Vehicular Ad hoc NETworks (VANETs) are a subclass of Mobile Ad hoc NETworks and represent a relatively new and very active field of research. VANETs will enable in the near future applications that will dramatically improve roadway safety and traffic efficiency. There is a need to increase traffic efficiency as the gap between the traveled and the physical lane miles keeps increasing. The Dynamic Traffic Assignment problem tries to dynamically distribute vehicles efficiently on the road network and in accordance with their origins and destinations. We present a novel dynamic decentralized and infrastructure-less algorithm to alleviate traffic congestions on road networks and to fill the void left by current algorithms which are either static, centralized, or require infrastructure. The algorithm follows an online approach that seeks stochastic user equilibrium and assigns traffic as it evolves in real time, without prior knowledge of the traffic demand or the schedule of the cars that will enter the road network in the future. The Reverse Online Algorithm for the Dynamic Traffic Assignment inspired by Ant Colony Optimization for VANETs follows a metaheuristic approach that uses reports from other vehicles to update the vehicle’s perceived view of the road network and change route if necessary. To alleviate the broadcast storm spontaneous clusters are created around traffic incidents and a threshold system based on the level of congestion is used to limit the number of incidents to be reported. Simulation results for the algorithm show a great improvement on travel time over routing based on shortest distance. As the VANET transceivers have a limited range, that would limit messages to reach at most 1,000 meters, we present a modified version of this algorithm that uses a rebroadcasting scheme. This rebroadcasting scheme has been successfully tested on roadways with segments of up to 4,000 meters. This is accomplished for the case of traffic flowing in a single direction on the roads. It is anticipated that future simulations will show further improvement when traffic in the other direction is introduced and vehicles travelling in that direction are allowed to use a store carry and forward mechanism. / Includes bibliography. / Dissertation (Ph.D.)--Florida Atlantic University, 2016. / FAU Electronic Theses and Dissertations Collection

Vehicular Ad Hoc Network (VANET) supports wireless communication among vehicles using vehicle-to-vehicle (V2V) communication and between vehicles and infrastructure using vehicle-to-infrastructure (V2I) communication. This communication can be utilized to allow the distribution of safety and non-safety messages in the network. VANET supports a wide range of applications which rely on the messages exchanged within the network. Such applications will enhance the drivers' consciousness and improve their driving experience. However, the efficiency of these applications depends on the availability of vehicles real-time location information. A number of methods have been proposed to fulfill this requirement. However, designing a V2V-based localization method is challenged by the high mobility and dynamic topology of VANET and the interference noise due to objects and buildings. Currently, vehicle localization is based on GPS technology, which is not always reliable. Therefore, utilizing V2V communication in VANET can enhance the GPS positioning. With V2V-based localization, vehicles can determine their locations by exchanging mobility data among neighboring vehicles. In this research work, we address the above challenges and design a realistic V2V-based localization method that extends the centroid localization (CL) by assigning a weight value to each neighboring vehicle. This weight value is obtained using a weighting function that utilizes the following factors: 1) link quality distance between the neighboring vehicles 2) heading information and 3) map information. We also use fuzzy logic to model neighboring vehicles' weight values. Due to the sensitivity and importance of the exchanged information, it is very critical to ensure its integrity and reliability. Therefore, in this work, we present the design and the integration of a mobility data verification component into the proposed localization method, so that only verified data from trusted neighboring vehicles are considered. We also use subjective logic to design a trust management system to evaluate the trustworthiness of neighboring vehicles based on the formulated subjective opinions. Extensive experimental work is conducted using simulation programs to evaluate the performance of the proposed methods. The results show improvement on the location accuracy for varying vehicle densities and transmission ranges as well as in the presence of malicious/untrusted neighboring vehicles. / Includes bibliography. / Dissertation (Ph.D.)--Florida Atlantic University, 2016. / FAU Electronic Theses and Dissertations Collection

