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Investigations are increasingly conducted online by not only novice sleuths but also by professionals -- in both competitive and collaborative environments. These investigations rely on publicly available information, called open source intelligence (OSINT). However, due to their online nature, OSINT investigations often present coordination, technological, and ethical challenges. Through semi-structured interviews with 14 professional OSINT investigators from nine different organizations, we examine the social collaboration and competition patterns that underlie their investigations. Instead of purely competitive or purely collaborative social models, we find that OSINT organizations employ a combination of both, and that each has its own advantages and disadvantages. We also describe investigators' use of and challenges with existing OSINT tools. Finally, we conclude with a discussion on supporting investigators' with more appropriable tools and making investigations more social. / Master of Science / Investigations are increasingly conducted online by not only novice investigators but also by professionals, such as private investigators or law enforcement agents. These investigations are conducted in competitive environments, such as Capture The Flag (CTF) events where contestants solve crimes and mysteries, but also in collaborative environments, such as teams of investigative journalists joining skills and knowledge to uncover and report on crimes and/or mysteries. These investigations rely on publicly available information called open source intelligence (OSINT) which includes public social media posts, public databases of information, public satellite imagery...etc. OSINT investigators collect and authenticate open source intelligence in order to conduct their investigations and synthesize the authenticated information they gathered to present their findings. However, due to their online nature, OSINT investigations often present coordination, technological, and ethical challenges. Through semi-structured interviews with 14 professional OSINT investigators from nine different organizations, we examine how these professionals conduct their investigations, and how they coordinate the different individuals and investigators involved throughout the process. By analyzing these processes, we can discern the social collaboration and competition patterns that enable these professionals to conduct their investigations. Instead of purely competitive or purely collaborative social models, we find that OSINT organizations employ a combination of both, and that each has its own advantages and disadvantages. In other words, professional OSINT investigators compete with each other but also collaborate with each other at different stages of their investigations or for different investigative tasks. We also describe investigators' use of and challenges with existing OSINT tools and technologies. Finally, we conclude with a discussion on supporting investigators with tools that can adapt to their different needs and investigation types and making investigations more social.

This study addresses the susceptibility of atmospheric rivers (ARs) to the behavior of the North Atlantic Subtropical High (NASH). ARs are a major mechanism for meridional moisture transport often connected to heavy precipitation and mid-latitude troughs. The NASH, a semi-permanent anticyclone over the subtropical North Atlantic Ocean, has been shown to be significantly influential on precipitation variability over the southeastern United States. A self-organizing map (SOM) was trained on a 4 x 3 regular grid over 250 iterations using ERA5 derived 6-hourly 850 hPa Geopotential Heights ≥ 1535 gpm from 1979-2020. The 12 resulting "nodes" were analyzed with respect to ARs defined by objects of ERA5 derived integrated water vapor transport (IVT) > 500 m-1 s-1 with lengths > 2000 km. Composites of thresholded 850 hPa heights, AR-concurrent PRISM precipitation, AR spatial frequency distribution maps, and seasonal AR frequency histograms per node illustrate seasonal interactions between the NASH and ARs that demonstrate a tendency of more frequent ARs and higher mean AR-driven precipitation over the Mississippi embayment and Ohio River Valley in the summer months, believed to be representative of extreme moisture transport events, when the NASH exhibits increased intensity, spatial expansion, and southwestward migration. Conversely, AR frequency and AR-concurrent precipitation composites suggest wintertime events are mainly supported by dynamically-driven nor'easter and bomb type cyclones when the NASH is constricted, at higher latitudes, and further east. Findings suggest that extreme summertime water vapor transport events associated with an AR are enhanced by the warm season NASH due to its increased intensity and proximity to the eastern US that acts as a supplementary lifting mechanism amidst low dynamic influence. / Master of Science / This study aims to investigate the response of atmospheric rivers (ARs) to the behavior of the North Atlantic Subtropical High (NASH). ARs are a major vehicle for the poleward transport of moisture from the tropics and subtropics. ARs are often affiliated with heavy precipitation and mid-latitude cyclones and frontal boundaries. The NASH, a semi-permanent anticyclone over the subtropical North Atlantic Ocean, has been shown to be significantly influential on precipitation variability over the southeastern United States. A