Authorities in modern cities are facing daily challenges related to traffic control. Due to the problem complexity caused by the urbanization growth, investing in developing traffic signal control systems (TSCS) to guarantee better mobility has taken more attention by these authorities. In the existing literature, the majority of TSCS offers only a real-time control for a detected traffic problem without considering early prediction and estimation of its occurrence. Furthermore, traffic problems related to the arrival and guidance of emergency vehicles are rarely considered. Based on these gaps, we rely on concepts and mechanisms from both, the Artificial and the convolution neural networks (ANN and CNN), coupled with the longest queue first maximal weight matching algorithm (LQF-MWM), to develop PANNAL, a predictive and reactive TSCS. PANNAL is a Multi-Agent based System, where each individual agent has ANN, CNN, and LQF-MWM to adapt signal sequences and durations and favor the crossing of emergency vehicles. Agents have a heterarchical architecture considered for coordination. We leant on VISSIM, a state-of-the-art traffic simulation software for simulation and evaluation. We adopted algorithms, scenarios, key performance indicators, and evaluation results from the recent literature for benchmarking. These algorithms are pre-emptive and have a high performance and competitive results in traffic control of disturbed traffic condition.

现代城市的当局每天都面临与交通管制有关的挑战。由于城市化发展引起的问题复杂性，这些当局更加重视开发交通信号控制系统（TSCS）以确保更好的机动性。在现有文献中，大多数TSCS仅对检测到的交通问题提供实时控制，而没有考虑对其发生的早期预测和估计。此外，很少考虑与应急车辆的到达和引导有关的交通问题。基于这些差距，我们依靠人工和卷积神经网络（ANN和CNN）的概念和机制，结合最长的队列优先最大权重匹配算法（LQF-MWM），来开发PANNAL，一种可预测的反应式TSCS。PANNAL是一个基于多代理的系统，其中每个单独的代理都有ANN，CNN和LQF-MWM，以适应信号序列和持续时间，并有助于穿越应急车辆。代理具有考虑进行协调的分层体系结构。我们依靠VISSIM，这是一种用于模拟和评估的最新交通模拟软件。我们采用了最新文献中的算法，方案，关键性能指标和评估结果作为基准。这些算法是先发制人的，在干扰交通状况的交通控制中具有高性能和竞争性结果。我们依靠VISSIM，这是一种用于模拟和评估的最新交通模拟软件。我们采用了最新文献中的算法，方案，关键性能指标和评估结果作为基准。这些算法是先发制人的，在干扰交通状况的交通控制中具有高性能和竞争性结果。我们依靠VISSIM，这是一种用于模拟和评估的最新交通模拟软件。我们采用了最新文献中的算法，方案，关键性能指标和评估结果作为基准。这些算法是先发制人的，在干扰交通状况的交通控制中具有高性能和竞争性结果。