned tasks, various Artificial Intelligence techniques were adopted. K-fold Cross-Validation as well as Out-of-Bag error was used for model selection and validation.
Transportation mode recognition models contributed to high classification accuracies (e.g., up to 98%). Thus, data obtained from the smartphone sensors were found to provide important information to distinguish between transportation modes. Driver violation (i.e., red light running) prediction models were resulted in high accuracies (i.e., up to 99.9%). Time to intersection (TTI), distance to intersection (DTI), the required deceleration parameter (RDP), and velocity at the onset of a yellow light were among the most important factors in violation prediction. Based on logistic regression analysis, movement type and presence of other users were found as significant factors affecting the probability of red light violations by cyclists at signalized intersections. Also, presence of other road users and age were the significant factors affecting violations at stop-controlled intersections. In case of stop-controlled intersections, violation prediction models resulted in error rates of 0 to 10 percent depending on how far from the intersection the prediction task is conducted.<br>Ph. D.
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Fallahtafti, Alireza. "Developing Risk-Minimizing Vehicle Routing Problem for Transportation of Valuables: Models and Algorithms." Ohio University / OhioLINK, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1627568962315484.
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Fuentes, Antonio. "Proactive Decision Support Tools for National Park and Non-Traditional Agencies in Solving Traffic-Related Problems." Diss., Virginia Tech, 2019. http://hdl.handle.net/10919/88727.
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Transportation Engineers have recently begun to incorporate statistical and machine learning approaches to solving difficult problems, mainly due to the vast quantities of data collected that is stochastic (sensors, video, and human collected). In transportation engineering, a transportation system is often denoted by jurisdiction boundaries and evaluated as such. However, it is ultimately defined by the consideration of the analyst in trying to answer the question of interest.
In this dissertation, a transportation system located in Jackson, Wyoming under the jurisdiction of the Grand Teton National Park and recognized as the Moose-Wilson Corridor is evaluated to identify transportation-related factors that influence its operational performance. The evaluation considers its unique prevalent conditions and takes into account future management strategies. The dissertation accomplishes this by detailing four distinct aspects in individual chapters; each chapter is a standalone manuscript with detailed introduction, purpose, literature review, findings, and conclusion. Chapter 1 provides a general introduction and provides a summary of Chapters 2 – 6. Chapter 2 focuses on evaluating the operational performance of the Moose-Wilson Corridor's entrance station, where queueing performance and arrival and probability mass functions of the vehicle arrival rates are determined. Chapter 3 focuses on the evaluation of a parking system within the Moose-Wilson Corridor in a popular attraction known as the Laurance S. Rockefeller Preserve, in which the system's operational performance is evaluated, and a probability mass function under different arrival and service rates are provided. Chapter 4 provides a data science approach to predicting the probability of vehicles stopping along the Moose-Wilson Corridor. The approach is a machine learning classification methodology known as "decision tree." In this study, probabilities of stopping at attractions are predicted based on GPS tracking data that include entrance location, time of day and stopping at attractions. Chapter 5 considers many of the previous findings, discusses and presents a developed tool which utilizes a Bayesian methodology to determine the posterior distributions of observed arrival rates and service rates which serve as bounds and inputs to an Agent-Based Model. The Agent-Based Model represents the Moose-Wilson Corridor under prevailing conditions and considers some of the primary operational changes in Grand Teton National Park's comprehensive management plan for the Moose-Wilson Corridor. The implementation of an Agent-Based Model provides a flexible platform to model multiple aspects unique to a National Park, including visitor behavior and its interaction with wildlife. Lastly, Chapter 6 summarizes and concludes the dissertation.<br>Doctor of Philosophy<br>In this dissertation, a transportation system located in Jackson, Wyoming under the jurisdiction of the Grand Teton National Park and recognized as the Moose-Wilson Corridor is evaluated to identify transportation-related factors that influence its operational performance. The evaluation considers its unique prevalent conditions and takes into account future management strategies. Furthermore, emerging analytical strategies are implemented to identify and address transportation system operational concerns. Thus, in this dissertation, decision support tools for the evaluation of a unique system in a National Park are presented in four distinct manuscripts. The manuscripts cover traditional approaches that breakdown and evaluate traffic operations and identify mitigation strategies. Additionally, emerging strategies for the evaluation of data with machine learning approaches are implemented on GPS-tracks to determine vehicles stopping at park attractions. Lastly, an agent-based model is developed in a flexible platform to utilize previous findings and evaluate the Moose-Wilson corridor while considering future policy constraints and the unique natural interactions between visitors and prevalent ecological and wildlife.
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Zhang, Zai Yong. "Simultaneous fault diagnosis of automotive engine ignition systems using pairwise coupled relevance vector machine, extracted pattern features and decision threshold optimization." Thesis, University of Macau, 2011. http://umaclib3.umac.mo/record=b2493967.