self-organizing map (SOM), a method of vector quantification, was trained on a 4 x 3 regular grid over 250 iterations using ERA5 derived 6-hourly 850 hPa Geopotential Heights ≥ 1535 meters from 1979-2020. The 12 resulting "nodes" were analyzed with respect to ARs defined by objects that result from masking the rate of transport of water vapor within a vertical column from 1000 hPa to 300 hPa of which that are greater than 2000 km long. Composites of thresholded 850 hPa heights, AR-concurrent precipitation, AR spatial frequency distribution maps, and seasonal AR frequency histograms per node illustrate seasonal interactions between the NASH and ARs that demonstrate a tendency of more frequent ARs and higher mean AR-driven precipitation over the Mississippi embayment and Ohio River Valley in the summer months, believed to be representative of severe precipitation events, when the NASH is stronger, larger, and further southwestward. Conversely, AR frequency and AR-concurrent precipitation composites suggest wintertime events are mainly supported by nor'easter and bomb type cyclones that occur when the Polar jet stream is strongest and when the NASH is constricted, at higher latitudes, and further east. Findings suggest that extreme summertime water vapor transport events associated with an AR are enhanced by the warm season NASH due to its increased intensity and proximity to the eastern US that acts as a supplementary lifting mechanism amidst low dynamic influence.

Work deal about self-organizing maps, especially about Kohonen self-organizing map. About creating of aplication, which realize creating and learning of self-organizing map. And about usage of self-organizing map for self-localization of robot.

Projetar máquinas capazes identificar pessoas é um problema cuja solução encontra uma grande quantidade de aplicações. Implementações em software de sistemas baseados em medições de características físicas pessoais (biométricos), estão começando a ser produzidos em escala comercial. Nesta categoria estão os sistemas de Reconhecimento Automático de Locutor, que se usam da voz como característica identificadora. No presente momento, os métodos mais populares são baseados na extração de coeficientes mel-cepstrais (MFCCs) das locuções, seguidos da identificação do locutor através de Hidden Markov Models (HMMs), Gaussian Mixture Models (GMMs) ou quantização vetorial. Esta preferência se justifica pela qualidade dos resultados obtidos. Fazer com que estes sistemas sejam robustos, mantendo sua eficiência em ambientes ruidosos, é uma das grandes questões atuais. Igualmente relevantes são os problemas relativos à degradação de performance em aplicações envolvendo um grande número de locutores, e a possibilidade de fraude baseada em vozes gravadas. Outro ponto importante é embarcar estes sistemas como sub-sistemas de equipamentos já existentes, tornando-os capazes de funcionar de acordo com o seu operador. Este trabalho expõe os conceitos e algoritmos envolvidos na implementação de um software de Reconhecimento Automático de Locutor independente de texto. Inicialmente é tratado o processamento dos sinais de voz e a extração dos atributos essenciais deste sinal para o reconhecimento. Após isto, é descrita a forma pela qual a voz de cada locutor é modelada através de uma rede neural de arquitetura Self-Organizing Map (SOM) e o método de comparação entre as respostas dos modelos quando apresentada uma locução de um locutor desconhecido. Por fim, são apresentados o processo de construção do corpus de vozes usado para o treinamento e teste dos modelos, as arquiteturas de redes testadas e os resultados experimentais obtidos numa tarefa de identificação de locutor. / The design of machines that can identify people is a problem whose solution has a wide range of applications. Software systems, based on personal phisical attributes measurements (biometrics), are in the beginning of commercial scale production. Automatic Speaker Recognition systems fall into this cathegory, using voice as the identifying attribute. At present, the most popular methods are based on the extraction of mel-frequency cepstral coefficients (MFCCs), followed by speaker identification by Hidden Markov Models (HMMs), Gaussian Mixture Models (GMMs) or vector quantization. This preference is motivated by the quality of the results obtained by the use of these methods. Making these systems robust, able to keep themselves efficient in noisy environments, is now a major concern. Just as relevant are the problems related to performance degradation in applications with a large number of speakers involved, and the issues related to the possibility of fraud by the use of recorded voices. Another important subject is to embed these systems as sub-systems of existing devices, enabling them to work according to the operator. This work presents the relevant concepts and algorithms concerning the implementation of a text-independent Automatic Speaker Recognition software system. First, the voice signal processing and the extraction of its essential features for recognition are treated. After this, it is described the way each speaker\'s voice is represented by a Self-Organizing Map (SOM) neural network, and the comparison method of the models responses when a new utterance from an unknown speaker is presented. At last, it is described the construction of the speech corpus used for training and testing the models, the neural network architectures tested, and the experimental results obtained in a speaker identification task.