This work presents the development of the Context-Aware Hybrid Data Dissemination protocol for vehicular networks. The importance of developing vehicular networking data dissemination protocols is exemplified by the recent announcement by the U.S. Department of Transportation (DOT) National Highway Traffic Safety Administration (NHTSA) to enable vehicle-to-vehicle (V2V) communication technology. With emphasis on safety, other useful applications of V2V communication include but are not limited to traffic and routing, weather, construction and road hazard alerts, as well as advertisement and entertainment. The core of V2V communication relies on the efficient dispersion of relevant data through wireless broadcast protocols for these varied applications. The challenges of vehicular networks demand an adaptive broadcast protocol capable of handling diverse applications. This research work illustrates the design of a wireless broadcast protocol that is context-aware and adaptive to vehicular environments taking into consideration vehicle density, road topology, and type of data to be disseminated. The context-aware hybrid data dissemination scheme combines store-and-forward and multi-hop broadcasts, capitalizing on the strengths of both these categories and mitigates the weaknesses to deliver data with maximum efficiency to a widest possible reach. This protocol is designed to work in both urban and highway mobility models. The behavior and performance of the hybrid data dissemination scheme is studied by varying the broadcast zone radius, aggregation ratio, data message size and frequency of the broadcast messages. Optimal parameters are determined and the protocol is then formulated to become adaptive to node density by keeping the field size constant and increasing the number of nodes. Adding message priority levels to propagate safety messages faster and farther than non-safety related messages is the next context we add to our adaptive protocol. We dynamically set the broadcast region to use multi-hop which has lower latency to propagate safety-related messages. Extensive simulation results have been obtained using realistic vehicular network scenarios. Results show that Context-Aware Hybrid Data Dissemination Protocol benefits from the low latency characteristics of multi-hop broadcast and low bandwidth consumption of store-and-forward. The protocol is adaptive to both urban and highway mobility models. / Includes bibliography. / Dissertation (Ph.D.)--Florida Atlantic University, 2014. / FAU Electronic Theses and Dissertations Collection

Assunto bastante abordado quando se trata de Sistemas Inteligentes de Transportes (ITS), a identificação veicular - utilizada em grande parte das aplicações de ITS deve ser entendida como um conjunto de recursos de hardware, software e telecomunicações, que interagem para atingir, do ponto de vista funcional, o objetivo de, conseguir extrair e transmitir, digitalmente, a identidade de um veículo. É feita tanto por sistemas que transmitem e recebem uma identidade digital quanto por sistemas que, instalados na infraestrutura da via, são capazes de reconhecer a placa dos veículos circulantes. Quando se trata da identificação automática por meio do reconhecimento da placa veicular, os estudos têm se concentrado sobremaneira nas tecnologias de processamento de imagens, não abordando - em sua maioria - uma visão sistêmica, necessária para compreender de maneira mais abrangente todas as variáveis que podem interferir na eficácia da identificação. Com o objetivo de contribuir para melhor entender e utilizar os sistemas de reconhecimento automático de placas veiculares, este trabalho propõe um modelo sistêmico, em camadas, para representar seus componentes. Associada a esse modelo, propõe uma classificação para os diversos tipos de falhas que podem prejudicar seu desempenho. Uma análise desenvolvida com resultados obtidos em testes realizados em campo com sistemas de identificação de placas voltados à fiscalização de veículos aponta resultados relevantes e limitações para obter correlações entre variáveis, em função dos diversos fatores que podem influenciar os resultados. Algumas entrevistas realizadas apontam os tipos de falhas que ocorrem com mais frequência durante a operação desses sistemas. Finalmente, este trabalho propõe futuros estudos e apresenta um glossário de termos, que poderá ser útil a novos pesquisadores. / The automatic vehicle identification is an important feature of Intelligent Transportation Systems (ITS) and is used in most ITS applications. The identification process is comprised of a group of interacting resources that involves hardware, software and telecommunication to, digitally, extract and transmit the identity of vehicles. At least two technologies may be used in the vehicle identification process: on-board devices transmitting a digital identity or systems installed on the road infrastructure, which identify and read the vehicle license plate. As far as vehicle license plate recognition is concerned, studies have been greatly focused on image processing technologies and have not addressed the problem in a systemic approach, which is very important for understanding all variables that can interfere with the effectiveness of identification. Having this approach in mind and intending to contribute for a better performance, this paper proposes a layer model representation of those systems as well as a failure type classification associated with it. An analysis, based on a significant set of results obtained from field tests of systems with plate recognition capabilities for law enforcement, shows important results as well as limitations to obtain mathematical correlation of variables. Interviews conducted with supply actors of such systems in Brazil point out the most significant sources of failures that occur during operation. Finally, the text presents potential topics for research and organizes a glossary of terms that may be useful to future researchers.