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Osgood, Thomas J. "Semantic labelling of road scenes using supervised and unsupervised machine learning with lidar-stereo sensor fusion." Thesis, University of Warwick, 2013. http://wrap.warwick.ac.uk/60439/.
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At the highest level the aim of this thesis is to review and develop reliable and efficient algorithms for classifying road scenery primarily using vision based technology mounted on vehicles. The purpose of this technology is to enhance vehicle safety systems in order to prevent accidents which cause injuries to drivers and pedestrians. This thesis uses LIDAR–stereo sensor fusion to analyse the scene in the path of the vehicle and apply semantic labels to the different content types within the images. It details every step of the process from raw sensor data to automatically labelled images. At each stage of the process currently used methods are investigated and evaluated. In cases where existingmethods do not produce satisfactory results improvedmethods have been suggested. In particular, this thesis presents a novel, automated,method for aligning LIDAR data to the stereo camera frame without the need for specialised alignment grids. For image segmentation a hybrid approach is presented, combining the strengths of both edge detection and mean-shift segmentation. For texture analysis the presented method uses GLCM metrics which allows texture information to be captured and summarised using only four feature descriptors compared to the 100’s produced by SURF descriptors. In addition to texture descriptors, the ìD information provided by the stereo system is also exploited. The segmented point cloud is used to determine orientation and curvature using polynomial surface fitting, a technique not yet applied to this application. Regarding classification methods a comprehensive study was carried out comparing the performance of the SVM and neural network algorithms for this particular application. The outcome shows that for this particular set of learning features the SVM classifiers offer slightly better performance in the context of image and depth based classification which was not made clear in existing literature. Finally a novel method of making unsupervised classifications is presented. Segments are automatically grouped into sub-classes which can then be mapped to more expressive super-classes as needed. Although the method in its current state does not yet match the performance of supervised methods it does produce usable classification results without the need for any training data. In addition, the method can be used to automatically sub-class classes with significant inter-class variation into more specialised groups prior to being used as training targets in a supervised method.
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Kumar, Saurabh. "Real-Time Road Traffic Events Detection and Geo-Parsing." Thesis, Purdue University, 2018. http://pqdtopen.proquest.com/#viewpdf?dispub=10842958.
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<p> In the 21<sup>st</sup> century, there is an increasing number of vehicles on the road as well as a limited road infrastructure. These aspects culminate in daily challenges for the average commuter due to congestion and slow moving traffic. In the United States alone, it costs an average US driver $1200 every year in the form of fuel and time. Some positive steps, including (a) introduction of the push notification system and (b) deploying more law enforcement troops, have been taken for better traffic management. However, these methods have limitations and require extensive planning. Another method to deal with traffic problems is to track the congested area in a city using social media. Next, law enforcement resources can be re-routed to these areas on a real-time basis. </p><p> Given the ever-increasing number of smartphone devices, social media can be used as a source of information to track the traffic-related incidents. </p><p> Social media sites allow users to share their opinions and information. Platforms like Twitter, Facebook, and Instagram are very popular among users. These platforms enable users to share whatever they want in the form of text and images. Facebook users generate millions of posts in a minute. On these platforms, abundant data, including news, trends, events, opinions, product reviews, etc. are generated on a daily basis. </p><p> Worldwide, organizations are using social media for marketing purposes. This data can also be used to analyze the traffic-related events like congestion, construction work, slow-moving traffic etc. Thus the motivation behind this research is to use social media posts to extract information relevant to traffic, with effective and proactive traffic administration as the primary focus. I propose an intuitive two-step process to utilize Twitter users' posts to obtain for retrieving traffic-related information on a real-time basis. It uses a text classifier to filter out the data that contains only traffic information. This is followed by a Part-Of-Speech (POS) tagger to find the geolocation information. A prototype of the proposed system is implemented using distributed microservices architecture.</p><p>
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Zátopek, Michal. "Metody strojového vidění pro rozpoznání dopravního značení." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2015. http://www.nusl.cz/ntk/nusl-221305.
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This diploma thesis is dealing with an issue of machine vision in the field of traffic signs recognition. The first part is devoted to machine vision in general traffic situations. Together with traffic applications there is paid attention to a possible autonomous vehicle and applications for the traffic signs recognition. The main part of this work is devoted to a description and an implementation of several methods for colour and pattern localisation of traffic signs in the scene and to identification algorithms. Apart from the implementation itself, these algorithms are submitted to several experiments for a valorisation of their success. The thesis also includes a gallery of images with traffic signs including a file with descriptive annotation for an automatic testing of algorithms.
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Glaab, Markus. "A distributed service delivery platform for automotive environments : enhancing communication capabilities of an M2M service platform for automotive application." Thesis, University of Plymouth, 2018. http://hdl.handle.net/10026.1/11249.
