A novel intelligent traffic recovery model for emergency vehicles based on context-aware reinforcement learning

Abstract

Management of traffic emergencies has become very popular in recent years. However, timely response to emergencies and recovering from an emergency is an important problem in itself. The strategies in the current studies merely suggest that after an emergency vehicle passes, the state should iterate to the next phase. Therefore, this paper proposes a novel approach for recovering from an emergency situation at an intersection based on real scenarios. The proposed method is a combination of context-aware and Reinforcement Learning (RL) models that predicts better alternatives for different states rather than just iterating to the next phase. In this regard, a new algorithm, named Interrupt Algorithm, is proposed to predict proper actions for recovering the emergency situation. This algorithm uses a Q-learning-based model that learns from traffic context for an emergency situation and chooses viable action from an action set. The recovery actions are categorized as max, min, and avg, respectively. Test results show that our proposed model outperforms traffic flow over than standard single choice recovering action-based approach by approximately 80%. Based on this, it may be more beneficial to choose different actions and therefore, proposed algorithm with the help of RL presents a more dynamic emergency recovery model.