Frustrations, monetary losses, lost time, high fuel consumption and CO ${}_2$ emissions are some of the problems caused by traffic jams in urban centers. In an attempt to solve this problem, this article proposes a traffic service to control congestion, named FOXS–Fast Offset Xpath Service. FOXS aims to reduce the problems generated by a traffic jam in a distributed way through roads classification and the suggestion of new routes to vehicles. Unlike the related works, FOXS is modeled using the Fog computing paradigm. Therefore, it is possible to take advantage of the inherent aspects of this paradigm, such as low latency, processing load balancing, scalability, geographical correlation and the reduction of bandwidth usage. In order to validate FOXS, our performance evaluation considers two realistic urban scenarios with different characteristics. When compared with related works, FOXS shows a reduction in stop time by up to 70%, the CO ${}_2$ emissions by up to 29% and, the planning time index by up to 49%. When considering communication evaluation metrics, FOXS reaches a better result than other solutions on the packet collisions metric (up to 11.5%) and on the application delay metric (up to 30%).

Congestion in large cities is widely recognized as a problem that impacts various aspects of society, including the economy and public health. To support the urban traffic system and to mitigate traffic congestion and the damage it causes, in this article we propose an assistant Intelligent Transport Systems (ITS) service for traffic management in Vehicular Networks (VANET), which we name FOXS-GSC, for Fast Offset Xpath Service with hexaGonS Communication. FOXS-GSC uses a VANET communication and fog computing paradigm to detect and recommend an alternative vehicle route to avoid traffic jams. Unlike the previous solutions in the literature, the proposed service offers a versatile approach in which traffic road classification and route suggestions can be made by infrastructure or by the vehicle itself without compromising the quality of the route service. To achieve this, the service operates in a decentralized way, and the components of the service (vehicles/infrastructure) exchange messages containing vehicle information and regional traffic information. For communication, the proposed approach uses a new dedicated multi-hop protocol that has been specifically designed based on the characteristics and requirements of a vehicle routing service. Therefore, by adapting to the inherent characteristics of a vehicle routing service, such as the density of regions, the proposed communication protocol both enhances reliability and improves the overall efficiency of the vehicle routing service. Simulation results comparing FOXS-GSC with baseline solutions and other proposals from the literature demonstrate its significant impact, reducing network congestion by up to 95% while maintaining a coverage of 97% across various scenery characteristics. Concerning road traffic efficiency, the traffic quality is increasing by 29%, for a reduction in carbon emissions of 10%.

Frustrações, perdas monetárias, perda de tempo, alto consumo de combustível e emissões de CO2 são um dos principais problemas causados por engarrafamento nos centros urbanos. Com o intuito de sanar tal problema, este artigo propõe o uRbAN - Rotemento veiculAr na Névoa, um serviço de tráfego para controlar os congestionamentos nos centros urbanos. O uRbAN visa reduzir os problemas gerados por um engarrafamento de forma distribuída para sugestão de novas rotas para os veículos. Ao contrário dos trabalhos da literatura, o uRbAN é modelado usando o paradigma da computação em névoa. Com isso, é possível aproveitar os aspectos inerentes de tal paradigma, tais como baixa latência, balanceamento de carga, escalabilidade, a importância da localização geográfica dos dados e a redução do uso da largura de banda. Para validar o uRbAN, nossa avaliação de desempenho foi realizada em um cenário urbano realístico. Quando comparado com outros trabalhos relacionados, o uRbAN apresenta uma redução no tempo de parada em até 70%, na emissão de CO2 em até 29%, no planning time index em até 49% com um desempenho satisfatório no tempo de resposta.