Dans un proche futur, les véhicules connectés et autonomes remplaceront nos véhicules actuels, et il sera nécessaire de repenser intégralement la mobilité. Le conducteur, avec ses lacunes, sera de plus en plus assisté, et un jour détrôné par un système embarqué, capable d'agir plus rapidement, tout en ayant une représentation plus précise et fiable de son environnement de navigation. Pourtant, il reste encore du chemin à parcourir avant d'arriver à un tel stade de maturité : l'environnement du véhicule est complexe, imprévisible et conflictuel, car partagé avec d'autres acteurs de la mobilité. Ce travail de thèse vise à anticiper l'arrivée de ces nouveaux véhicules afin de proposer des comportements coopératifs au niveau des véhicules et de l'infrastructure tout en permettant un contrôle décentralisé de ce type de système complexe. Dans le cadre d'une approche multi-agents et d'une architecture distribuée, nous proposons d'abord une modélisation par couplage des dynamiques physique et communicationnelle, auxquelles s'ajoute une intégration de la fiabilité de l'information (confiance). L'étape suivante a été de développer un cadre de simulation propice à l'implémentation de nos modèles dans le cas des véhicules connectés. Nous introduisons un nouveau simulateur de trafic, construit comme une extension d'une plateforme existante, au sein duquel les flux d'informations entre les véhicules et avec l'infrastructure prennent la forme d'échanges de messages. Toutes les informations du système proviennent de capteurs, et toutes les entités, modélisées comme des agents, sont autonomes dans leur prise de décision. De nouvelles formes de contrôle sont désormais envisageables en utilisant des consignes transmises par l'infrastructure communicante. Le couplage des dynamiques assure la cohérence et l'inter-dépendance des différents modèles dans le simulateur. Nous montrons en simulation que, grâce à l'intégration d'informations supplémentaires via la communication, les véhicules, modélisés par un modèle microscopique multi-anticipatif bilatéral, sont capables de réduire l'effet de perturbations propagées au sein d'un flux. En termes de stratégies de contrôle, une des problématiques principales est de garantir une forme de contrôle qui s'adaptera aux différentes phases de déploiement des systèmes coopératifs. L'analogie avec des problèmes de l'IA (problème de cognition) nous a mené à traiter le problème de manière plus abstraite : comment permettre à un système autonome de contrôler son environnement. Les approches constructivistes, que nous avons retenues, modélisent le processus de cognition comme un phénomène de construction itératif. Pour le trafic coopératif, l'avantage est de disposer d'un système capable de générer ses propres stratégies, en utilisant ou non des connaissances expertes, et de les faire évoluer au cours du temps pour s'adapter aux véhicules composant le flux. Les résultats de notre approche sont présentés dans deux cadres de simulation. Le premier est un prototype visant à illustrer les comportements de bas niveau dans un environnement simplifié. Nous montrons que le modèle est capable dans ce cadre de combiner différentes représentations individuelles pour construire une représentation et de s'adapter à différents contextes en les recombinant dynamiquement. Puis, dans le cadre de simulation du trafic coopératif, les résultats laissent entrevoir le potentiel de notre approche dans des applications réelles / In a near future, connected and automated vehicles will progressively replace current vehicles, leading to deep changes in transportation. The driver will be soon assisted and then replaced by an embedded system, able to act quicker, relying on a more robust and precise representation of its surrounding environment. However, some steps are still needed before coming up with such a level of automation since the vehicle environment is complex and unpredictable. This work intends to anticipate the introduction of these new kinds of vehicles by providing cooperative behaviors at both infrastructure and vehicle levels, at the same time allowing a decentralized control of these systems. We propose a distributed modeling framework, using multi-agent systems, relying on the coupling of the system dynamics: information, communication and reliability (modeled through the concept of trust). The next step was to develop a simulation framework enabling the implementation of our models for connected vehicles applications. We present a new microscopic traffic simulator, built as an extension of an existing platform, and able to model information exchanges using messages between vehicles and with the infrastructure. All data are provided by sensors and all entities, modeled as agents, are autonomous regarding their decision process. Thanks to the simulator, it is possible to imagine new control strategies relying on recommendations disseminated by the connected infrastructure. Consistency and interdependence of the simulator components are ensured by the dynamic coupling. As for the vehicles’ dynamics, we propose a bilateral multi-anticipative model that integrates additional information from communications in the vehicle decision process. Results in simulation confirm that the model is able to reduce the propagation of perturbation through the flow, leading to a more homogeneous and stable traffic. One of the major issues regarding traffic control strategies will be to dynamically adapt the action policy to the several deployment stages of cooperative transportation systems. The similarities with Artificial Intelligence problems like cognition motivate a more abstract study: how to model an autonomous system able to control its environment. We choose the constructivist approaches, that propose to model the cognition process as an iterative building process. For cooperative traffic, the benefits lie in the ability of the system to generate its own strategies, relying or not on domain specific knowledge, and then make them evolve to be adapted to vehicles in the flow. The results from our approach are presented in two distinct simulation frameworks. The first one is an experimentation prototype aiming at highlighting the low-level behaviors in a simplified environment. In this context, we show that the model is able to combine efficiently several individual concurrent representations in order to build a high-level representation that can be adapted to several contexts. The second framework is the traffic simulator where the results lead to some insights about the potential of our approach for such realistic applications

