Inclusive and Adaptive Traffic Management for Smart Cities: A Framework Combining Emergency Response and Machine Learning Optimization

Smart control technologies that can manage the complexity of urban traffic while also reducing response times for emergency vehicles are necessary. This article proposes AETM (Adaptive and Equitable Traffic Management), an adaptive and equitable traffic management system that integrates contextual methods for handling emergencies with traffic light control based on reinforcement learning. The system uses Q-learning to optimize traffic light phases under normal traffic conditions and integrates a dedicated emergency vehicle module, which includes detection, dynamic rerouting and contextual preemption functions. The system adaptively optimizes traffic light phases under normal traffic conditions and integrates a specialized module for emergency vehicles, which ensures their detection, dynamic rerouting and contextual preemption. The priority level is evaluated through an auxiliary fuzzy mechanism, based on interpretable rules, which takes into account local conditions without influencing the learning process. The performance of the framework is evaluated in a microscopic simulation environment by comparing classical control, adaptive control, and the full AETM configuration. The results highlight significant reductions in travel times and stops for emergency vehicles while maintaining overall traffic stability.