Predicting traffic disturbances is a challenging problem in urban cities. Emergency vehicles (EV) is one of the biggest disturbances that affect traffic fluidity. The goal of this paper is to provide a machine learning application to deal with emergency cases in traffic networks. Particularly, we investigate the use of deep learning techniques coupled with Artificial Immune System to tackle the issue of EV guidance at signalized intersections. To accomplish this goal, we develop a traffic signal control system capable to estimate traffic status, guide EV to reach their destinations while assuming better traffic condition, control traffic signals, and adapt to new disturbances. For traffic forecasting, the suggested system inherits the advantages of convolutional neural networks, classification, and long short term memory. To control traffic signals, the suggested system uses the immune memory algorithm. To enhance and adapt control decisions to traffic disturbances, the suggested system uses a continuous learning approach assumed by an adapted reinforcement learning algorithm. Assessments using well-known algorithms from the literature are detailed in this work. The benchmarking algorithms are the preemptive longest queue first matching weight matrix system, the pre-emptive immune memory algorithm inspired case-based reasoning, and the preemptive optimized stage based fixed time algorithm. Experiments show a competitive performance of the suggested system compared to benchmarking algorithms.

中文翻译：

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深度学习技术的混合预测和控制交通干扰

预测交通干扰是城市中的一个具有挑战性的问题。紧急车辆 (EV) 是影响交通流动性的最大干扰之一。本文的目标是提供一种机器学习应用程序来处理交通网络中的紧急情况。特别是，我们研究了深度学习技术与人工免疫系统相结合的使用，以解决信号交叉口处的 EV 引导问题。为了实现这一目标，我们开发了一种交通信号控制系统，能够估计交通状况，引导电动汽车到达目的地，同时假设更好的交通状况，控制交通信号并适应新的干扰。对于交通预测，建议的系统继承了卷积神经网络、分类和长短期记忆的优点。为了控制交通信号，建议的系统使用免疫记忆算法。为了增强和适应交通干扰的控制决策，建议的系统使用由适应的强化学习算法假设的连续学习方法。这项工作详细介绍了使用文献中众所周知的算法进行的评估。基准算法有抢占式最长队列优先匹配权重矩阵系统、基于案例推理的抢占式免疫记忆算法和基于抢占式优化阶段的固定时间算法。实验表明，与基准算法相比，所建议的系统具有竞争力的性能。建议的系统使用一种由适应的强化学习算法假设的持续学习方法。这项工作详细介绍了使用文献中众所周知的算法进行的评估。基准算法有抢占式最长队列优先匹配权重矩阵系统、基于案例推理的抢占式免疫记忆算法和基于抢占式优化阶段的固定时间算法。实验表明，与基准算法相比，所建议的系统具有竞争力的性能。建议的系统使用一种由适应的强化学习算法假设的持续学习方法。这项工作详细介绍了使用文献中众所周知的算法进行的评估。基准算法有抢占式最长队列优先匹配权重矩阵系统、基于案例推理的抢占式免疫记忆算法和基于抢占式优化阶段的固定时间算法。实验表明，与基准算法相比，所建议的系统具有竞争力的性能。以及基于抢占式优化阶段的固定时间算法。实验表明，与基准算法相比，所建议的系统具有竞争力的性能。以及基于抢占式优化阶段的固定时间算法。实验表明，与基准算法相比，所建议的系统具有竞争力的性能。