Submitted by Daniella Santos (daniella.santos@ufma.br) on 2017-11-22T16:02:27Z No. of bitstreams: 1 HeygonAraújo.pdf: 4678551 bytes, checksum: 923410aa48c69f8cf3b40b930eaa71fb (MD5) / Made available in DSpace on 2017-11-22T16:02:27Z (GMT). No. of bitstreams: 1 HeygonAraújo.pdf: 4678551 bytes, checksum: 923410aa48c69f8cf3b40b930eaa71fb (MD5) Previous issue date: 2017-07-13 / The definition of the best sequence on route duplication of freight systems consists of a complex NP-hard problem. There exists a huge variety of meta-heuristics (MH) capable of generating satisfactory solutions. However, it is fastidious to know which MH will produce the best solution for a Duplication Sequence Problem (DSP). This paper proposes a process development methodology which guides to evaluate the best duplication sequence comparing the MH’s performance with existing approaches such as linear analytical method (LAM), and thus to ensure that the system has its capacity maximized in the possible shortest time interval. If this sequence is prioritized incorrectly, it tends to generate wastes such as time and currency in new routes which will not add system capacity in short term. The potential of this methodology is demonstrated by a case study in railways. / A definição da sequência ótima de duplicação em vias de sistemas de transportes de cargas consiste de um problema de complexidade intratável. Existem uma grande variedade de Meta-heurísticas (MHs) capazes de gerar soluções satisfatórias. Entretanto, é fastidioso conhecer a MH que produzirá a melhor solução para um dado Problema de Sequenciamento de Duplicação (PSD) . Não há na literatura uma metodologia para estruturar, planejar e controlar algoritmos e processos na modelagem das variedade de MHs aplicadas para encontrar a solução ótima neste tipo de problema. Este trabalho apresenta uma metodologia de desenvolvimento de processo o qual busca pela sequência ótima de duplicação em um dado sistema de transporte comparando a performance de MH com abordagens existentes, tais como método analítico lineares (MALs), e desta forma garantir que o sistema tenha sua capacidade maximizada no menor intervalo de tempo possível. Caso esta sequência seja priorizada de forma incorreta, a tendência será o desperdício de tempo e dinheiro em novas vias as quais não agrerarão capacidade ao sistema no curto prazo. O potencial desta metodologia é demonstrado através de um estudo de caso em ferrovias.

Ramp metering is an effective management strategy, which helps to keep traffic density below the critical value, preventing breakdowns and thus maintaining the full capacity of the freeway. Warrants for ramp metering installation have been developed by a number of states around the nation. These warrants are generally simple and are based on the traffic, geometry, and safety conditions in the immediate vicinity of each ramp (local conditions). However, advanced applications of ramp metering utilize system-based metering algorithms that involve metering a number of on-ramps to address system bottleneck locations. These algorithms have been proven to perform better compared to local ramp metering algorithms. This has created a disconnection between existing agency metering warrants to install the meters and the subsequent management and operations of the ramp metering. Moreover, the existing local warrants only consider recurrent conditions to justify ramp metering installation with no consideration of the benefits of metering during non-recurrent events such as incidents and adverse weather. This dissertation proposed a methodology to identify the ramps to meter based on system-wide recurrent and non-recurrent traffic conditions. The methodology incorporates the stochastic nature of the demand and capacity and the impacts of incidents and weather using Monte Carlo simulation and a ramp selection procedure based on a linear programming formulation. The results of the Monte Carlo simulation are demand and capacity values that are used as inputs to the linear programming formulation to identify the ramps to be metered for each of the Monte Carlo experiments. This method allows the identification of the minimum number of ramps that need to be metered to keep the flows below capacities on the freeway mainline segment, while keeping the on-ramp queues from spilling back to the upstream arterial street segments. The methodology can be used in conjunction with the existing local warrants to identify the ramps that need to be metered. In addition, it can be used in benefit-cost analyses of ramp metering deployments and associated decisions, such as which ramps to meter and when to activate in real-time. The methodology is extended to address incidents and rainfall events, which result in non-recurrent congestion. For this purpose, the impacts of non-recurrent events on capacity and demand distributions are incorporated in the methodology.