Made available in DSpace on 2017-07-21T14:19:23Z (GMT). No. of bitstreams: 1 Kelly Lais.pdf: 3909972 bytes, checksum: a9e068fb926de9ca233857005e400c84 (MD5) Previous issue date: 2014-12-16 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior / O Sensoriamento Remoto (SR) dispõe de tecnologias em constante crescimento e com grande potencial para a agricultura, tanto no gerenciamento de culturas, manejo de solo, bem como discriminações de feições da terra. Atualmente, há muitos métodos de análise e categorização de paisagens, que com a integração de dados de SR e técnicas de Sistemas de Informação Geográfica (SIG) apresentam alternativa promissora. Isto é, proporcionam maior facilidade na manipulação de dados geográficos, bem como otimização da validação a campo. Neste contexto, esta pesquisa foi realizada utilizando classificação digital não-supervisionada pela Rede Neural Artificial (RNA) Self-Organizing Maps (SOM) no reconhecimento de padrões de uso e cobertura da terra em um recorte de imagem orbital de alta resolução Rapideye pertencente à Bacia do Pitangui, o qual abrange o município de Ponta Grossa, localizado a centro-leste do Estado do Paraná. Primeiramente aplicouse a técnica NDVI (Índice de Vegetação por Diferença Normalizada) para estimular a separação das classes, principalmente os diferentes tipos de cultivos agrícolas, bem como cobertura florestal. A imagem orbital e NDVI foram segmentadas por meio de Análise de Imagem Baseada em Objeto (GEOBIA), gerando descritores com propriedades espaciais, espectrais e de textura, culminando no banco de dados relacional (BDR) com tais descritores. Mediante Análise de Componentes Principais (ACP) reduziu-se a dimensionalidade dos dados do BDR, selecionando os descritores mais significativos. A dimensionalidade foi reduzida, sem perda de informação, de 42 descritores para 21, a saber 6 espaciais, 12 espectrais e 3 de textura. Após esta preparação dos dados, utilizou-se a RNA SOM para o reconhecimento dos padrões pré-determinados a campo. As classes de uso e cobertura da terra discriminadas pela RNA SOM foram cultivos (cultivo 1, 2, 3 e 4), estradas e construções, cobertura florestal e corpos d’água. A RNA SOM culminou no agrupamento das classes cultivos inclusive em relação ao seu ciclo fenológico. A associação da banda artificial NDVI, com seus descritores às bandas espectrais, incrementou a separabilidade entre classes, tais como cobertura florestal e corpos d’água. As classes de uso e cobertura da terra foram validadas a campo, a exatidão global foi de 91% de acerto, com índice kappa de 0,9, considerado resultado excelente em valores de referência. Também foi realizado o teste estatístico F, o qual satisfez as hipóteses de nulidade nas áreas analisadas.Conclui-se que os métodos utilizados apresentaram eficácia, agilidade e baixo custo no mapeamento d o uso e cobertura da terra em escala detalhada. / Remote Sensing (RS) uses steadily growing technologies and presents great potential for agriculture, e.g. in crop and land management, as well as for discrimination of land features. Currently, there are many methods of analysis and landscape categorization that when integrated with RS data and Geographic Information Systems (GIS) techniques stage as promising alternatives. That is, they provide greater ease in handling spatial data as well as optimizing validation on the field. In this context, this study was carried out using unsupervised digital classification with Artificial Neural Network (ANN) Self- Organizing Maps (SOM), in order to recognize patterns of land cover and land use in part of a high-resolution Rapideye orbital image belonging to the Pitangui River Basin, which encompasses the city of Ponta Grossa, located in the Central-Eastern portion of the State of Paraná. Initially, NDVI (Normalized Difference Vegetation Index) technique was applied to stimulate the separation of classes, especially to evidence different types of agricultural crops and forest cover. The orbital image and the NDVI were segmented through Geographic Object-Based Image Analysis (GEOBIA), generating descriptors with spatial, spectral and textural properties, culminating in the relational database (RDB) with such descriptors. With Principal Component Analysis (PCA) dimensionality of the BDR data was reduced, selecting the most significant descriptors. Dimensionality was reduced without information loss, from 42 descriptors to 21, namely 6 spatial, 12 spectral and 3 textural. After this data preparation, ANN SOM was used to recognize predetermined patterns in the field. The classes of land