One of the main challenges in urban development faced by large cities is related to traffic jam. Despite increasing efforts to maximize the vehicle flow in large cities, to provide greater accuracy to estimate the traffic jam and to maximize the flow of vehicles in the transport infrastructure, without increasing the overhead of information on the control-related network, still consist in issues to be investigated. Therefore, using artificial intelligence method, we propose a solution of inter-vehicle communication for estimating the congestion level to maximize the vehicle traffic flow in the transport system, called TRAFFIC. For this, we modeled an ensemble of classifiers to estimate the congestion level using TRAFFIC. Hence, the ensemble classification is used as an input to the proposed dissemination mechanism, through which information is propagated between the vehicles. By comparing TRAFFIC with other studies in the literature, our solution has advanced the state of the art with new contributions as follows: (i) increase in the success rate for estimating the traffic congestion level; (ii) reduction in travel time, fuel consumption and CO₂ emission of the vehicle; and (iii) high coverage rate with higher propagation of the message, maintaining a low packet transmission rate.

大城市面临的城市发展的主要挑战之一是交通拥堵。尽管加大了在大城市中使车辆流量最大化的努力，但在不增加控制相关网络上信息开销的情况下，提供了更高的准确性来估计交通拥堵并最大化交通基础设施中的车辆流量，仍然是一个问题。有待调查。因此，使用人工智能方法，我们提出了一种车辆间通信解决方案，用于估计拥塞程度，以最大化运输系统中的车辆交通流量，称为TRAFFIC。为此，我们对分类器进行了建模，以使用TRAFFIC估计拥塞程度。因此，将集成分类用作建议的传播机制的输入，通过它们在车辆之间传播信息。通过将TRAFFIC与文献中的其他研究进行比较，我们的解决方案通过以下新贡献推动了最新技术的发展：（i）提高估计交通拥堵水平的成功率；（ii）减少旅行时间，燃料消耗和车辆的CO ₂排放量；（iii）高覆盖率和更高的消息传播，保持较低的分组传输速率。