Contemporary studies of Intelligent Transportation Systems (ITS) use simulations of vehicular and communications traffic, due to the ethical and practical infeasibility of conducting experiments on real transportation networks. Different simulators have been developed for modeling real-time vehicular mobility and inter-vehicular communication under varying traffic and roadway conditions. While most model the effect of mobility on communications, only a few simulate the impact of inter-vehicular communication on vehicular mobility. None, moreover, are implemented as parallel or distributed frameworks: an essential requirement for the study of ITS applications in large-scale urban environments. As a starting point for developing such a framework, one contemporary simulator, VNetInetSim, was tested to determine its behavior under large loads. Testing determined that VNetInetSim's memory usage and execution time increase exponentially in the number of simulated vehicles while remaining relatively constant under increased communication traffic.

A transformative revolution in transportation is coming with the advent of Au-tonomous Vehicles (AVs), which are expected to increase mobility, reduce trafficcongestion, and save fuel. Although AVs present significant advantages, they alsopose substantial challenges, particularly when it comes to security and safety. Theaim of this study is to map out the existing knowledge in order to facilitate furtherresearch and development, which will hasten the rollout of secure and reliable au-tonomous vehicles. This, in turn, will enable a sustainable and efficient future fortransportation. Research on AV safety and security is reviewed in this thesis in acomprehensive systematic literature review. The search process identified a total of283 studies published between 2019 and 2022, out of which 24 studies were selectedthrough a multi-stage process according to our predefined protocol. Based on re-search topics in selected studies, our findings have a significant impact on the fieldof Artificial Intelligence and automated vehicles. Based on our findings, we canprovide a summary of current knowledge regarding the safety, security, and stabilityimplications of autonomous vehicles. Simulations, real-life experiments, and physi-cal tests were all used in the selected articles for evaluation. Aside from the excellentresults, we identified many limitations of the articles, including the limitations of thedata sets, the analysis of unusual events, and the verification practices.

Adaptive ramp metering (ARM) is a widely popular intelligent transportation system (ITS) tool that boasts the ability to reduce congestion and streamline traffic flow during peak hour periods while maintaining a lower implementation cost than traditional methods such as freeway widening. This thesis explores the effectiveness of ARM implementation on an 18 mile segment of the Interstate 80 (I-80) corridor in the Bay Area residing in northern California. Smaller segments of this particular segment were analyzed to determine the effective length of ARM on efficiency at various lengths originating from a known bottleneck location. Efficiency values were also compared against a control segment of the Interstate 280 (I-280) in San Jose to provide a test site experiencing similar traffic congestion but without any ARM implementation. An Empirical Bayes analysis was conducted to provide the foundation of a safety evaluation of the ramp metering implementation and determine a counterfactual estimate of expected collisions had ARM implementation not occurred. It was found that the installation of the ramp meters did allow for some marginal increases in efficiency but may not be entirely associated with ARM implementation due to a variety of external factors as well as showing inconsistent behavior between analyzed segments. Regarding safety, the predictive model estimates 32.8 collisions to occur along a 0.5 mile segment within a three-year timeframe if ARM were not installed, which implies substantial improvements in safety conditions. However additional efficiency and safety data within the “after” period may be necessary to provide a more robust and conclusive evaluation as the ARM system is still relatively new.

No Brasil existe a perspectiva de crescimento expressivo do volume de dados a ser processado pelas prestadoras de serviços de rastreamento em decorrência do aumento natural do uso de sistemas de rastreamento e também para atender a Resolução 330 de 2009 e Deliberação 135 de 30/01/2013 do Conselho Nacional de Trânsito (CONTRAN). Este crescimento gera a necessidade da incorporação de ferramentas analíticas nos sistemas de gerenciamento do rastreamento e monitoramento de veículos e na gestão de risco, para aumentar a sua eficiência e atender o crescimento do mercado. O objetivo desta dissertação é de propor uma metodologia que permita caracterizar o comportamento de movimentação de um veículo, com a finalidade de auxiliar o processo de tomada de decisão no gerenciamento e monitoramento de veículos. A caracterização do comportamento de movimentação do veículo foi feita pela geração de um modelo analítico do comportamento de movimentação, coletando os dados pretéritos da posição espacial e temporal. Este modelo baseia-se na movimentação e considera os aspectos comportamentais espaciais e temporais de forma independente. A caracterização do comportamento gera informações para identificar o comportamento espacial e temporal do veículo monitorado para um determinado nível de confiabilidade. / In Brazil there is the prospect of growth in the volume of data to be processed by the tracking service providers due to the natural increase of the use of tracking systems and also to meet the Resolution 330 of 2009 and Resolution 135 of 01.30.2013 of the National Traffic Council (CONTRAN), due to this growth the need of incorporation of analytical tools in systems management tracking and monitoring of vehicles and risk management are created, to increase their efficiency and meet market growth. This study objective is to propose a methodology to characterize the moving vehicle behavior, in order to assist the process of decision making in management and vehicle tagging. The vehicle handling behavior will be characterized by generating an analytical model of the vehicle movement, collecting bygone data of spatial position and time. This model will consist of a motion model taking into account that the spatial and temporal aspects of behavior are taken independently. The behavior characterization generates reports able to identify the spatial and temporal behavior of the monitored vehicle for a given level of reliability.