cover and land use discriminated by ANN SOM were crops (crop 1, 2, 3 and 4), roads and buildings; forest cover and water bodies. The ANN SOM culminated in the grouping of crop classes including in relation to its phenological cycle. The association of the NDVI artificial band with descriptors to spectral bands, increased the separability between classes, such as forest cover and water bodies. Classes of land cover and land use were validated in the field, the global accuracy was 91%, with kappa index of 0.9 and considered to be excellent as reference values. F statistical test was also carried out and showed satisfiability in the analyzed areas. It is concluded that the methods used were effective, agile and low-cost in detailed scale mapping of land use and coverage.

Projetar máquinas capazes identificar pessoas é um problema cuja solução encontra uma grande quantidade de aplicações. Implementações em software de sistemas baseados em medições de características físicas pessoais (biométricos), estão começando a ser produzidos em escala comercial. Nesta categoria estão os sistemas de Reconhecimento Automático de Locutor, que se usam da voz como característica identificadora. No presente momento, os métodos mais populares são baseados na extração de coeficientes mel-cepstrais (MFCCs) das locuções, seguidos da identificação do locutor através de Hidden Markov Models (HMMs), Gaussian Mixture Models (GMMs) ou quantização vetorial. Esta preferência se justifica pela qualidade dos resultados obtidos. Fazer com que estes sistemas sejam robustos, mantendo sua eficiência em ambientes ruidosos, é uma das grandes questões atuais. Igualmente relevantes são os problemas relativos à degradação de performance em aplicações envolvendo um grande número de locutores, e a possibilidade de fraude baseada em vozes gravadas. Outro ponto importante é embarcar estes sistemas como sub-sistemas de equipamentos já existentes, tornando-os capazes de funcionar de acordo com o seu operador. Este trabalho expõe os conceitos e algoritmos envolvidos na implementação de um software de Reconhecimento Automático de Locutor independente de texto. Inicialmente é tratado o processamento dos sinais de voz e a extração dos atributos essenciais deste sinal para o reconhecimento. Após isto, é descrita a forma pela qual a voz de cada locutor é modelada através de uma rede neural de arquitetura Self-Organizing Map (SOM) e o método de comparação entre as respostas dos modelos quando apresentada uma locução de um locutor desconhecido. Por fim, são apresentados o processo de construção do corpus de vozes usado para o treinamento e teste dos modelos, as arquiteturas de redes testadas e os resultados experimentais obtidos numa tarefa de identificação de locutor. / The design of machines that can identify people is a problem whose solution has a wide range of applications. Software systems, based on personal phisical attributes measurements (biometrics), are in the beginning of commercial scale production. Automatic Speaker Recognition systems fall into this cathegory, using voice as the identifying attribute. At present, the most popular methods are based on the extraction of mel-frequency cepstral coefficients (MFCCs), followed by speaker identification by Hidden Markov Models (HMMs), Gaussian Mixture Models (GMMs) or vector quantization. This preference is motivated by the quality of the results obtained by the use of these methods. Making these systems robust, able to keep themselves efficient in noisy environments, is now a major concern. Just as relevant are the problems related to performance degradation in applications with a large number of speakers involved, and the issues related to the possibility of fraud by the use of recorded voices. Another important subject is to embed these systems as sub-systems of existing devices, enabling them to work according to the operator. This work presents the relevant concepts and algorithms concerning the implementation of a text-independent Automatic Speaker Recognition software system. First, the voice signal processing and the extraction of its essential features for recognition are treated. After this, it is described the way each speaker\'s voice is represented by a Self-Organizing Map (SOM) neural network, and the comparison method of the models responses when a new utterance from an unknown speaker is presented. At last, it is described the construction of the speech corpus used for training and testing the models, the neural network architectures tested, and the experimental results obtained in a speaker identification task.