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.

Changeable Message Signs (CMS) are commonly utilized by transportation agencies to inform motorists of traffic, roadway, and environmental conditions. They may be used to provide information, such as delay and alternate route guidance, in the event of an incident, construction or a roadway closure. The effectiveness of CMS in managing freeway traffic, however, is a function of many factors including the number of CMS installations, the location of CMS, the messages displayed, varied traffic network characteristics, and drivers' response to incident conditions and CMS information. The objective of this thesis is to develop a CMS location planning model that can be used by transportation agencies to develop a CMS location plan that could achieve the largest long-term benefit to the system. This research is mainly motivated by the lack of systematic, robust and practical methods for locating CMS. State-of-practice methods rely mostly on the practitioner's experience and judgement. Other methods fail to incorporate reasonable driver behaviour models, consider time-varying demand, allow for computational efficiency on large networks, or consider the spatial variation of incidents on a traffic network. A new CMS location optimization model has been developed that is unique in both model realism and computational efficiency. The model incorporates several components to estimate incident delay, predict driver response, estimate network-wide benefit, and choose those CMS locations that would provide the most benefit. Deterministic queuing methods are used in conjunction with historic incident characteristics to approximate the delay impact of an incident with and without CMS. A discrete choice model is used to predict the rate at which drivers would switch from the incident route to a less congested alternative under CMS information. A network traffic assignment model is then incorporated in an attempt to estimate the resulting traffic induced by incidents. Genetic algorithms are utilized as an optimization technique to choose a set of CMS that would provide the most benefit. An extensive computational analysis was performed on both a hypothetical network and a segment of Highway 401 through Toronto. A sensitivity analysis was performed to test the model's response to parameter and data estimation errors. The model was found to be most sensitive to the diversion model parameters. The model produced reasonable results with locations selected upstream of major freeway interchange diversion points. Considering the additional components included in the proposed model, and its ability to consider more location schemes, the proposed model may be considered superior to previous CMS location models.

Changeable Message Signs (CMS) are commonly utilized by transportation agencies to inform motorists of traffic, roadway, and environmental conditions. They may be used to provide information, such as delay and alternate route guidance, in the event of an incident, construction or a roadway closure. The effectiveness of CMS in managing freeway traffic, however, is a function of many factors including the number of CMS installations, the location of CMS, the messages displayed, varied traffic network characteristics, and drivers' response to incident conditions and CMS information. The objective of this thesis is to develop a CMS location planning model that can be used by transportation agencies to develop a CMS location plan that could achieve the largest long-term benefit to the system. This research is mainly motivated by the lack of systematic, robust and practical methods for locating CMS. State-of-practice methods rely mostly on the practitioner's experience and judgement. Other methods fail to incorporate reasonable driver behaviour models, consider time-varying demand, allow for computational efficiency on large networks, or consider the spatial variation of incidents on a traffic network. A new CMS location optimization model has been developed that is unique in both model realism and computational efficiency. The model incorporates several components to estimate incident delay, predict driver response, estimate network-wide benefit, and choose those CMS locations that would provide the most benefit. Deterministic queuing methods are used in conjunction with historic incident characteristics to approximate the delay impact of an incident with and without CMS. A discrete choice model is used to predict the rate at which drivers would switch from the incident route to a less congested alternative under CMS information. A network traffic assignment model is then incorporated in an attempt to estimate the resulting traffic induced by incidents. Genetic algorithms are utilized as an optimization technique to choose a set of CMS that would provide the most benefit. An extensive computational analysis was performed on both a hypothetical network and a segment of Highway 401 through Toronto. A sensitivity analysis was performed to test the model's response to parameter and data estimation errors. The model was found to be most sensitive to the diversion model parameters. The model produced reasonable results with locations selected upstream of major freeway interchange diversion points. Considering the additional components included in the proposed model, and its ability to consider more location schemes, the proposed model may be considered superior to previous CMS location models.