No Brasil existe a perspectiva de crescimento expressivo do volume de dados a ser processado pelas prestadoras de serviços de rastreamento em decorrência do aumento natural do uso de sistemas de rastreamento e também para atender a Resolução 330 de 2009 e Deliberação 135 de 30/01/2013 do Conselho Nacional de Trânsito (CONTRAN). Este crescimento gera a necessidade da incorporação de ferramentas analíticas nos sistemas de gerenciamento do rastreamento e monitoramento de veículos e na gestão de risco, para aumentar a sua eficiência e atender o crescimento do mercado. O objetivo desta dissertação é de propor uma metodologia que permita caracterizar o comportamento de movimentação de um veículo, com a finalidade de auxiliar o processo de tomada de decisão no gerenciamento e monitoramento de veículos. A caracterização do comportamento de movimentação do veículo foi feita pela geração de um modelo analítico do comportamento de movimentação, coletando os dados pretéritos da posição espacial e temporal. Este modelo baseia-se na movimentação e considera os aspectos comportamentais espaciais e temporais de forma independente. A caracterização do comportamento gera informações para identificar o comportamento espacial e temporal do veículo monitorado para um determinado nível de confiabilidade. / In Brazil there is the prospect of growth in the volume of data to be processed by the tracking service providers due to the natural increase of the use of tracking systems and also to meet the Resolution 330 of 2009 and Resolution 135 of 01.30.2013 of the National Traffic Council (CONTRAN), due to this growth the need of incorporation of analytical tools in systems management tracking and monitoring of vehicles and risk management are created, to increase their efficiency and meet market growth. This study objective is to propose a methodology to characterize the moving vehicle behavior, in order to assist the process of decision making in management and vehicle tagging. The vehicle handling behavior will be characterized by generating an analytical model of the vehicle movement, collecting bygone data of spatial position and time. This model will consist of a motion model taking into account that the spatial and temporal aspects of behavior are taken independently. The behavior characterization generates reports able to identify the spatial and temporal behavior of the monitored vehicle for a given level of reliability.

Advances in computing and wireless communication technologies have increased interest in smart vehicles, vehicles equipped with significant computing, communication and sensing capabilities to provide services to travelers. Smart vehicles can be exploited to improve driving safety and comfort as well as optimize surface transportation systems. Wireless communications among vehicles and between vehicles and roadside infrastructures represent an important class of vehicle communications. One can envision creating an integrated radio network leveraging various wireless technologies that work together in a seamless fashion. Based on cost-performance tradeoffs, different network configurations may be appropriate for different environments. An understanding of the properties of different vehicular network architectures is absolutely necessary before services can be successfully deployed. Based on this understanding, efficient data services (e.g., data dissemination services) can be designed to accommodate application requirements. This thesis examines several research topics concerning both the evaluation and design of vehicular networks. We explore the properties of vehicle-to-vehicle (v2v) communications. We study the spatial propagation of information along the road using v2v communications. Our analysis identifies the vehicle traffic characteristics that significantly affect information propagation. We also evaluate the feasibility of propagating information along a highway. Several design alternatives exist to build infrastructure-based vehicular networks. Their characteristics have been evaluated in a realistic vehicular environment. Based on these evaluations, we have developed some insights into the design of future broadband vehicular networks capable of adapting to varying vehicle traffic conditions. Based on the above analysis, opportunistic forwarding that exploit vehicle mobility to overcome vehicular network partitioning appears to be a viable approach for data dissemination using v2v communications for applications that can tolerate some data loss and delay. We introduce a methodology to design enhanced opportunistic forwarding algorithms. Practical algorithms derived from this methodology have exhibited different performance/overhead tradeoffs. An in-depth understanding of wireless communication performance in a vehicular environment is necessary to provide the groundwork for realizing reliable mobile communication services. We have conducted an extensive set of field experiments to uncover the performance of short-range communications between vehicles and between vehicles and roadside stations in a specific highway scenario.