This diploma thesis occupies itself with the subject of near synonymy, concretely with adjectives. On the basis of corpus linguistic methods two pairs of near synonyms have been researched - verschieden/unterschiedlich and bedeutend/bedeutsam. The 15 primary collocators (according to the syntactic position of each adjective) have been examined using the InterCorp parallel corpus methods in order to find out the most frequent Czech equivalence. Keywords: lexical-semantic relations, near synonymy, lexicography, corpora, cooccurrence analysis, Self Organizing Maps, CCDB

We used the self-organizing maps (SOMs) method on eight models that participated in the Coupled model intercomparison project phase 5 (CMIP5) and two different greenhouse gases (GHG) concentration experiments. The SOMs were created from the winter 500 hPa horizontal temperature flux for each model. The clustering by the SOM revealed that in addition to the three flux pathways found in reanalyses (Pacific, Atlantic and Siberian/continental pathway), superpositions of these occur for the free running climate models, which develop their dynamic more freely than the reanalyses. It was found that the general structure of fluxes is indirectly dependent on the GHG concentrations, as the derived results from SOM patterns are different between the two GHG concentrations. It is suggested that flux patterns change from stable cyclonic motion over the north pole to flux pathways that feature more meridional fluxes through the North Atlantic and North Pacific into the Arctic. / Die Methode der Self-Organizing Maps (SOMs) wurde auf acht CMIP5-Modelle mit jeweils zwei verschiedenen Treibhausgasszenarien angwendet. Die SOMs wurden für jedes Modell und jede der beiden Modelläufe für den horizontalen Temperaturfluss in 500 hPa im Winter erstellt. Zusätzlich zu den aus der Analyse von Reanalyse-Daten erwarteten drei Transportwegen (pazifisch, atlantisch und sibirisch/kontinental) wurden Überlagerungen dieser gefunden. Es konnte gezeigt werden, dass die grundsätzliche Struktur der Transporte indirekt abhängig von der Treibhausgaskonzentration ist. Die Ergebnisse deuten darauf hin, dass sich die generelle Struktur des atmosphärischen Transports von einer stabilen zyklonalen Bewegung über dem Nordpol sich zu Transporten verschiebt, welche meridionale Transporte über den Nortdatlantik und den Nordpazifik in die Arktis führen.

