A hybridization of deep learning techniques to predict and control traffic disturbances

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.