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The automotive domain is changing. On the way to more convenient, safe, and efficient vehicles, the role of electronic controllers and particularly software has increased significantly for many years, and vehicles have become software-intensive systems. Furthermore, vehicles are connected to the Internet to enable Advanced Driver Assistance Systems and enhanced In-Vehicle Infotainment functionalities. This widens the automotive software and system landscape beyond the physical vehicle boundaries to presently include as well external backend servers in the cloud. Moreover, the connectivity facilitates new kinds of distributed functionalities, making the vehicle a part of an Intelligent Transportation System (ITS) and thus an important example for a future Internet of Things (IoT). Manufacturers, however, are confronted with the challenging task of integrating these ever-increasing range of functionalities with heterogeneous or even contradictory requirements into a homogenous overall system. This requires new software platforms and architectural approaches. In this regard, the connectivity to fixed side backend systems not only introduces additional challenges, but also enables new approaches for addressing them. The vehicle-to-backend approaches currently emerging are dominated by proprietary solutions, which is in clear contradiction to the requirements of ITS scenarios which call for interoperability within the broad scope of vehicles and manufacturers. Therefore, this research aims at the development and propagation of a new concept of a universal distributed Automotive Service Delivery Platform (ASDP), as enabler for future automotive functionalities, not limited to ITS applications. Since Machine-to-Machine communication (M2M) is considered as a primary building block for the IoT, emergent standards such as the oneM2M service platform are selected as the initial architectural hypothesis for the realisation of an ASDP. Accordingly, this project describes a oneM2M-based ASDP as a reference configuration of the oneM2M service platform for automotive environments. In the research, the general applicability of the oneM2M service platform for the proposed ASDP is shown. However, the research also identifies shortcomings of the current oneM2M platform with respect to the capabilities needed for efficient communication and data exchange policies. It is pointed out that, for example, distributed traffic efficiency or vehicle maintenance functionalities are not efficiently treated by the standard. This may also have negative privacy impacts. Following this analysis, this research proposes novel enhancements to the oneM2M service platform, such as application-data-dependent criteria for data exchange and policy aggregation. The feasibility and advancements of the newly proposed approach are evaluated by means of proof-of-concept implementation and experiments with selected automotive scenarios. The results show the benefits of the proposed enhancements for a oneM2M-based ASDP, without neglecting to indicate their advantages for other domains of the oneM2M landscape where they could be applied as well.
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Kelly, Brennan James. "Experimental and Simulated Analysis of Voltage Stress Within a Bar-Wound Synchronous Machine Excited by a Silicon Carbide Inverter." The Ohio State University, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=osu1619111736344023.
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Turner, Aaron P. "DEVELOPMENT OF A DECISION SUPPORT SYSTEM FOR CAPACITY PLANNING FROM GRAIN HARVEST TO STORAGE." UKnowledge, 2018. https://uknowledge.uky.edu/bae_etds/58.
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This dissertation investigated issues surrounding grain harvest and transportation logistics. A discrete event simulation model of grain transportation from the field to an on-farm storage facility was developed to evaluate how truck and driver resource constraints impact material flow efficiency, resource utilization, and system throughput. Harvest rate and in-field transportation were represented as a stochastic entity generation process, and service times associated with various material handling steps were represented by a combination of deterministic times and statistical distributions. The model was applied to data collected for three distinct harvest scenarios (18 total days). The observed number of deliveries was within ± 2 standard deviations of the simulation mean for 15 of the 18 input conditions examined, and on a daily basis, the median error between the simulated and observed deliveries was -4.1%.
The model was expanded to simulate the whole harvest season and include temporary wet storage capacity and grain drying. Moisture content changes due to field dry down was modeled using weather data and grain equilibrium moisture content relationships and resulted in an RMSE of 0.73 pts. Dryer capacity and performance were accounted for by adjusting the specified dryer performance to the observed level of moisture removal and drying temperature. Dryer capacity was generally underpredicted, and large variations were found in the observed data. The expanded model matched the observed cumulative mass of grain delivered well and estimated the harvest would take one partial day longer than was observed.
Usefulness of the model to evaluate both costs and system performance was demonstrated by conducting a sensitivity analysis and examining system changes for a hypothetical operation. A dry year and a slow drying crop had the largest impact on the system’s operating and drying costs (12.7% decrease and 10.8% increase, respectively). The impact of reducing the drying temperature to maintain quality in drying white corn had no impact on the combined drying and operating cost, but harvest took six days longer. The reduced drying capacity at lower temperatures resulted in more field drying which counteracted the reduced drying efficiency and increased field time. The sensitivity analysis demonstrated varied benefits of increased drying and transportation capacity based on how often these systems created a bottleneck in the operation. For some combinations of longer transportation times and higher harvest rates, increasing hauling and drying capacity could shorten the harvest window by a week or more at an increase in costs of less than $12 ha-1.