Thesis (PhD)--Stellenbosch University, 2015. / ENGLISH ABSTRACT: The debilitating social, economic and environmental ramifications of traffic congestion are experienced in large cities the world over. The optimisation of traffic signal timings at signalised road intersections attempts to mitigate the extent of these adverse effects of traffic congestion by reducing the delay time experienced by vehicles in a transport network. Today, traffic signal control schemes may be classiffied into one of two main classes, namely fixed-time traffic signal control strategies, which are typically cyclic in nature, and vehicle-actuated traffic signal control strategies, which are typically acyclic in nature. Generally, cyclic control strategies tend to lack exibility, and are unable to adapt to short-term uctuations in traffic ow rates, resulting in green times that are either too long or too short. On the other hand, acyclic control strategies tend to lack coordination between intersections, resulting in vehicles being required to stop at the majority of signalised intersections they encounter. Self-organising traffic signal control has been proposed as an attractive alternative form of control which both exhibits exibility and facilitates a global coordination between intersections as a result of localised signal switching policies. Two examples of existing self-organising traffic signal control algorithms from the literature include an algorithm proposed by Lammer and Helbing in 2008 and an algorithm proposed by Gershenson and Rosenblueth in 2012. These algorithms have been shown to outperform both optimised fixed-time traffc signal control techniques as well as state-of-the-art vehicle actuated trffic signal control techniques, in terms of reducing vehicle delay time in a transport network. A draw-back of both of these self-organising approaches, however, is that their effective operation relies on carefully selected parameter values; poorly selected parameter values may render these algorithms very ineffectual. In this dissertation, three novel self-organising traffic signal traffic control algorithms are proposed. These three algorithms assume the use of existing radar detection sensors mounted at the intersection to provide the necessary input data. The radar detection sensors are capable of detecting and tracking individual vehicles approaching an intersection, providing real-time information pertaining to their physical dimensions, velocities, and ranges from the intersection in terms of both time and distance. The three traffic signal control algorithms are free of any user-specialised parameters, and instead rely solely on the data provided by the radar detection sensors to inform their signal switching policies. The first of these traffic signal control algorithms is inspired by inventory control theory, and draws parallels between the monetary costs typically considered in inventory control models and the delay time costs associated with traffic control at signalised intersections, which the algorithm attempts to minimise. The second novel traffic control algorithm is inspired by the chemical process of osmosis in which solvent molecules move unaided from a region where they are highly concentrated, across a semi-permeable membrane, into a region of high solute molecule concentration. The algorithm models vehicles approaching an intersection as solvent molecules and the physical space available for the vehicles to occupy once they have passed through the intersection as solute molecules. Following this analogy, the intersection is considered to be the semi-permeable membrane. The third traffic control algorithm is a hybrid of the inventory and osmosis-inspired algorithms together with an intersection utilisation maximisation technique, which prevents unnecessary or prolonged underutilisation of an intersection. The three novel trafficc control algorithms, together with the algorithms of Lammer and Helbing, and of Gershenson and Rosenblueth, as well as a fixed-time control algorithm, are implemented in a purpose-built microscopic traffic simulation modelling framework. Several measures are employed to evaluate the relative performances of the algorithms. These measures include the usual mean and maximum resulting delay times incurred by vehicles and the saturation level of the roadways in the transport network, as well as three novel performance measure indicators which include the mean number of stops made by vehicles, their mean normalised delay time and the mean normalised number of stops made. The algorithms are compared in the context of a linear corridor road network topology as well as a grid road network topology under various traffic ow conditions. The overall performance of the novel hybrid traffic signal control algorithm is found to be superior for the corridor road network topology, while the performance of the osmosis-inspired algorithm is found to be superior for the grid road network topology. / AFRIKAANSE OPSOMMING:Die negatiewe sosiale, ekonomiese en omgewingsimpak van verkeersopeenhoping word in groot stede regoor die w^ereld ervaar. Die doel met die optimering van verkeersligwerkverrigting by straatkruisings is om die omvang van hierdie negatiewe impak tee te werk deur die vertraging van voertuie in 'n vervoernetwerk te verminder. Hedendaagse verkeersbeheeralgoritmes kom in een van twee hoofklasse voor, naamlik vaste-tyd beheerstrategiee, wat gewoonlik siklies van aard is, en beheerstrategiee gebaseer op voertuigopsporing, wat tipies asiklies van aard is. Oor die algemeen beskik sikliese beheerstrategiee nie oor genoegsame buigsaambeid om aan te pas by kort-termyn fluktuasies in verkeersvloei nie, wat tipies daartoe lei dat hul groentye spesifiseer wat of te lank of te kort is. Aan die ander kant is asikliese beheerstrategiee nie daartoe in staat om koordinasie tussen naasliggende straatkruisings te bewerkstellig nie, wat weer daartoe lei dat voertuie genoodsaak word om by die oorgrote meerderheid straatkruisings op hul pad te stop. Die self-organiserende beheer van verkeersligte is as 'n aantrektlike, buigsame alternatief voorgestel wat in staat is om globale koordinasie tussen naasliggende straatkruisings as gevolg van gelokaliseerde seinstrategiee te bewerkstellig. Twee voorbeelde van bestaande self-organiserende verkeersbeheeralgoritmes in die literatuur is die algoritmes wat in 2008 deur Lammer and Helbing en in 2012 deur Gershenson en Rosenblueth voorgestel is. Daar is aangetoon dat hierdie algoritmes daartoe in staat is om ge-optimeerde vaste-tyd beheerstrategiee sowel as gevorderde strategiee gebaseer op voertuigopsporing uit te stof in terme van 'n vermindering van die vertraging van voertuie in 'n vervoernetwerk. 'n Nadeel van beide hierdie self-organiserende benaderings is egter dat hul doeltreffende werkverrigting berus op versigtig-gekose parameterwaardes; willekeurige parameterwaardes mag lei na hoogs ondoeltreffende werkverrigitng van die algoritmes. Drie nuwe self-organiserende verkeersbeheeralgoritmes word in hierdie proefskrif voorgestel. Hierdie drie algoritmes maak vir hul toevoerdata staat op die beskikbaarhed van bestaande radar opsporingsensors wat by straatkruisings geinstalleer is. Die sensors is daartoe in staat om individuele voertuie wat 'n straatkruising nader, op te spoor, te volg en intydse data oor hul fisiese dimensies, snelhede, en afstande na die kruising (in terme van beide tyd en afstand) te lewer. Die drie algoritmes bevat geen gebruikers-gespesifiseerde parameters nie, en maak in plaas daarvan slegs gebruik van die sensortoevoerdata om hul beheerstrategiee te bepaal. Die eerste van hierdie verkeersbeheeralgoritmes is deur die teorie van voorraadbeheer geinspireer en maak gebruik van parallelle tussen die monet^ere kostes wat tipies in voorraadbeheermodelle voorkom en die kostes in terme van vertragingstyd wat met verkeersbeheer by straatkruisings aangegaan word, en wat deur die algoritme geminimeer word. Die tweede verkeersbeheeralgoritme is deur die chemiese proses van osmose geinspireer, waar molekules van 'n oplossingsmiddel sonder eksterne hulp vanaf 'n gebied waar hul in hoe konsentrasie voorkom, deur 'n gedeeltelik-deurlaatbare membraan beweeg na 'n gebied waarin hul ook in hoe konsentrasie, maar in opgeloste vorm voorkom. Die algoritme modelleer voertuie wat 'n straatkruising nader as die molekules van die oplossingsmiddel en die fisiese ruimte wat aan die ander kant van die kruising beskikbaar is om deur voertuie beset te word, as molekules in opgeloste vorm. In hierdie analogie word die kruising self as die gedeeltelik-deurlaatbare membraan beskou. Die derde algoritme is 'n hibriede strategie waarin elemente van die eerste twee algoritmes in samewerking met 'n tegniek vir die maksimering van straatkruisingsbenutting gekombineer word, en wat wat ten doel het om onnodige of verlengte onderbenutting van die kruising te vermy. Hierdie drie nuwe verkeersbeheeralgoritmes word, tesame met die bestaande algoritmes van Lammer en Helbing, en van Gershenson en Rosenblueth, asook 'n vaste-tyd beheeralgoritme, in 'n mikroskopiese verkeersimulasiemodelleringsraamwerk wat spesifiek vir die doel ontwerp is, geimplementeer. Verskeie maatstawwe word ingespan om die relatiewe werkverrigting van die algoritmes te evalueer. Hierdie maatstawwe sluit in die gebruiklike gemiddelde en maksimum vertragingstye van voertuie en die versadigingsvlak van strate in die vervoernetwerk, sowel as drie nuwe maatstawwe, naamlik die gemiddelde aantal stoppe deur voertuie, hul genormaliseerde vertragingstye en die gemiddelde, genormaliseerde aantal stoppe. Die algoritmes word in die kontekste van 'n line^ere topologie van opeenvolgende straatkruisings en 'n netwerktopologie van reghoekige straatblokke onder verskeie verkeersdigthede met mekaar vergelyk. Daar word bevind dat die nuwe hibriede algoritme die beste vaar in die line^ere topologie, terwyl die osmose-ge inspireerde algoritme die ander algoritmes uitstof in die straatblok-netwerktopologie.