An additional field study was conducted to examine corn harvest losses in Kentucky. Total losses for cooperator combines were found to be between 0.8%-2.4% of total yield (86 to 222 kg ha-1). On average, the combine head accounted for 66% of the measured losses, and the total losses were highly variable, with coefficients of variation ranging from 21.7% to 77.2%. Yield and harvest losses were monitored in a single field as the grain dried from 33.9% to 14.6%. There was no significant difference in the potential yield at any moisture level, and the observed yield and losses displayed little variation for moisture levels from 33.9% to 19.8%, with total losses less than 1% (82 to 130 kg dry matter ha-1). Large amounts of lodging occurred while the grain dried from 19.8% to 14.6%, which resulted in an 18.9% reduction in yield, and harvest losses in excess of 9%. Allowing the grain to field dry generally improved test weight and reduced mechanical damage, however, there was a trend of increased mold and other damage in prolonged field drying.
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Cappella, Matteo. "Studio e valutazione di tecniche di training per il riconoscimento automatico di attività attraverso dispositivi mobili." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2017.
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L'utilizzo degli smartphone è cresciuto rapidamente nel corso dell'ultimo decennio. Questi dispositivi oltre ad avere ottime capacità comunicative, di memoria e di calcolo, sono equipaggiati con numerosi sensori. Quest'ultimi permettono ai ricercatori di raccogliere numerose informazioni riguardanti le persone e il contesto che le circonda. Un aspetto molto importante che è possibile analizzare tramite la raccolta delle informazioni provenienti dai sensori è sicuramente quello del riconoscimento delle modalità di trasporto (Transportation Mode Detection), che consiste, appunto, nell'individuare la classe di mobilità intrapresa da un utente in un determinato momento tramite degli algoritmi di machine learning.
In questo elaborato, vengono utilizzate varie tecniche di apprendimento su un dataset contenente cinque differenti classi di trasporto quali stare fermi, camminare, andare in auto, autobus e treno. L'obiettivo che si è cercato di raggiungere è stato quello di verificare la possibilità di riconoscere le modalità di trasporto di un utente di cui non si possiedono informazioni, ovvero un soggetto che non è presente all'interno dell'insieme di dati usati per allenare il modello di predizione.
In modo particolare, lo studio si è focalizzato sulla tecnica di apprendimento incrementale attraverso la quale è stato possibile aggiornare il modello con l'aggiunta di nuove informazioni senza perdere la conoscenza acquisita in addestramenti precedenti.
Infine, si è indagato sul problema relativo all'etichettatura dei dati. Dato che questa operazione risulta essere molto costosa, nell'elaborato è stata proposta una soluzione basata su tecniche di apprendimento semi-supervisionato che consentono di sfruttare una combinazione di dati etichettati e non.
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Carel, Léna. "Analyse de données volumineuses dans le domaine du transport." Thesis, Université Paris-Saclay (ComUE), 2019. http://www.theses.fr/2019SACLG001/document.
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L'objectif de cette thèse est de proposer de nouvelles méthodologies à appliquer aux données du transport public. En effet, nous sommes entourés de plus en plus de capteurs et d'ordinateurs générant d'énormes quantités de données. Dans le domaine des transports publics, les cartes sans contact génèrent des données à chaque fois que nous les utilisons, que ce soit pour les chargements ou nos trajets. Dans cette thèse, nous utilisons ces données dans deux buts distincts. Premièrement, nous voulions être capable de détecter des groupes de passagers ayant des habitudes temporelles similaires. Pour ce faire, nous avons commencé par utilisé la factorisation de matrices non-négatives comme un outil de pré-traitement pour la classification. Puis nous avons introduit l'algorithme NMF-EM permettant une réduction de la dimension et une classification de manière simultanée pour un modèle de mélange de distributions multinomiales. Dans un second temps, nous avons appliqué des méthodes de régression à ces données afin d'être capable de fournir une fourchette de ces validations probables. De même, nous avons appliqué cette méthodologie à la détection d'anomalies sur le réseau<br>The aim of this thesis is to apply new methodologies to public transportation data. Indeed, we are more and more surrounded by sensors and computers generating huge amount of data. In the field of public transportation, smart cards generate data about our purchases and our travels every time we use them. In this thesis, we used this data for two purposes. First of all, we wanted to be able to detect passenger's groups with similar temporal habits. To that end, we began to use the Non-negative Matrix Factorization as a pre-processing tool for clustering. Then, we introduced the NMF-EM algorithm allowing simultaneous dimension reduction and clustering on a multinomial mixture model. The second purpose of this thesis is to apply regression methods on these data to be able to forecast the number of check-ins on a network and give a range of likely check-ins. We also used this methodology to be able to detect anomalies on the network